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PHYSICAL AND CFD MODELS OF PM SEPARATION AND SCOUR IN
HYDRODYNAMIC UNIT OPERATIONS
By
HWAN CHUL CHO
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2012
2
© 2012 Hwan Chul Cho
3
To my parents who always stand behind me, supporting me,
and believing there is nothing that I cannot achieve,
and to everyone who has encouraged and
supported me to achieve a Ph.D.
4
ACKNOWLEDGMENTS
First and foremost, I express my deep appreciation to my advisor, Dr. John J. Sansalone,
who gave me the best chance to work in this great academic area. He has consistently guided,
encouraged and supported me throughout the journey to my Ph.D. program. His patient
elucidation, enlightening ideas and precious comments have contributed a lot to my
understanding of this research area and shaping my concept of scientist and engineer. It was and
will be great fortune and enormous inspiration for me in my life.
I also extend my sincere appreciation to the distinguish professors on my committee: Dr.
Ben Koopman, Dr. James Heaney, and Dr. Jennifer Curtis. I am also very grateful to my
committee members for their helpful advice on the dissertation work.
I express my thanks to my colleagues: Dr. Jong-Yeop Kim, Dr. Natalie Magill-Winberry,
Dr. Gaoxiang Ying, Dr. Christian Berretta, Dr. Joshua Dickenson, Dr. Ruben Kertesz and Dr.
Tingting Wu, who shared me with their knowledge and helpful discussion. My appreciation also
extends to my colleagues including, Mr. Karl Seltzer, Ms. Christina Herr-Joiner, Mr. Adam
Marquez, Ms. Valarie Thorsen, Ms. Aniela Burant, Mr. Gregory Brenner, Ms. Sowmya Sankaran,
Mr. Saurabh Raje, Ms. Giuseppina Garofalo, Dr. Young-min Cho, Dr. Se-jin Youn, Dr. Myung-
heui Woo and Mr. Rascal Cho for their valuable assistance and help. Their friendships have been
one of my important accomplishments in the past five years.
I would like to express my special and warmest thanks to my best friend, Dr. Subbu-
Srikanth Pathapati, for not only his enormous help, but also his sincere friendship.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ...............................................................................................................4
LIST OF TABLES ...........................................................................................................................8
LIST OF FIGURES .........................................................................................................................9
LIST OF ABBREVIATIONS ........................................................................................................11
ABSTRACT ...................................................................................................................................16
CHAPTER
1 INTRODUCTION ..................................................................................................................18
2 PHYSICAL MODELING OF PARTICULATE MATTER WASHOUT FROM A
HYDRODYNAMIC SEPARATOR .......................................................................................22
Overview .................................................................................................................................22
Methodology ...........................................................................................................................24
Physical Model Configuration .........................................................................................24
Flow Velocity Measurement ...........................................................................................25
Pre-deposited PM ............................................................................................................25
RTD Test ..................................................................................................................26
Scour Thresholds ......................................................................................................28
Results.....................................................................................................................................29
In-Situ Velocity Profiles ..................................................................................................29
Washout PM Granulometry .....................................................................................30
Residence Time Distributions (RTDs) .....................................................................31
Densimetric Froude Number ....................................................................................32
Time Rate of Washout ..............................................................................................33
Initiation of Scour ............................................................................................................34
Summary .................................................................................................................................38
3 PHYSICAL AND CFD MODELING OF PM SEPARATION AND SCOUR IN
HYDRODYNAMIC SEPARATORS ....................................................................................53
Overview .................................................................................................................................53
Objectives ...............................................................................................................................56
Methodology ...........................................................................................................................56
Physical Clarification and Re-suspension Function Modeling .......................................56
CFD Modeling .................................................................................................................59
CFD Governing Equations ..............................................................................................59
Particulate Phase Modeling .............................................................................................61
Re-suspension and Washout Modeling ...........................................................................63
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Numerical Procedure .......................................................................................................63
Volumetric Efficiency Calculation ..................................................................................63
Results.....................................................................................................................................64
Comparison Physical and CFD Modeling .......................................................................64
Physical Modeling of Separation and washout Function ................................................64
PSD Result .......................................................................................................................66
PM Dynamics ..................................................................................................................68
Fluid Velocity Magnitude ................................................................................................69
Probability of PM Separation and Washout ....................................................................70
Summary .................................................................................................................................71
4 STEPWISE STEADY CFD MODELING OF UNSTEADY FLOW AND PM
LOADING TO UNIT OPERATIONS ...................................................................................82
Overview .................................................................................................................................82
Objectives ...............................................................................................................................84
Methodology ...........................................................................................................................84
Watershed and Three Hydrodynamic Separator Configurations .....................................84
Physical Modeling Methodology ....................................................................................85
CFD Modeling Methodology ..........................................................................................86
Particulate Phase Modeling .............................................................................................89
Modeling of Static Screen and Cartridge ........................................................................90
CFD Parameters ...............................................................................................................91
Stepwise Step Modeling Removal and PM Separation ...................................................91
Result ......................................................................................................................................93
Event Hydrology Indices .................................................................................................93
Probabilities of PM Separation by the BHS, SHS, and VCF ..........................................95
Stepwise Steps Comparison to Measured Data ...............................................................96
Summary .................................................................................................................................98
5 REMOVAL AND PARTITIONING OF NITROGEN AND PHOSPHORUS OF
NUTRIENTS IN HYDRODYNAMIC SEPARATOR ON URBAN RAINFALL-
RUNOFF PARTICULATE MATTER GENERATED FROM IMPERVIOUS
SURFACE CARPARK ........................................................................................................111
Overview ...............................................................................................................................111
Objectives .............................................................................................................................112
Methodology .........................................................................................................................113
Catchment ......................................................................................................................113
Data Acquisition, Management, and Sampling .............................................................113
PM Separation ...............................................................................................................114
Water Chemistry Analysis .............................................................................................115
Nitrogen and Phosphorus Analysis ...............................................................................115
Partitioning Indices for Nitrogen and Phosphorus ........................................................116
Hydrologic and Loading Parameters .............................................................................117
Analysis of Recovered Sediment Deposit from Hydrodynamic Separator ...................117
Results...................................................................................................................................117
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Event Hydrology ...........................................................................................................117
Overall Treatment Efficiency of BHS as a Function of Hydrology ..............................118
PM fraction and PM-based N and P fraction masses distribution .................................119
Event based Nitrogen and Phosphorus Loadings ..........................................................119
Nutrients Removal Efficiency as a function of Hydrology ...........................................120
Nutrients Partitioning ....................................................................................................121
Nutrient from the Recovered Sediment Deposit ...........................................................123
Summary ...............................................................................................................................125
6 CONCLUSION.....................................................................................................................139
APPENDIX
A CHAPTER 3. PHYSICAL AND CFD MODELING OF PM SEPARATION AND
SCOUR IN HYDRODYNAMIC SEPARATORS ...............................................................142
B CHAPTER 4. STEPWISE STEADY CFD MODELING OF UNSTEADY FLOW AND
PM LOADING TO UNIT OPERATIONS ...........................................................................146
LIST OF REFERENCES .............................................................................................................159
BIOGRAPHICAL SKETCH .......................................................................................................169
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LIST OF TABLES
Table page
2-1 Medianwashout rate and effluent mass load as a function of flow rates. SM is sandy
silt in the Unified Soil Classification System (USCS). SM I is a SCS 75, SM II is a
SCS 106, and SM III is NJDEP gradation .........................................................................40
2-2 d10, d50, d90 for effluent SM I, SM II, and SM III as a function of flow rates. ...................41
2-3 d10, d50, d90 for pre-deposited PM. .....................................................................................42
2-4 The summary of RTD tests as a function of flow rate.Qd is hydraulic design flow
rate for baffled HS. Flow beyond 100% Qd over flows inlet weir and is not treated. .......43
3-1 Summary of measured and modeled separation and washout function result with
RPD. ...................................................................................................................................73
4-1 Hydrologic indices across storm events for BHS, SHS, and VCF. .................................101
4-2 CFD model comparisons to measured data across storm events for BHS, SHS, and
VCF. .................................................................................................................................102
4-3 CFD parameters for BHS, SHS, and VCF. ......................................................................103
5-1 Hydrologic characterization of the 10 rainfall-runoff events monitored between May
24, 2010 and August 21, 2010 in Gainesville, FL ...........................................................126
5-2 Summary of EMCs and ΔMass for total dissolved nitrogen (TDN), total nitrogen
(TN), total dissolved phosphorus (TDP), and total phosphorus (TP). .............................127
5-3 Summary of event mean value and range of variation of the dissolved fraction (fd)
and partition coefficient (Kd) of nitrogen and phosphorus for influent and effluent
runoff................................................................................................................................128
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LIST OF FIGURES
Figure page
2-1 A plan view schematic of the baffled HS testing facility, across-sectional profile of
the HS, and PSDs for pre-deposited PM. ...........................................................................44
2-2 Velocity as a function of ADV height at (A) location in baffled HS and the mean
flow velocity in the baffled HS as a function of flow rate and SOR.. ...............................45
2-3 Effluent PSDs for the range of flow rates as a function of flow rate. Gamma
parameters as a function of surface overflow rate. Design SOR = 464.5 L/min-m2. ........46
2-4 The relationships between turbidity and SORand PND as a function of SOR. The
relationships between turbidity and effluent PM and PND.. .............................................47
2-5 Median washout rate (g/min) and ti / τ as a function of surface overflow rate. Each
linear relationship in plot A and B as a function of SOR has a R2 ≥ 0.97. ........................48
2-6 Effluent PM as a function of densimetric Froude number, flow rates, and SOR.R2 is
0.96 for SM I, 0.96 for SM II, and 0.97 for SM III as a function of Froude number. .......49
2-7 Effluent mass load range of flow rates as a function of time normalized to maximum
duration. Washoff model parameters as a function of surface overflow rate. ...................50
2-8 Measured and modeled washout rate in g/minas a function of time normalized to
maximum duration and volume normalized to 7.6 turnover volume.................................51
2-9 Shield’s parameters for SM I, SM II, and SM III as a function of particle Reynolds
number. (u* is shear velocity, and v is kinematic viscosity of water). ...............................52
3-1 Modeled PSD plots of treated and washout PM from BHS, VHS, and SHS. ...................74
3-2 Plots of measured and modeled Δ mass and washout rate at constant SOR for BHS,
VHS, and SHS units. ..........................................................................................................75
3-3 Effluent PM trajectories inside the BHS, VHS, and SHS for particle with diameters
of 25 μm, 106 μm, and 300 μm, respectively. Particle density (ρp) is 2.65 g/cm3.............76
3-4 Washed out PM trajectories inside the BHS, VHS, and SHS for particle with
diameters of 10 μm, 25 μm, and 75 μm. Particle density (ρp) is 2.65 g/cm3. ....................77
3-5 Fluid velocity magnitude as a function of flow rates in BHS, VHS, and SHS. .................78
3-6 Probability of PM separation and washout by the BHS, VHS, and SHS. .........................79
3-7 Gamma parameters as a function of flow rates for BHS, VHS, and SHS (Qd for BHS
is 9.1 L/s, Qd for VHS is 79.3 L/s, and Qd for SHS is 31.2 L/s). .......................................80
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3-8 Volumetric efficiency (VE) as a function of flow rates in BHS, VHS, and SHS. .............81
4-1 Plot A is a plan view schematic of a BHS testing watershed in Gainesville, FL. Plot
B is a plan view schematic of SHS and MFS testing watershed in Baton Rouge, LA. ...104
4-2 Isometric views of the geometries of unit operations. .....................................................105
4-3 Probability of PM separation by unit operations. γ and β represent shape factor and
scaling factor. ...................................................................................................................106
4-4 A) is a CDF for the range of rainfall-runoff flow rate (L/s) in BHS. B) is flow rates
(L/s) and effluent PM mass (g) as a function of elapsed time in BHS.. ..........................107
4-5 A) is a CDF for the range of rainfall-runoff flow rate (L/s) in SHS. B) is flow rates
(L/s) and effluent PM mass (g) as a function of elapsed time in SHS.. ...........................108
4-6 A) is a CDF for the range of rainfall-runoff flow rate (L/s) in VCF. B) is flow rates
(L/s) and effluent PM mass (g) as a function of elapsed time in VCF.. ..........................109
4-7 Mean and variation of the stepwise steady model absolute RPD for BHS, SHS, and
VCF. The lower right quartile box plot is the variation of absolute RPDs. ....................110
5-1 Profile section of 1.21 m diameter BHS deployed for physical modeling loaded by
urban source area catchment. ...........................................................................................129
5-2 PM fraction and PM-based N and P fraction masses distribution within each
monitored rainfall-runoff event........................................................................................130
5-3 Separation for TN, and TP in different fractions as a function of PM fractions. Range
bars represent standard deviation. ....................................................................................131
5-4 Phosphorus mass concentration distributions for each PM fractions. ............................132
5-5 Nitrogen mass concentration distributions for each PM fractions. ..................................133
5-6 fd values and equilibrium coefficient, Kd values of nitrogen and phosphorus in
influent and effluent.. .......................................................................................................134
5-7 Granulometric equilibrium distribution of ammonium-nitrogen, nitrate-nitrogen,
TKN, phosphate and TP. ..................................................................................................135
5-8 The fd of influent and effluent TN (TP) as a function of cumulative treated rainfall-
runoff volume...................................................................................................................136
5-9 The cumulative gamma distribution parameters (ɤ for shape factor and β for scaling
factor) for event-based normalized particle size distributions (PSD)... ...........................137
5-10 Cumulative influent and effluent mass of PM, phosphorus (P), and nitrogen (N)
through the entire monitoring campaign for BHS in Gainesville, FL. ............................138
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LIST OF ABBREVIATIONS
ADM Axial Dispersion Model
ADV Acoustic Doppler Velocimetry
AOCMP Monodispersed AL-Ox Coated Granular Media
BHS Baffled Hydrodynamic Separator
BMP Best Management Practices
CDF Cumulative Density Function
CFD Computational Fluid Dynamics
COD Chemical Oxygen Demand
CSO Combined Sewer Overflows
CV Control Volume
D.O Dissolved Oxygen
DPM Discrete Particle Modeling
EMC Event Mean Concentration
FVM Finite Volume Method
HS Hydrodynamic Separator
ICP-MS Inductively Coupled Plasma – Mass Spectrometry
IPRT Initial Pavement Residence Time
MBE Mass balance Error
MS4 Municipal Separate Storm Sewer System
N Nitrogen
NJDEP New Jersey Department of Environment Protection
P Phosphorus
PAH Polycyclic Aromatic Hydrocarbon
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PDH Previous Dry Hours
PLC Programmable Logic Controller
PM Particulate Matter
PSD Particle Size Distribution
QA Quality Assurance
QC Quality Control
RANS Reynolds Averaged Navier-Stokes
RCF Radial Cartridge Filter
RPD Relative Percent Difference
RTD Residence Time Distribution
SHS Screened Hydrodynamic Separator
SIMPLE Semi-Implicit Method for Pressure Linked Equation
SOR Surface Overflow Rate
SM Non-Cohesive Sandy Silt
SSC Susupended Sediment Concentration
SSE Sum of Squared Errors
SWMM Storm Water Management Model
TDN Total Dissolved Nitrogen
TDP Total Dissolved Phosphorus
TDS Total Dissolved Solid
TISM Tanks in Series model
TKN Total Kjehldahl Nitrogen
TN Total Nitrogen
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TP Total Phosphorus
TSS Total Suspended Solid
UOPs Unit Operations and Processes
USCS Unified Soil Classification System
VCF Volumetric Clarifying Filtration
VE Volumetric Efficiency
VHS Vortex Hydrodynamic Separator
drain Rainfall Depth
irain-max Maximum Rainfall Intensity
ninf Number of Influent Samples
neff Number of Effluent Samples
Qd Hydraulic Design Flow Rate
Qmed Median Flow Rate
Qp Peak Flow Rate
ti The time at which tracer initially appears
tp The time at which peak concentration is observed
train Rainfall Runoff Duration
tt Theoretical residence time
t50 The time at which 50 % of tracer had passed through the reactor
t90 The time at which 90 % of tracer had passed through the reactor
MDI Morrill Dispersion Index
1 / MDI Volumetric Efficiency
ti/tt Index of short-circuiting
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tp /tt Index of modal retention time
t50/tt Index of average retention time
(t50)/(t50-tp) Hazen’s N
σ2 The variance
tΔmean Mean detention time based on discrete time step measurements, T
Ci Concentration at ith measurement, ML-3
Δti Time increment about Ci, T
σ2Δc Variance based on discrete time measurements, T
2
d Diameter of the soil particle
ρs Mass density of the soil
d Diameter of the soil particle
g Acceleration due to gravity
φ Angle of friction of the soil
SS Specific gravity of soil
*u The shear velocity
Shields parameter
Vrunoff Volume of Runoff
Cd Dissolved fraction concentration
Cp Particulate-bound fraction concentration
Cs Particulate-bound mass (mg/g of dry particulate mass)
fd Dissolved fraction
fp Particulate-bound fraction
Kd Partitioning coefficient
15
Md Dissolved mass
Mp Particulate-bound mass
MS Normalized Cumulative Mass Loading for PM
MTN Normalized Cumulative Mass Loading for Total Nitrogen
MTP Normalized Cumulative Mass Loading for Total Phosphorus
16
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
PHYSICAL AND CFD MODELS OF PM SEPARATION AND SCOUR IN
HYDRODYNAMIC UNIT OPERATIONS
By
Hwan Chul Cho
May 2012
Chair: John J. Sansalone
Major: Environmental Engineering Sciences
A hydrodynamic separator (HS) is commonly used as a preliminary unit operation for
separation of particulate matter (PM) and PM-associated constituents transported by urban
rainfall-runoff. Advantages of HS units are passivity, small treatment footprint, ease of
retrofitting into existing sewer or treatment system infrastructure, efficacy for neutrally-buoyant
substances and detritus, low head loss, and capacity for hydraulic bypass beyond a given flow
rate. Although the small footprint of an HS is advantageous for integration into sewer (storm or
combined) or drainage systems, it also concentrates flow energy. In many HS units where PM
sludge is not isolated or in units not maintained (cleaned) frequently, washout of previously
separated PM sludge can result in short periods of net export of PM.
The purpose of this study was to increase understanding of the hydrodynamic and
clarification response of best management practices (BMPs) for urban rainfall-runoff
management. Four types of unit operations were investigated by means of a coupled
experimental and numerical approach. Additionally, this study investigates PM washout from
three HS units as a function of steady flow rates and particle size distributions (PSDs), using a
computational fluid dynamics (CFD) modeling framework for “scour” assessment. CFD is a
branch of fluid mechanics that uses numerical methods to integrate the Navier-Stokes equations
17
to solve fluid flow problems. Four types of full-scale HS units were modeled in 3D using
FLUENT v 6.0. A finite volume method (FVM) was applied to discretize the governing
equations into the physical space directly. Modeling in 3-D is less susceptible to the
complications from the lack of geometric symmetry, complex static screen geometry, vortex
flow and gravitational forces on the motion of particles in unit operations. Post-processing the
CFD predictions provided insight into the mechanistic behavior of the HS by means of three
dimensional hydraulic profiles, particle trajectories and pressure distributions. A stepwise steady
flow model effectively predicts the monitored storm events data in UOPs.
This study examines the inter- and the intra- event nitrogen and phosphorus removal as a
function of particle size, hydrology and partitioning for an urban carpark, treated by unit
operations with significant biogenic loadings.
18
CHAPTER 1
INTRODUCTION
Impervious surface area and urban runoff can impair receiving waters. Many sources of
PM, nutrients (N, and P), metals, and anthropogenic chemicals are present and exposed to
rainfall-runoff in urban areas (Lee and Bang 2000). PM is a significant vehicle in the transport of
these constituents by urban runoff (Sansalone 2002). PM delivered by rainfall-runoff varies
temporally within a storm event and across storm events and can vary spatially within the same
watershed (Sansalone 2002). Hydrodynamic separators (HS) are commonly used urban unit
operations to remove oil and inorganic materials including PM in urban stormwater runoff and
combined sewer overflows (CSOs) (Brombach 1987, Brombach et al. 1993, Pisano et al. 1994,
USEPA 1999, Andoh and Saul 2003). In North America there are over 50,000 HS operating HS
units. The advantages of HS are that they are passive devices, often have a small footprint, and
can be easily retrofitted to existing infrastructure. However, many of these systems are left
unattended for long stretches of time (e.g., a minimum range of 1 to 47 days between rainfall
events in Gainesville, FL). An estimation of long term performance of detention basins has been
derived by storm water management model (SWMM) simulation (Nix et al. 1988). Minimizing
scour from a HS was recently added to the essential elements of best management practices
(BMPs) design (EPA 2004). Therefore, management of scour from HS is a challenge that need to
be addressed.
Sediment transport and re-suspension has been widely studied by many researchers,
however, most of research has focused on sediment re-suspension and transport mechanisms for
open channel flow (Cellino and Lemmin 2004, Gargett et al. 2004, Orlins, and Gulliver, 2003)
not in a HS. There is a need to quantify scour in a baffled HS (BHS) to provide designers with an
understanding of scour as function of particle diameter, and flow rates. In this study, a series of
19
scour tests was performed on BHS with two different sediment pre-loaded conditions with
particle size distributions (PSDs) defined prior to testing across six flow rates representing 25 %
(0.91 L/s) to 125 %(11.31 L/s) of the maximum hydraulic operating flow rate (Q) or design flow
rate (Qd) of the unit tested. The tests are conducted under steady flow regimes, to better
distinguish the effects of different pre-loaded PM and flow rates.
Residence time distribution (RTD) testing is conducted to characterize the flow mixing
behavior in a baffled HS. The experimental RTD testing is performed with pulse input method.
The flow rate (Q), and the geometry of a HS influences the flow mixing behavior of a HS. A
primary component of this study is a comparison of multi-phase physical modeling to CFD
modeling of HS. CFD approaches are increasingly utilized to model particle-laden flows (Curtis
et al. 2004; van Wachem et al. 2003). CFD can predict fluid flow, mass transfer, chemical
reactions, and related phenomena by solving governing fluid equations using numerical methods.
CFD modeling has been used for describing the behavior of rainfall-runoff unit operations and
processes (UOPs) (Pathapati and Sansalone 2009).
In urban stormwater, CFD has enhanced the modeling of PM separation for transient
flows (Sansalone and Pathapati 2009; Garofalo and Sansalone 2011) and heterodisperse particle
size distributions (Dickenson and Sansalone 2009) as well as re-entrainment of PM by scouring
mechanisms (Pathapati and Sansalone 2012). However, a 3-D numerical model to simulate the
transient hydrodynamics with variable PSDs needs longer computational time than a model
simulating steady state flow (Pathapati and Sansalone 2011). The unsteady computational time
for the hydrodynamic separator (HS) is approximately 3±0.5 days using a workstation equipped
with dual quad-core 2.6 GHz processors and 32 GB of random access memory (Pathapati and
Sansalone 2011). Stepwise step flow modeling can be used to reduce computational time.
20
Stepwise steady CFD modeling utilizes a series of discretized steady flow steps of rainfall-runoff
events. Identifying numbers of stepwise flow steps is necessary to ensure efficient use of
computational time.
Excessive phosphorus loading to natural waters has been known for decades to accelerate
eutrophication and lead to decline in water quality of rivers, lakes and oceans (Weon et al. 2002).
In urban rainfall-runoff, phosphorus partitions from PM into dissolved phases. Rainfall-runoff
characteristics, including water chemistry, unit residence time, and unsteady loading, dictate the
partitioning of phosphorus and nitrogen. Additionally, oxidation-reduction, mixing, particulate
composition and particulate bound concentration gradients can affect the extent of partitioning of
phosphorus and metals (Sansalone and Buchberger 1997). PM-based phosphorus distributes
across the PSDs (Ma et al. 2010). Particulate-bound phosphorus is distributed across sediment (>
75 μm), settleable (25 ~ 75 μm) and suspended (< 25 μm) fractions. Phosphorus predominantly
found in urban rainfall runoff is associated with particles greater than 75 µm (Sansalone et al.
1998). Physical unit operations typically only target the particulate bound fraction of phosphorus
(Barrett et al. 1998, Bartone and Uchrin 1999, Comings et al. 2000, Dechesne et al. 2005). For
this reason, the phosphorus control in physical unit operations may be ineffective without
knowledge of the partitioning. Phosphorus partitioning is important not only to understand
phosphorus fate and transport, but also to select appropriate unit operation in the watershed.
Elevated nitrogen is ubiquitous in urban watersheds (Hopkinson and Vallino 1995).
Nitrate-nitrogen leaching from biogenic material is a source of contamination of surface and
groundwater (Broschat 1995, Ku and Hershey1997). Total nitrogen in runoff is associated with
dissolved and particulate phases as well as biogenic material. Nutrient uptake is greatly
influenced by environmental variables such as water availability and temperature (Marschner
21
1995). Characterization of nitrogen in urban runoff is necessary not only for improving treatment
strategies for nitrogen reduction, and but also for identifying viable treatment.
This dissertation focuses on the examination of scour, PM separation mechanisms,
partitioning of nitrogen and phosphorus, and behavior of a BHS to separate PM, nitrogen and
phosphorus. The performance of a BHS is evaluated and studied by controlled and uncontrolled
physical models. Models are subjected to steady conditions with regulated PSD gradation and
actual rainfall-runoff treatment under stepwise steady step conditions as well as CFD modeling.
22
CHAPTER 2
PHYSICAL MODELING OF PARTICULATE MATTER WASHOUT FROM A
HYDRODYNAMIC SEPARATOR1
Overview
Particulate matter (PM) serves as a vehicle for the transport of chemicals, acting as a
substrate to which chemicals reversibly partition (Berretta and Sansalone 2011a; Brezonik 2002;
Dean 2005). Beyond being a substrate for chemicals, PM also impacts the turbidity and oxygen
demand of receiving waters (Sansalone 2002, Shinya et al. 2000). Screened hydrodynamic
separator (HS) units are commonly used as preliminary unit operations for publicly-owned-
treatment-works (POTWs), followed by primary clarification (sedimentation) and filtration. For
baffled HS units, PM is separated by settling, although the original design intent was primarily
for oil and grease separation (OGS). Historically, screened forms of HS units have been applied
to preliminary and high-rate treatment of combined sewer overflows (CSOs) (Brombach et al.,
1987; Brombach et al.; 1993; Pisano et al., 1994; USEPA 1999; Andoh et al., 2003). Advantages
of HS units are passivity, small treatment footprint, ease of retrofitting into existing sewer or
treatment system infrastructure, efficacy for neutrally-buoyant substances and detritus, low head
loss, and capacity for hydraulic bypass beyond a given flow rate. Nonetheless, commensurate
with these advantages are disadvantages. For instance, while a small footprint provides economy
and integration into standard drainage structures, the limited spatial footprint and volume results
in concentration of hydraulic energy, short residence times and limited PM separation whether
for Type I (discrete) or II (flocculent) settling. One challenge associated with HS units is the
need for frequent maintenance (cleaning) so that, as an HS collects PM, the unit neither functions
1 Re-printed with permission from Cho, H., and Sansalone, J.J., (2012), Physical modeling of particulate matter
washout from a hydrodynamic separator, Journal of Environmental Engineering
23
as a source of PM and chemicals through washout, nor generates anaerobic conditions in the
stored PM sludge (USEPA 2002, Sansalone et al. 2010).
There has been a long history of scour research, with a specific interest in bridge
foundation scour under the influence of flood flows, the study of which dates back over a half
century (Shields 1936; Vanoni 1946). More recently, there have been questions regarding the
applicability of approaches such as Shields scour diagram across different flows regimes
(Buffington 1999). Although HS units are subject to open-channel flow, they are small footprint
units subject to highly unsteady flows, high velocities and complex intra-unit hydrodynamics.
Because these units are infrequently (on the order of once a year) maintained (Sansalone and
Pathapati 2009) high fluid velocities at the PM sludge interface generate scour and washout.
Hence, there is a need to characterize these phenomena. In this study, PM washout is specifically
examined through controlled physical modeling supported by PM, flow and velocity monitoring.
Velocity monitoring uses Acoustic Doppler Velocimetry (ADV) to measure fluid velocities and
vectors (Kraus et al. 1994; Lohrman et al.1994;Voulgaris et al. 1998). In addition, residence time
distribution (RTD) curves are used to characterize the hydrodynamic behavior of systems
operating at steady flow (Fernandez-Semprere 1994). The RTD methodology is well-
documented and therefore not reproduced herein (Levenspiel 1962; Froment and Bischoff 1979;
Smith 1981; Denbigh and Turner 1984; Fogler 1992; Westerterp et al. 1993).
In this study, physical model load-response experiments are performed on a full-scale
baffled HS subject to the following conditions: (1) pre-deposited PM of differing PSDs and (2)
differing steady flow rates representing 10 % (0.91 L/s) to 125 % (11.31 L/s) of the maximum
design flow rate (Qd). For the HS unit, washout is hypothesized to be a function of the PSD of
the pre-loaded PM and of the flow rate (or SOR) at a given PM deposit (sludge) depth. In order
24
to test this hypothesis, data, including effluent PM and PSDs, velocity profiles in the HS and
RTDs, are determined at each flow rate and for each batch of pre-loaded PM. It is hypothesized
that washout of the pre-loaded PM can be explained using these data to illustrate effluent PM
load generated from previously settled PM deposited in the HS. It is further hypothesized that
effluent PM concentration is a function of the densimetric Froude number and SOR for each
PSD. These results are contrasted with the open-channel approach using the Shields’ criterion.
Methodology
Physical Model Configuration
A schematic of the HS component of the physical model system is presented in Figure 2-
1 along with the HS profile view. While the HS unit was initially developed as a
commercial/industrial OGS unit, (hence the inverted horizontal baffle design), for over a decade
the baffled HS has primarily served as a small urban watershed settling unit for PM (USEPA
1999). In this controlled physical model study the source influent for each model run is potable
water (PM < 0.1 mg/L as suspended sediment concentration (SSC) turbidity <0.3 NTU and 20°
C ± 2°C) stored in two 45 m3 tanks. The flow delivery system consists of two centrifugal pumps
(19 L/s – 3 HP and 75.6 L/s– 10 HP). The flow monitoring system consists of dual monitoring
meters in parallel, an electromagnetic meter for flows from 1 to 272 L/s and a volumetric flow
meter for flow from 0.1 L/s to 10 L/s. The diameter of the baffled HS is 1.22 m.
The distance from the bottom of the HS lower chamber to the invert of the HS outlet is
1.52 m (Hmax). The surface area of the unit is 1.17 m2. At the design flow rate, one unit volume
(one turnover volume) is approximately 1780 L. The inlet and outlet diameters are 0.2 m each.
The design flow rate (Qd) is 9.05 L/s, which is observed to represents 90% of the hydraulic
capacity of the unit (defined as the flow rate at which bypass over the overflow weir begins).
25
Flow Velocity Measurement
All ADV-based velocity measurements are taken within the HS. The velocity
measurements are monitored using a 10 MHz-ADV (velocity range: 1 mm/s – 2.5 m/s)
(Sontek/YSI). The velocity determination is based on the Doppler shift principle, which is
implemented with a bistatic (focal point) acoustic Doppler system and consists of a transmitter
and three receivers (Voulgaris 1998). The ADV is a multi-function (sound emitter, sound
receiver, and signal conditioning electronic module) water velocity monitoring device for
precise, in-situ measurement of velocity.
Fluid velocities are measured at 10 locations in the HS. In a horizontal plane at the water-
PM bed interface, (0.17Hmax; Hmax = 1.52 m), there are seven measurement points. In addition,
velocity measurements are made at the center of the unit, location (A) at 3 different heights
(0.34, 0.50, and 0.67Hmax). A schematic of the velocity measurement locations is shown in
Figure 2-1A. Fluid velocity is measured at each flow rate during the washout and RTD runs.
Pre-deposited PM
Three hetero-disperse and non-cohesive sandy silts (SM) are utilized in three series of
separate but equivalent testing conditions to examine HS washout. The Unified Soil
Classification System (USCS) is utilized for textural classification of each gradation of PM
(Coduto 1999). The mass-based PSDs of the pre-loaded PM are measured utilizing wet
dispersion laser diffraction based on the principle of Mie small-angle light scattering and
diffraction due to the presence of particles between the laser emitter and detector (Finlayson-Pitts
and Pitts 2000). Diffraction patterns are dependent on particle sizes, with larger scattering angles
associated with smaller size particles. Although the siliceous silts are sub-rounded, Mie theory
applies not only to spherical but also non-spherical particles (Jonasz 1991). Scattering patterns
are utilized to determine the % volume of particle sizes of the PSD through an optical method in
26
which the detectors and windows are integral parts of the measurement zone. The particle
analyzer used is a Malvern Mastersizer with a measurement range of ~0.02 to 2000 μm. The
three PM gradations are designated as SM I, SM II and SM III, with a d50m of 15 μm, 22 μm and
67 μm, respectively. PM gradations are generated from standard gradations available from U.S.
Silica.
As part of the testing matrix summarized in Table 2-1, PM washout testing is performed at
0.91 (10), 2.26 (25), 4.78 (50), 6.79 (75), 9.05 (100), and 11.31 L/s (125% of Qd). Each washout
run is conducted by first pluviating PM across the entire surface area (1.17 m2) of the HS bottom
chamber to a level depth of 0.17Hmax. The HS is then filled with water at a flow rate of less than
0.5 L/s to avoid any flow scouring of the PM bed. Since HS units store runoff between runoff
events, runs are conducted with the HS unit filled with water to the outflow invert. Flow is
diverted around the HS until a steady-state flow is achieved. Once flow is directed into the HS,
sampling is initiated and discrete 1L samples are manually taken in duplicate at equal volume
sampling intervals, calculated based on flow rate and time. Run duration is chosen such that
approximately 7 volumetric turnovers are achieved (each HS volume is 1.78 m3). Ten duplicate
samples are collected for PSD and PM analysis (as SSC). Studies have demonstrated that SSC is
a representative gravimetric index for hetero-disperse PSDs, which include sand-size or coarser
PM (Gray et al. 2000). SSC analysis is carried out by filtering the entire volume of each replicate
through a nominal 1.0 μm filter (ASTM 1999; Ying and Sansalone 2008).
RTD Test
The RTD quantifies the hydrodynamic characteristics of a unit operation at a given flow
rate (Fogler 1992). In this study, the friction velocity at the water-PM interface (0.17Hmax)
generated at a given flow rate is indexed by the SOR of the HS unit. The RTD of the baffled HS
is determined by using NaCl as non-reactive tracer and measuring conductivity as a function of
27
flow rate and time. A concentrated NaCl solution is injected as a pulse input. The RTD analysis
time is approximately 3-4 times the theoretical mean residence time (Nauman and Buffham
1983). Effluent conductivity is monitored with a calibrated conductivity probe (600 OMS-O)
manufactured by YSI Inc. Each tracer test is validated by a mass balance check with the error
tolerance set at ± 5% by mass. The inert tracer (NaCl: 200 g/500 mL) is prepared in 1-L
polypropylene (PP) bottle, and injected in the influent drop box as a single pulse. The tracer
concentration is measured at the effluent pipe for each fully-developed flow rate from 2.26 to
11.31 L/s. The conductivity probe is fully submerged at the outfall of the effluent pipe.
Conductivity measurements are taken at 1 second intervals. The mean residence time is defined
as follows (Hazen 1904; Tchobanoglous et al. 2003):
0
0
)(
)(
dttC
dttCttmean (2-1)
The RTD function E(t) is related to the concentration, C(t) while the cumulative RTD
curve is designated as the F curve. E(t) and F(t) are expressed as follows.
0)(
)()(
dttC
tCtE (2-2)
t
tEtF0
)()( (2-3)
The following RTD indices are reported in this study: tt,, the theoretical residence time,
(defined as tt = V/Q, where V is the volume of the HS unit, and Q is the flow through the system);
the Morrill dispersion index (MDI := t90/t10, where tx is the time at which x% of the tracer had
eluted from the HS); 1/MDI, the ‘volumetric efficiency’; tΔmean, the mean detention time based
on discrete time steps and Hazen’s N; N = (t50)/(t50-tp) (Levenspiel 1972; Letterman 1999), an
index of short-circuiting used for overflow models (Magill and Sansalone 2010); and tp, the time
28
at which peak concentration is observed. The values of tx (x=10, 50, 90, etc) and tp are obtained
from measured tracer data. The mass balance error with respect to the mass of tracer injected was
± 5 %. The variance of tracer concentration as a function of time is approximated as follows.
(2-4)
In this expression, σ2
c is the variance and is based on discrete time measurements (T2); ti
is the time at which tracer initially appears.
Scour Thresholds
The applicability of the Shields approach to scour thresholds in the HS unit and
commensurate PM washout is tested in this study. Scour of non-cohesive PM at the water-PM
deposit interface is induced when the effective vertical and lateral confining stresses acting on
PM at this interface are less than the shear stress generated by the flow at this interface
(Annandale 2006). Common parameters to estimate PM transport capacity include the critical
shear stress, shear stress, and shear velocity. The critical shear stress, (τc) needed to generate the
incipient motion of a particle is expressed as follows (Beheshti and Ataie-Ashtiani 2008).
tan)(3
2 sc gd (2-5)
In this expression, d is particle diameter, ρs is mass density of PM or soil, g is acceleration
due to gravity, and is the internal angle of friction of the PM or soil. Peck (1974) determined
a range of 29 < < 41 for very loose to very densely packed non-cohesive sediment. The critical
shear stress cannot be predicted directly from a Shields curve and requires an iterative procedure.
(Beheshti and Ataie-Ashtiani 2008). Shear stress can be defined based on the shear velocity.
2
*u (2-6)
2
2
2)( m e a n
ii
iii
c ttC
tCt
29
/* u is the shear velocity. The Shields parameter, , which is widely used to predict
the initiation of particle motion, is not measured directly but rather derived from shear stress and
velocity. When τ = τc, the shear stress is expressed in dimensionless form as the Shields
parameter.
gdS
u
s )1(
2
*
(2-7)
In this expression, Ss is the relative viscosity (=γs/γ, where γs is the sediment viscosity
[ML-1T-1]; γ is the water viscosity [ML-1T-1]); and g is the acceleration due to gravity [L/T2].
Scour occurs when the dimensionless shear stress τ0 is larger than θ.
Results
In-Situ Velocity Profiles
Tables 2-1 and 2-2 summarize the physical model conditions and the results of 18
washout experiments performed on the baffled HS with SM I, SM II and SM III pre-loaded PM
deposits. As illustrated in Figure 2-2, the flow velocities in the HS range from 0.7 to 44.6 mm/s
for all locations, depths and flow rates. Spatial symmetry does not consistently translate into
velocity profile symmetry. For example, when comparing symmetric locations (D) and (E) or (F)
and (G) in Figure 2-1A, the velocity profiles are not symmetric. However, there is an
approximately linear relationship between flow rate and mean velocity at each spatial location
and at each depth along the central axis of the HS. Maximum velocities consistently occur near
the effluent drop pipe which is located between locations (B) and (C) in Figure 2-1. Given the
requirements of continuity and the smaller 102 mm diameter of the effluent drop-pipe, higher
velocities are expected due to the pressure increase at the effluent drop-pipe. Based on the spatial
distribution of these velocities with a minimum mean velocity occurring at (A), all further
velocity measurements are performed with the ADV fixed at location (A) for each depth.
30
Washout PM Granulometry
The granulometry of eluted PM is represented as mass-based PSDs, particle number
density (PND), turbidity and PM measured as SSC. Washout response of the HS is examined as
a function of SOR and pre-deposited PM gradation based on measured effluent PSDs. The PSD
results in Figure 2-3 indicate that PM washout is predominately fine PM for all gradations of pre-
deposited PM. Upon converting the PSD into a mass distribution, the effluent mass-based d50m
ranged from 0.8 to 1.0 μm for SM I, from 1.2 to 3.1 μm for SM II, and from 5.0 μm to 15.0 μm
for SM III for the range of flow rates tested in Figure 2-2. Higher flow rates generate consistently
higher values of d50m for all gradations. In Figures 2-3A, C, and E, the mass-based PSDs are
plotted. These graphs take the form of a two-parameter gamma distribution. The probability
density function of a gamma distribution is given by the following expression.
)(
)(
)(
1
x
ex
xg (2-8)
In this expression, Γ is the gamma function; γ is the shape factor and β is the scaling
factor. Below, G(x) is the cumulative gamma distribution and x represents particle diameter.
x
dxxgxG0
)()( (2-9)
Representation of PM size scale is through the scaling parameter, β, while the
representation of the shape (hetero-dispersivity or decreasing uniformity of the size) of the PSD
is parameterized by the shape parameter, γ. These parameters are displayed as a function of SOR
in Figures 2-3B, D and F for each eluted PSD. Figures 2-3B, D and F illustrate an increasing γ
which indicates a finer PSD at a constant β; whereas an increasing β indicates a coarser PSD at
constant γ. The increased uniformity of the eluted PSD as compared to the heterogeneity of the
31
pre-deposited PM gradation.. The shape parameter remains relatively constant across the range
of SORs in particular for the coarser hetero-disperse PM, SM III. In contrast, the scaling
parameters for the eluted PSDs generated from the much finer and nominally more uniform SM I
and II remain essentially constant and equal. The β values are up to an order of magnitude lower
than for the eluted PM generated from SM III.
As seen in Figures 2-4A and C the phenomenon of turbidity, which is primarily generated
by fine PM, displays linear trends with SOR. These trends are unique for each pre-deposited
gradation. Figure 2-4 also illustrates the linear relationship between effluent PM as a gravimetric
concentration and turbidity. The results in Figures 2-4B and D do not illustrate distinctly
different linear relationships for SM I and II due to the primary influence of eluted fine PM on
concentration and turbidity. In contrast, while the eluted PND and turbidity are also linearly
related for SM III as shown in Figure 2-4B and D this coarser and hetero-disperse gradation
produces an elution with lower PND and turbidity.
Residence Time Distributions (RTDs)
Table 2-3 illustrates the RTD indices of the HS as a function of flow rates. At lower flow
rates the low volumetric efficiency values indicate that the entire HS volume is not utilized. For a
given HS unit size the flow rate (or SOR) is seen to be a primary factor in determining washout
behavior of the HS unit. SOR is calculated as follows.
A
QSORrateoverflowSurface )( (2-10)
In this expression Q (L3/T) is flow rate and A (L
2) is the surface area. Ideal discrete
settling theory (Tchobonaglous et al. 2003) suggests that if a particle has a settling velocity
greater than the SOR, then the particle is separated as in a clarification basin. PM separation is
calculated as follows.
32
dxQ
AuXseparatedPM
CX p
C 0
)1( (2-11)
The first term on the right of this expression represents the mass fraction of particles with
settling velocity (up) greater than SOR and the second term represents the mass fraction of
particles with settling velocity (up) less than Q/A. SOR was used as an index which allows a
direct comparison to Type I (discrete) settling velocities.
Measured residence times are lower than theoretical residence times for flow rates lower
than the design flow rate (9.05 L/s), and higher than theoretical residence times for flow rates
higher than the design flow rate. At lower flow rates, volumetric zones of the HS are not
mobilized, while at higher flow rates these “dead zones” are mobilized. Table 2-3 provides
indices such as σ2/ tt
2 and volumetric efficiency based on MDI, t50/tt, and the modal retention
time index tp/tt. Measured and theoretical residence times did not differ significantly at the design
flow rate of 9.05 L/s, suggesting that at the design flow these zones are mobilized.
Figure 2-5 illustrates the median washout rate and short circuiting index as a function of
SOR. Median washout rates increase linearly with increasing SOR for each of the PM
gradations. Figure 2-5A illustrates that SM I, the finest gradation, has the highest median
washout rate of the three gradations. The index of short circuiting, ti/tt also increases linearly with
increasing SOR, similarly to the behavior of the median washout rate. With increasing SOR
there is higher washout rate from the HS as the volume at the PM-water interface is increasingly
mobilized.
Densimetric Froude Number
Previous research (Aguirre-Pe et al. 2003) has suggested using a modified form of the
Froude number, the densimetric Froude number, to relate flow characteristics to scour (in this
case washout). The densimetric Froude number as a function of PM diameter is useful as an
33
independent variable in predicting the washout, as shown in Figure 2-6. This Froude number
relates flow velocity to the PSD granulometric and gravimetric characteristics as follows:
2/1
50 ])1[( mgds
VFr
(2-12)
In this expression, s is the specific gravity, d50m is from the effluent PM and V is the flow
velocity in the baffled HS. The flow velocity, V, in the baffled HS is measured using ADV. To
evaluate sedimentation for a particle subject to Type I settling, SOR as a function of PM size is a
commonly used design index parameter for wastewater and stormwater detention/retention
basins. SOR has units of velocity (L/T). The upward overflow velocity is compared with the
nominal downward velocity of each particle size to determine sedimentation.
Time Rate of Washout
As the flow ranged from 10 to 125% of Qd, the median SSC eluted ranged from 8.2 mg/L
to 20.6 mg/L for SM I, from 7.3 mg/L to 13.2 mg/L for SM II, and from 2.0 mg/L to 10.4 mg/L
for SM III. For SM I at a flow of 0.1Qd, the eluted SSC increased by 13% as compared to SM II
and by 310% as compared to SM III. The effluent d50 values summarized in Table 2-2 illustrate
that the eluted PM was dominated by PM in the suspended range. Figure 2-7 shows that mass
washout decreases exponentially with time. For each gradation, the net-eluted mass approaches a
low asymptotic equilibrium value after approximately 0.7(t/tmax), which equates to approximately
9500 L or 5.3 turnover volumes for this unit. The initial exponential decline lasting up to
0.4(t/tmax) is likely a result of scouring the pre-deposited non-cohesive PM interface where the
effective confining stress is essentially zero and there is negligible interlocking between
particles. As a saturated non-cohesive bed, the primary source of shear strength is frictional and
the surface layer does not benefit from a confining stress. There is a clear dependence of the
initial washout magnitude on the corresponding magnitude of the influent flow rate. However,
34
irrespective of the flow rate and PM gradation, a first-order exponential washout model similar
to that employed by Sartor and Boyd (1974), Alley (1981), and Alley and Smith (1981) for
washoff of PM from pavement by runoff is observed. Analogous to these washoff models where
PM availability is not limiting, the evolution of washout can be expressed as follows.
n
ktn
n
dtemM0
0
0
(2-13)
In this expression, t is the effluent sampling time (min); M is the mass load (g); m0 is M
for the initial washout rate (g/min), and k is inverse washout time scale (1/min). The mass and
rate of washout are modeled by this equation. Eluted mass rates as a function of volume are
shown in Figure 2-8 for each flow rate.
Initiation of Scour
The resolution of the ADV utilized in this study ranged from 1 mm/s to 2.5 m/s. Fluid
velocities were measured at a level within 5 mm of the sediment surface, providing an
approximation of the velocity at the water-PM interface that induced shear. To account for
fluctuations of velocities at each measurement location all velocities were obtained as a mean of
seven equally distributed locations across the area of the HS at a given level as shown in Figure
2-1. In addition, at each location, velocities in the x, y and z direction were recorded in triplicate
at 1 second intervals, for each steady flow rate, for run times ranging from a minimum of 20
minutes to a maximum of 100 minutes. The open volume encompassed by the three small legs of
the ADV is approximately 3000 mm3. The impact of the ADV legs and cable on the flow field
were assumed to be negligible as the HS volume is much larger (1.78 m3).
Shear stresses are determined using measured velocities adjacent to the water-PM
interface. The results are shown in Figure 2-9 for each deposited PM gradation. SM I, SM II and
SM III illustrate similar trends for shear stress and critical shear stress at the water-PM interface.
35
Based on physically measured velocities, PSDs and flow rates, the PM bed interface shear stress
does not provide a clear relationship with the observed washout rates. Currently, there is no shear
stress criterion for HS units. The dimensionless shear stress in the HS at each flow rate is
significantly lower than the Shields criterion, suggesting that there should not be scouring.
However, even with low shear stresses measured washout occurs. Using the Shields criterion the
washout rate cannot be inferred directly from the calculated shear stress at the PM-water
interface of the baffled HS. Given that the baffled HS is a circular Type I settling tank, a
modified form of the scour velocity formula derived from Stoke’s Law by Swamee and Tyagi
(1996) is used to calculate scour velocity in settling tanks and it is also tested against the
measured results.
125.02
3
)1(
B
QgdskV sc
sc
(2-14)
In this expression, Vsc is the scour velocity; s is the specific gravity of the PM deposit; g
is the acceleration due to gravity; d is the scoured PM size; Q is the flow rate; B is equivalent
width of the settling tank, and k ranges from 0.5 and 0.8 depending upon the specific geometry of
the configuration (Ingersoll and McKee 1956). Resulting scour velocity ranged from 0.003 to
0.01 mm/s, across the SOR range. These velocities are sufficiently smaller than measured
velocities, indicating that washout should not be generated; a result not supported by the
observed washout.
The results of this study have several practical implications. One implication relates to
the frequency of maintenance to minimize washout. For instance, for the baffled HS it is shown
that washout occurs when separated PM reaches a given depth. This depth can be calibrated for
the HS unit with catchment PM loading data and annual rainfall-runoff volume either as
36
measured data or from continuous simulation models such as the Stormwater Management
Model (SWMM). While HS units are all predominately Type I settling units, there are a number
of internal configurations for HS units. For example, in contrast to the horizontally-baffled HS
unit of this study, a screened HS consisting of a screened and volute chamber is only capable of
separating coarser PM size (> 75 μm) from stormwater, in the range of the coarser sand-size
fraction of SM III. The screened HS design directs flow energy in a downward spiral towards the
sump that collects these PM deposits, allowing for mobilization and washout of PM (Pathapati
and Sansalone 2009). The standard 2400 μm screen provides apertures capable of passing PM as
coarse as gravel-size. In contrast, results from this study illustrate that the design of the baffled
HS unit volumetrically isolates the deposited PM. There is a tradeoff between hydraulic capacity
(flow rate or SOR), dissipation of flow energy and the propensity for washout. The horizontally-
baffled HS unit provides dissipation of incoming flow energy with depth and, correspondingly,
creates an isolation zone for deposited PM. In comparison, in the screened HS flow energy is
directed circumferentially downward and into the vertically-screened area to the sump containing
deposited PM, before exiting upward and outward through the screen, the volute chamber, and
finally from the unit.
This study illustrates that washout is a function of the PSD of deposited PM and also of
SOR. While deposited PSDs and SOR can be controlled to a degree by HS unit sizing and
design, results of this study suggest that isolation of the deposited PM is critical. Even with
isolation above the deposited PM, many studies have demonstrated that water chemistry can
change significantly in unit operations that store submerged or wet PM deposits between runoff
events. For example, sumps with stored runoff and PM can go anaerobic within two days; far
shorter than the mean time between runoff events for nearly all climatic regions of the USA
37
(Sansalone et al. 2010). Additionally, PM sludge deposits in wet sumps are habitat for microbial
growth and a source for chemical leaching from PM (Ying and Sansalone 2008). Frequent
cleaning is not practiced for de-centralized unit operations (manufactured or non-proprietary).
Yet the tradeoff is that unmaintained unit operations can be unintended sources of PM,
pathogens and chemicals thereby degrading their intended role as temporary sinks between
maintenance points. While there are environmental benefits from more frequent maintenance,
stakeholders can also benefit through generation of load credit programs to offset maintenance
costs. States such as Florida are providing nutrient load credits for quantifiable and documented
maintenance practices of unit operations and drainage appurtenances (Berretta et al. 2011b).
If consideration is given to on-line versus off-line installation of HS units, from these
results, the baffled HS unit provided isolation of deposited PM. With isolation, effluent indices
of the baffled HS for each PM gradation tested were nominal but demonstrable at the design flow
rate with gravimetric concentrations of 9 to 18 mg/L, effluent turbidity ranged from 10 to 40
NTUs and PND was in the range of 109 #/L. While these results can allow on-line application
with maintenance of the baffled HS, other HS configurations are more prone to washout. By
comparison, at the design flow rate of the screened HS described above, the PM washout
concentration for the coarsest PM gradation (SM III) is 51 mg/L. Therefore on-line versus offline
applications require washout evaluations as conducted in this study. Additionally, while the
PSDs used are reproducible certification gradations, these are inorganic (siliceous) particles and
the aqueous matrix is a reproducible potable water matrix. This provides a reproducible and
precise testing metric for washout comparisons but may not accurately reproduce field conditions
of varying runoff chemistry, a mixture of biogenic and anthropogenic PM, microbial growth and
anaerobic conditions in unit operation sumps and sludge zones.
38
Summary
Hydrodynamic separator (HS) units are commonly deployed in small developed
watersheds to provide stormwater PM separation. A baffled HS unit, one common HS type, was
analyzed for washout of pre-deposited PM as a function of surface overflow rates (SOR) indexed
as flow rates from 10 to 125% of the HS design flow. Washout was also examined for three pre-
deposited hetero-disperse PSDs of sandy silt PM (SM I at < 75 μm, SM II at < 100 μm, and SM
III at < 1000 μm). Velocity measurements and an RTD analysis were utilized to obtain a
hydrodynamic ‘signature’ of the HS. Results indicate that the spatial symmetry of the baffled HS
does not consistently translate to spatially-similar velocities at the water-PM interface. Results
from RTD analysis with an inert tracer demonstrated that the mean and theoretical residence
times did not differ significantly at the design flow rate. Only as the flow approached design
flow rate was the volume of the HS mobilized towards the depth of the water-PM interface. As a
function of SOR, the median rate of washout ranged from 0.4 to 13.3 g/minute for SM I, from
0.3 to 4.9 g/minute for SM II, and from 0.2 to 3.1 g/minute for SM III and were statistically
significantly different.
A densimetric Froude number, relating flow velocity to gravimetric and granulometric
PM indices of the washout PM, reproduced modeled PM washout as a mass concentration for all
PSDs across the SOR range. During washout the finer PM at or near the water-PM interface
subject to negligible effective confining stress was preferentially mobilized and eluted. The
unmobilized coarser PM fraction of the PSD functioned to confine lower PM and attenuate
continued PM elution. For each PM gradation there was an exponential decline in PM washout
on a gravimetric basis as a function of washout time. In contrast to gravimetric-based washout,
eluted turbidity and particle number density (PND) primarily influenced by finer suspended PM,
both displayed a linearly increasing washout trend as a function of SOR and PSD. Washout, as
39
measured by turbidity and PND, differed markedly between the coarsest (SM III) and the finer
PSDs (SM I and II). Irrespective of the basis used, washout indices are a function of SOR and
PSD for a given depth of PM deposits.
Using the Shields criterion, an open channel approach to washout, negligible washout
was predicted, which did not replicate the measured washout from the HS unit. The scour
velocity method for settling tanks of Swamee and Tyagi (1996) also did not reproduce the
measured washout results of this study. Results suggest the investigation of models capable of
coupling hydrodynamics and PSD/PND such as computational fluid dynamics (CFD).
40
Table 2-1. Medianwashout rate and effluent mass load as a function of flow rates. SM is sandy
silt in the Unified Soil Classification System (USCS). SM I is a SCS 75, SM II is a
SCS 106, and SM III is NJDEP gradation
Texture
Classification
of PM
Target
flow
rate
(L/s)
Operating
flow
rate
(L/s)
Surfaceover
flow rate
(L/min-m2)
Volume
of
flow
(L)
Time
duration
of run
(min)
Median
washout
rate
(g/min)
Median
SSC
[mg/L]
Effluent
mass
load
(g)
SM I
(Sandy silt
< 75 µm)
γ = 0.8,
β = 28.9
0.91 0.90 46.7 12902 250.0 0.43 8.2 298.5
2.26 2.46 126.3 14205 100.0 1.06 9.8 386.5
4.78 4.80 245.3 14394 50.0 2.81 11.0 485.4
6.79 7.00 348.5 13861 33.3 4.89 13.9 644.0
9.05 9.30 464.5 13936 25.0 7.33 17.8 742.5
11.31 11.60 580.5 13931 20.0 10.28 20.6 897.6
SM II
(Sandy silt
< 100µm)
γ = 0.7,
β = 157.0
0.91 0.90 46.7 12955 250.0 0.28 7.3 167.6
2.26 2.42 126.3 14538 100.0 0.81 7.7 247.7
4.78 4.73 245.3 14205 50.0 1.64 8.5 286.0
6.79 6.98 348.5 13824 33.3 2.35 9.4 431.6
9.05 9.37 464.5 14072 25.0 3.11 11.0 559.3
11.31 11.65 580.5 13992 20.0 4.88 13.2 692.2
SM III
(Sandy silt
< 1000 µm)
γ = 0.6,
β = 232.6
0.91 0.90 46.7 13142 250.0 0.18 2.0 168.7
2.26 2.45 126.3 14214 100.0 0.52 2.5 226.2
4.78 4.79 245.3 14316 50.0 1.89 5.4 263.7
6.79 6.88 348.5 13842 33.3 2.60 7.5 387.5
9.05 9.21 464.5 13358 25.0 2.80 8.8 428.6
11.31 11.61 580.5 13607 20.0 3.11 10.4 536.6
41
Table 2-2. d10, d50, d90 for effluent SM I, SM II, and SM III as a function of flow rates.
Effluent PM
Operating
flow
rate
(L/s)
SM I
(Sandy silt < 75 µm)
SM II
(Sandy silt < 100µm)
SM III
(Sandy silt < 1000 µm)
d10
(µm)
d50
(µm)
d90
(µm)
d10
(µm)
d50
(µm)
d90
(µm)
d10
(µm)
d50
(µm)
d90
(µm)
0.91 0.6 0.8 1.6 1.7 1.2 9.3 6.7 5.0 34.7
2.26 0.7 0.8 2.0 1.9 2.0 15.6 7.9 5.9 51.7
4.78 0.8 0.8 2.0 2.2 2.3 16.4 8.3 6.2 54.6
6.79 0.8 0.9 2.2 2.4 2.3 17.3 9.0 8.6 60.4
9.05 0.8 1.0 2.2 2.5 2.5 17.7 10.2 10.6 65.4
11.31 0.8 1.0 2.3 2.5 3.1 17.3 10.6 15.0 74.6
42
Table 2-3. d10, d50, d90 for pre-deposited PM.
Pre-deposited bed PM
Pre-
deposited
PM depth
(m)
SM I
(Sandy silt < 75 µm)
γ = 0.8, β = 28.9
SM II
(Sandy silt < 100µm)
γ = 0.7, β = 157.0
SM III
(Sandy silt < 1000 µm)
γ = 0.6, β = 232.6
d10
(µm)
d50
(µm)
d90
(µm)
d10
(µm)
d50
(µm)
d90
(µm)
d10
(µm)
d50
(µm)
d90
(µm)
0.25 1.6 15.0 70.3 1.8 22.0 75.2 7.2 67.0 335.9
43
Table 2-4. The summary of RTD tests as a function of flow rate.Qd is hydraulic design flow rate
for baffled HS. Flow beyond 100% Qd over flows inlet weir and is not treated.
RTD statistics
Q
(L/s) 2.26
(L/s)
4.78
(L/s)
6.79
(L/s)
9.05
(L/s)
11.31
(L/s)
% of Qd 25% 50% 75% 100% 125%
τ 786.1 372.3 261.9 196.5 157.2
tmean (s) 749.1 337.3 222.9 197.0 182.1
ti (s) 45.0 34.0 32.0 28.0 26.0
tp (s) 68.0 52.0 53.0 52.0 46.0
t10 (s) 81.0 69.0 61.0 49.0 45.0
t50 (s) 495.0 209.0 159.0 137.0 125.0
t90 (s) 1587.0 687.0 521.0 407.0 323.0
MDI 19.6 10.0 8.5 8.3 7.2
1/MDI 0.05 0.10 0.12 0.12 0.14
ti/tt 0.06 0.09 0.12 0.14 0.17
tp/tt 0.09 0.14 0.20 0.26 0.29
σ2/tt
2 1.36 0.27 0.31 0.27 0.09
t50/tt 0.63 0.56 0.61 0.70 0.80
Hazen's N 1.16 1.33 1.50 1.61 1.58
44
Diameter, d (m)
0.11101001000
Per
cen
t fi
ner
by m
ass
(%)
0
20
40
60
80
100
SM I
SM II
SM III
Pre-deposited PM
Figure 2-1. Plot A) is a plan view schematic of the baffled HS testing facility and the velocity
meters placement in the baffled HS is shown. Plot B) illustrates across-sectional
profile of the HS through the centerline of the unit. Plot C) PSDs for pre-deposited
PM.
A) Plan view of HS testing system
B) Section view of Baffled HS
Pre-deposited
PM
C) PSDs for pre-deposited PM
45
Flow rate (L/s)
0 2 4 6 8 10 12
Mea
n v
eloci
ty (
mm
/s)
0
20
40
60
80(a) 0.17 H
max
(a) 0.33 Hmax
(a) 0.50 Hmax
(a) 0.67 Hmax
Flow rate (L/s)
0 2 4 6 8 10 12
Mea
n v
eloci
ty (
mm
/s)
0
20
40
60
80(b) 0.17 H
max
(c) 0.17 Hmax
Surface overflow rate (L/min-m2)
0 20 40 60 80 100 120
Mea
n v
eloci
ty (
mm
/s)
0
20
40
60
80(d) 0.17 H
max
(e) 0.17 Hmax
Surface overflow rate (L/min-m2)
0 20 40 60 80 100 120M
ean
vel
oci
ty (
mm
/s)
0
20
40
60
80(f) 0.17 H
max
(g) 0.17 Hmax
Figure 2-2. Velocity as a function of ADV height at (A) location in baffled HS and the mean
flow velocity in the baffled HS as a function of flow rate and SOR. (Hmax = 1.52 m;
the distance from the bottom of the unit to the invert of the outlet is 1.52m).
46
Diameter, d (m)
0.11101001000
Per
cent
Fin
er b
y M
ass
(%)
0
20
40
60
80
100
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Per
cent
Fin
er b
y M
ass(
%)
0
20
40
60
80
100
Pre-deposited
sediment SM II
( = 0.7, = 24.6)
Pre-deposited
sediment SM I
( = 0.8,
= 18.6)
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
0
1
2
3
4
0
10
20
30
40
0
1
2
3
4
0
10
20
30
40
SM I ( = 0.8, = 18.6)
SM II ( = 0.7, = 24.6)
A)
C)
B)
D)
Diameter, d (m)
0.11101001000
Per
cent
Fin
er b
y M
ass(
%)
0
20
40
60
80
100 Pre-deposited
sediment SM III
( = 0.6, = 232.6)
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
0
1
2
3
4
0
10
20
30
40
SM III ( = 0.6, = 232.6)
E)
F)
Figure 2-3. Effluent PSDs for the range of flow rates as a function of flow rate (A –SM I, C –
SM II, E – SM III).Gamma parameters as a function of surface overflow rate (B – SM
I, D – SM II, F – SM III). Design SOR = 464.5 L/min-m2.
47
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600T
urb
idit
y (
NT
U)
0
10
20
30
40
50
60
70
SM I
SM II
SM III
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
Par
ticl
e num
ber
den
sity
(#/L
)
0.0
2.0e+9
4.0e+9
6.0e+9
8.0e+9
1.0e+10
1.2e+10
1.4e+10
SM I
SM II
SM III
Effluent PM [mg/L]
0 10 20 30 40 50 60 70
Turb
idit
y (
NT
U)
0
10
20
30
40
50
60
70
SM I
SM II
SM III
Particle number density (#/L)
0.0
2.0
e+9
4.0
e+9
6.0
e+9
8.0
e+9
1.0
e+10
1.2
e+10
1.4
e+10
Turb
idit
y (
NT
U)
0
10
20
30
40
50
60
70
SM I
SM II
SM III
A) B)
C) D)
Figure 2-4. For each non-cohesive PSD (SM I at < 75 μm, SM II at < 100 μm, and SM III at <
1000 μm), plots A and C illustrate the relationships between turbidity and surface
overflow rate (SOR)and PND (particle number density) as a function of SOR,
respectively. For these PSDs, plots B and D illustrate the relationships between
turbidity and effluent PM and PND respectively. All R2 values exceed 0.94 and p =
0.05.
48
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
Med
ian w
ash
out
rate
(g/m
in)
0
4
8
12
16
SM I ( = 0.8, = 18.6)
SM II ( = 0.7, = 24.6)
SM III ( = 0.6, = 232.6)
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
Short
cir
cuit
ing i
ndex
, t i /
0.00
0.05
0.10
0.15
0.20
Index of Short circuiting (ti / )
A)
B)
Figure 2-5. Median washout rate (g/min) and ti / τ as a function of surface overflow rate. Each
linear relationship in plot A and B as a function of SOR has a R2 ≥ 0.97.
49
Froude number
0.0 0.5 1.0 1.5 2.0
Eff
luen
t P
M [
mg/L
]
0
20
40
60
80
100
Flow rate (L/s)
SM I
SM II
SM III
Surface overflow rate
(L/min-m2)
0 100 200 300 400 500 600
Eff
luen
t P
M [
mg/L
]
0
20
40
60
80
100
Flow rate (L/s)
2.9 6.0 9.1 12.2 2.0 3.9 5.9 7.8 9.7
3.1 5.9 8.8 11.6SM II
SM I
4.4 8.8 13.1 17.5 21.9 26.3
11.7
SM III
SM I, II
6.2 13.4 21.5 28.7SM III
(Fr=V / [(s - 1)gd50m
]1/2
)
SM I
SM II
SM III
Figure 2-6. Effluent PM as a function of densimetric Froude number, flow rates, and SOR.R2 is
0.96 for SM I, 0.96 for SM II, and 0.97 for SM III as a function of Froude number. R2
is 0.98 for SM I, 0.97 for SM II, and 0.98 for SM III as a function of surface overflow
rate.
50
t / tmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Eff
luen
t m
ass
load
(g)
0
100
200
300
400
500
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Eff
luen
t m
ass
load
(g)
0
100
200
300
400
500
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
m (
g)
0
300
600
900
1200
1500
k (
1/m
in)
0.0
2.5
5.0
7.5
10.0
12.5
m
k
m (
g)
0
300
600
900
1200
1500
k (
1/m
in)
0.0
2.5
5.0
7.5
10.0
12.5
m
k
SM I ( = 0.8, = 28.9)
SM II ( = 0.7, = 24.6)
SM I ( = 0.8, = 28.9)
SM II ( = 0.7, = 24.6)C)
A) B)
D)
t / tmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Eff
luen
t m
ass
load
(g)
0
100
200
300
400
500
Surface overflow rate (L/min-m2)
0 100 200 300 400 500 600
m (
g)
0
300
600
900
1200
1500
k (
1/m
in)
0.0
2.5
5.0
7.5
10.0
12.5
m
k
SM III ( = 0.6, = 232.6)
SM III ( = 0.6, = 232.6)E) F)
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Figure 2-7. Effluent mass load range of flow rates as a function of time normalized to maximum
duration (A – SM I, C – SM II, E – SM III). Washoff model parameters as a function
of surface overflow rate (B – SM I, D – SM II, F – SM III).
51
t/tmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
SM I
SM II
SM III
Modeled (SM I)
Modeled (SM II)
Modeled (SM III)
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
t/tmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
V/Vmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
V/Vmax
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Was
ho
ut
rate
(g
/min
)
0.1
1
10
100
1000
Q = 0.91 L/s
Q = 4.78 L/s
Q = 2.26 L/s
Q = 6.79 L/s
Q = 11.31 L/s
tmax
= 250 min
tmax
= 100 min
tmax
= 50 min tmax
= 33 min
tmax
= 20 min
Q = 9.05 L/s
tmax
= 25 min
Figure 2-8. Measured and modeled washout rate in g/minas a function of time normalized to
maximum duration and volume normalized to 7.6 turnover volume.
52
Reynolds number (Rep* = u
*d/v
0.0001 0.001 0.01 0.1 1 10 100 1000
Cri
tica
l S
hie
lds
stre
ss (
c
c/(S
Sg
d)
1e-4
1e-3
1e-2
1e-1
1e+0
1e+1
1e+2
1e+3
d10
d50
d90
d10
d50
d90
d10
d50
d90
SM I
SM II
SM III
Figure 2-9. Shield’s parameters for SM I, SM II, and SM III as a function of particle Reynolds
number. (u* is shear velocity, and v is kinematic viscosity of water).
53
CHAPTER 3
PHYSICAL AND CFD MODELING OF PM SEPARATION AND SCOUR IN
HYDRODYNAMIC SEPARATORS
Overview
Rainfall-runoff (stormwater) transports a mixture of hetero-disperse particulate matter
(PM) and chemicals that partition to and from PM (Sheng et al 2008; Lee and Bang 2000). Once
entrained in stormwater, PM separation is challenging in an urban environment due to
uncontrolled variable of flow rates, spatially-distributed loadings and land area or infrastructure
constraints to support stormwater control. Therefore, small-footprint devices such as
hydrodynamic separators (HS) units (U.S. EPA 1999; Rushton 2004) have been deployed
specifically as preliminary unit operations for PM where larger scale retention basins that
provide hydrologic and PM control are not needed (Rushton 2004). A unique feature of HS units
is that the particle trajectory can potentially approach that of much larger basins (Field and
O’Connor 1996). While HS units have been in use for decades, modeling their PM separation is
fairly recent whether as a function of steady flow (Sansalone and Pathapati 2009) or for storm
events with varying flow rates and hetero-disperse particle size distributions (PSD) (Sansalone
and Pathapati 2009; Kim and Sansalone 2008). While unit operation models can reproduce PM
fate with physically-based methods such as combining SOR and PM phenomena, intra-event
data are critical to couple unsteady flow rates with PSDs, PM granulometry, and partitioning
(Sansalone et al. 2010). Therefore many unit operations models remain as steady flow
approaches. On the other hand, models such as the Stormwater Management Model (SWMM)
were designed for the complexity of fully unsteady flow in complex urban systems (Huber and
Dickenson 1988), with a focus on modeling how the urban interface modified rainfall-runoff
relationships.
54
In recent years, computational fluid dynamics (CFD) has been applied to environmental
engineering. For example, CFD has been used to model the behavior of a screened HS under
steady flow and constant PSDs (Faram and Harwood 2003). CFD modeling can provide detailed
information regarding the unit operation load-response, including velocity profiles, particle
tracks, pressure gradients, species transport, density and thermal gradients and turbulence
(Versteeg and Malalasekera 1995). Similarly regulatory agencies (State of Pennsylvania 2003)
require HS units to be physically modeled at steady flow rates and constant PSDs.
Over the last several decades studies of PM separation by HS units have been examined
through physical and numerical models. Field and O’Connor (1996) reported that swirl and
vortex feature of HS units give longer magnitude of particle trajectory than in traditional unit
operations. Fenner et al. (1997, 1998) have suggested that no single dimensionless group can be
used in describing, and scaling the PM separation performance. Li et al. (1999) in their study of a
partial exfiltration system determined that 2-D numerical model has been applied to simulate
variably saturated flow. U.S. EPA (1999) report of HS concluded that HS mostly rely on
gravitational force as well as centrifugal forces in order to separate PM. Andoh et al. (2003)
studied a hydrodynamic vortex separator (HDVS) and found that CFD simulation could be
applied for the assessment of the efficiency of a HDVS intended for PM separation. Luyckx and
Berlamont (2004) considered a vortex separator and their modeling indicated that a vortex
separator is based on settling velocity of PM. Cates et al. (2009) monitored 26 storm events with
HS unit and overall TSS removal was 59%, while turbidity results show a median EMC
reduction of 57%. A baffled HS (BHS), which functions primarily as a sedimentation device,
has been tested to assess the performance of PM removal and a function has been developed
linking PM removal to Péclet number (Pe) (Wilson et al. 2009).
55
More recently, studies of HS units have examined PM washout (commonly as scour
testing) through physical and numerical models. From a monitoring campaign that encompassed
seven rainfall-runoff events, Yu and Stopinski (2001) reported that effluent concentrations
exceeded influent PM concentrations for a VHS. Ruston (2004) carried out a monitoring
campaign for a SHS loaded by a 54 hectare surface parking area and demonstrated that 51% of
the events produced higher effluent than influent PM concentrations as total suspended solid
(TSS). Kim et al. (2007) tested a SHS for PM washout (scour testing) and documented that the
pre-deposited PM height in the sump had the most dominant impact on the degree of scour
during the duration of each run. A screened HS (SHS), which combines vortex separation,
screening and sedimentation, has been tested for PM separation and washout using a pre-
deposited heterodisperse particle size distribution (0.1 to 2000 μm) as a function of steady flow
rates (Pathapati and Sansalone 2012). Washout from a BHS has been physically modeled as a
function of SOR and PSDs for a fixed depth of PM deposits (Cho and Sansalone 2012). Results
indicated that a densimetric Froude number reproduced modeled PM washout as a mass
concentration.
HS units have been tested physically (Jianghua et al. 2009; Wilson et al. 2009) as well as
numerically using CFD (Andoh et al. 2003) to quantify PM separation. Cellino and Lemmin
(2004) demonstrated that the burst cycle plays an important role in PM suspension mechanics in
open channel flow. Gargett et al. (2004) investigated PM transport and re-suspension in shallow
seas. Research related to PM transport and re-suspension has predominantly focused on open
channel flow such as clear-water and shallow seas, however, modeling of PM washout (re-
suspension) has not been done much for unit operations in urban watershed area. In this study the
56
complexity of PM separation and washout in HS units can be quantified by a physically
validated CFD modeling approach.
Objectives
This study applies the methodology to three mechanistically unique HS, each with a
unique geometry. The three HS are modeled so as to have the same SOR. This provides basis for
comparison that is independent of the geometry. In this study, physical model of PM separation
and washout experiments are performed on a full-scale BHS, VHS, and SHS subject to the
following conditions: (1) PM injection and pre-deposited PM of PSDs known a priori across a
range of SOR and (2) differing steady flow regimes representing 10 to 125 % of the design flow
rate (Qd) for each HS units. The physical models are then compared with CFD modeling. This
study then develops a separation function for each HS as a function of particle diameter which
can be utilized to predict washout under different pre-deposited PM conditions, flow rates and
PSDs.
Methodology
Physical Clarification and Re-suspension Function Modeling
This study was performed with three different types of commercial HS units: BHS, VHS,
and SHS. The hydraulic design flow rate (Qd) for BHS, VHS, and SHS are 9.1 L/s, 79.3 L/s, and
31.2 L/s, respectively. The Qd for the 3 types of HS unit are provided by the manufacturer. The
BHS has a diameter of 1.2 m, a sedimentation chamber height of 1.5 m, surface area of 1.2 m2,
and a volume of 1.8 m3. The BHS is made for specifically oil and grease and floatables
separation and consists of a sedimentation chamber, drop tee inlet and outlet pipes, and an
overflow weir. PM separation is dominated by gravitational separation. Ranges of SOR are from
47.3 to 590.9 L/min-m2, which is between 0.10 and 1.25 of the BHS Qd. The VHS has a
rectangular chamber, a width of 3.1 m, a depth of 1.2 m, a height of 2.1 m, surface area of 3.7 m2,
57
and volume of a 7.0 m3. The VHS consists of a swirl chamber and two baffles. PM separation is
predominantly due to vortex and gravitational separation. Ranges of SOR are from 128.0 to
1280.2 L/min-m2, or, between 0.10 and 1.00 of the VHS Qd. The SHS has a diameter of 2.1 m, a
screened chamber height of 1.7 m, surface area of 3.6 m2, and a volume of 6.1 m
3. The SHS
consists of static-screen of 2400 μm in width and vortex gravitational separation chamber.
Vortex and gravitational separation are the dominate PM separation mechanisms despite the
static-screen installed in the unit. Ranges of SOR are from 52.3 to 653.5 L/min-m2, or, between
0.10 and 1.25 of the SHS Qd.
The influent and pre-deposited PM consists of hetero-dispersed and non-cohesive sandy
silt (SM). The mass-based PSDs of influent and pre-deposited PM are shown in Figure 3-1. The
d50m of SM is 67 μm. Over 50% of the influent gradation consists of particles finer than75 μm.
Based on Unified Soil Classification System (USCS) standard, this gradation is classified as
‘well-graded sandy silt’ and denoted as ML (Liu et al 2008; ASTM 2006). The duration of each
run is chosen for a volumetric turnover of approximately 7.6 (BHS volume is 13.7 m3, VHS
volume is 53.2 m3, and SHS volume is 46.4 m
3). BHS and SHS are analyzed under six different
flow rates ranging from 0.10 to 1.25 Qd. VHS is analyzed under five different flow rates (0.1 Qd
– 1.00 Qd) for separation and washout function.
For the separation function, the PM injection tank had to be cleaned thoroughly using
potable water with a hose and brush such that no PM was present in the slurry tank prior to the
run. The pipes leading to the BHS, and SHS units were flushed with potable water to remove any
PM or biogenic material in the unit. The PM injection tank was filled with 180 liters of clean
water into which the prepared SM gradation was added. The programmable logic control (PLC)
was set with the target flow rate and the PM injection pump was set with the steady injection rate
58
in Hz. The data logger (CR3000) was compiled to start logging the flow rate every second. Ten
duplicate 1 L samples were collected for PSD, and PM analysis, (as suspended solid
concentration (SSC)). Previous studies have demonstrated that SSC is a representative
gravimetric index for hetero-disperse gradations that include sand-size or coarser PM (Gray et al
2000). SSC analysis is carried out by filtering the entire sample volume of each replicate through
a nominal 1.0 µm filter (ASTM 2007; Ying and Sansalone 2008). Separation function is
measured by SSC removal (%), which is computed as a percentage (by mass) of particles
captured by the HS (MHS) relative to the particles (MINF) added into the system.
100(%) INF
HS
M
MPSD (3-1)
A mass balance analysis is also conducted after every event to ensure mass conservation
based on influent, effluent and recovered mass in HS. A criterion is set by requiring the
magnitude of the mass balance error (MBE) to be equal to 10 by dry mass.
(3-2)
Washout function tests are performed at 5 and 6 different flow rates for VHS (0.10 Qd –
1.00 Qd) and BHS (0.10 Qd – 1.25 Qd). The SHS was performed with 2 different flow rates (1.00
Qd and 1.25 Qd). Each re-suspension run is conducted by first pluviating the entire surface area
of the bottom chamber of the HS with PM to a pre-deposited PM. Since HS units store runoff
between runoff events, each run is conducted with an HS unit filled with water above an
undisturbed pre-deposited PM bed. Flow is diverted around the HS until a steady-state flow is
achieved. Once re-suspension flows are directed into the HS, sampling is initiated and discrete1
L samples are manually taken in duplicate at equal sampling intervals, calculated based on flow
rate and time. PSDs and SSC are analyzed for washout function. Washout rate is measured by
100(%)
INF
EFFHSINF
M
MMMMBE
59
washout mass load (g), which has been computed as a ratio of mass of particles washed out by
the HS (WHS) to the duration time (T) of the system.
T
WrateWashout HS (3-3)
The intensity of washout is expressed as washout rate (g/min) while the magnitude of
washout can be evaluated by effluent mass load (or effluent concentration in this specific case
because total influent volume was set as a constant per each run).
CFD Modeling
The Navier-Stokes equations can define any single-phase fluid flow, but are non-linear
partial differential equations. It is difficult to solve them directly, so we need numerical methods.
CFD is a branch of fluid mechanics that uses numerical methods to integrate the Navier-Stokes
equations in order to solve fluid flow problems. Three types of full-scale HS units are modeled in
3D using FLUENT v 6.0. A finite volume method (FVM) is applied to discretize the governing
equations into the physical space directly. Modeling in 3-D is less susceptible to the
complications which arise from the lack of geometric symmetry, complex static screen geometry,
vortex flow and gravitational forces on the motion of particles in HS.
A k-ε model is suited for separation and washout function (Morin et al 2008; Liang et al
2005; Pathapati and Sansalone 2011). Two-equation Reynolds Averaged Navier-Stokes (RANS)
models are applied to swirling multiphase flows in the HS (Pathapati and Sansalone 2009;
Garofalo and Sansalone 2011). The standard k-ε model has been applied to turbulent flow model
in HS successfully (Pathapati and Sansalone 2009; Garofalo and Sansalone 2011).
CFD Governing Equations
The governing equations are derived for incompressible flow. The conservation of mass
and momentum are determined using the RANS equations as following
60
0
z
w
y
v
x
u (3-4)
The momentum equations are as follows:
x momentum:
xg
z
u
y
u
x
u
x
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(3-5)
y momentum: ygz
v
y
v
x
v
y
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(3-6)
z momentum:
zg
z
w
y
w
x
w
z
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(3-7)
In these equation, is fluid density, u, v, and w are Reynolds averaged fluid velocities, g
is sum of body forces, and p is the Reynolds averaged pressure. The momentum continuity
equation for the x, y and z directions can be obtained by assigning values of u correspondingly,
where u is combining of the x, y, and z velocity vector component, since the hydrodynamics of
HS vary as a function of x, y, and z spatial coordinates. The 3-D Navier Stokes equations for a
Newtonian fluid are determined by 3-D velocity vector components.
Turbulence modeling is widely applied the two-equation k-ε model. k and ε equations
allow one to determine the turbulent velocity and length scales independently. The transport
equations of the standard k-ε model are expressed by the following equations.
For k and ε:
k
jk
t
j
i
i
Gx
k
xku
xk
t)()(
(3-8)
kCG
kC
xxu
xtk
j
t
i
i
i
2
21)()(
(3-9)
61
In this expression, is the generation of due to the mean velocity gradients; is the
generation of due to buoyancy; , , are constants; , are turbulent Prandtl
numbers for and ; , are user-defined source terms. The values of C1ε, C2ε, C3ε, σk and σε
used in this model are 1.44, 1.92, 0.09, 1.0 and 1.3 respectively (Launder and Spalding 1974).
Newton’s law of viscosity is applied to illustrate the relationship between viscous stresses and
“Reynolds stresses”. It should be noted that eddy viscosity (µ) is a non-physical quantity, and is
expressed by the following equation.
(3-10)
In this expression, is turbulent kinetic energy per unit mass, [L2T
-2] and is the rate of
dissipation of turbulent kinetic energy per unit mass, [L2T
-3].
As an assumption of the standard k-ε model, μ is taken to be isotropic,. From the standard
k-ε model equations, the information regarding the flow field and turbulent field, such as
velocity profiles, kinetic energy, eddy energy and eddy diffusivity, are generated. The standard
k-ε model with standard wall functions is found to be an effective approach to modeling flow
through the BHS.
Particulate Phase Modeling
For the both separation and washout function studies, the Euler-Lagrangian approach is
applied to model the particle behavior in the HS. This approach is valid for dilute multiphase
flows when PM volume fraction is less than 10% (Elgobashi 1991). A Lagrangian discrete
particle model (DPM) is applied to track particles. Due to the extremely dilute nature of the flow,
the DPM assumes there are no particle-particle interactions. Particle trajectories are calculated by
kG kbG
k1C
2C 3C k
k kS S
2kf
k
62
integrating the particle force balance equation. The Lagrangian DPM is derived from force
balances based on Newton’s law describing particle settling (Pathapati and Sansalone 2009).
p
p
pD
p guuF
dt
du
)()(
(3-11)
24
Re182
pD
pp
D
C
dF
(3-12)
pp
DR
a
R
aaC
2
321 (3-13)
uud pp
p
Re (3-14)
where, up is a particle velocity, u is fluid velocity; ρp is particle density, ρ is fluid density; dp is a
particle diameter; µ is viscosity; a1, a2, and a3 are empirical constants that apply to smooth
spherical particles as a function of the Reynolds number (Morsi and Alexander 1972); and Rep is
a particle Reynolds number.
The PSD is divided into 18 classes of particles based on a standard sieve. The particle
diameter is constant within same class. Particles are tracked for each steady flow rate. The
particles that become trapped in the HS are considered to have been removed through HS. The
particle removal efficiency is calculated by the following equation.
(3-15)
In this expression, is the number of particles that remain in the baffled HS, and NI is
the number of particles injected at the inlet.
100*I
HS
N
Np
H SN
63
Re-suspension and Washout Modeling
The method for modeling re-suspension from a flat PM bed is performed. An Eulerian-
Lagrangian approach is used for modeling. First, turbulence modeling is used to obtain a steady
state flow field. Following this, a series of plane surfaces is created in the PM deposit region of
each HS. The interval between these plane surfaces is equal to at least one particle diameter for
each particle size tracked. Particles are then placed (velocity = 0, to simulate particles at rest) at
grid points across these surfaces. With mesh size of ranging from 1.5 cm to 2.0 cm, each layer
has 37802, 9586, and 45472 points available for tracking particles for BHS, VHS, and SHS units,
respectively. Particles are then tracked for tracking lengths obtained by a simulated tracer study
using ‘massless’ particles. The error of tracer study is calculated to be ± 1%. This provides an
unbiased tracking length that can be used to predict PM washout. The particles that exit the HS
through the outlet are considered to be re-suspended.
Numerical Procedure
The geometries are spatially discretized into 3.2, 5.5, and 2.1 million units for the BHS,
VHS, and SHS. As mentioned in previous sections, FVM and a second order upwind scheme are
applied in this study. In addition, the Semi-Implicit Method for Pressure Linked Equation
(SIMPLE) algorithm (Patankar 1980) is applied.. Through convergence of the numerical process,
a numerical method can meet pre-designated standards of consistency and stability. Convergence
is achieved when the error residuals fall below the preset convergence criteria (10-4
).
Volumetric Efficiency Calculation
Results of the tracer study are then compared with different units’ results from validated
CFD models for residence time distribution (RTD) and complete hydrodynamic profiles of HS,
including the spatial frequency distribution of velocities. The following RTD indices are reported
in this study: tt,,the theoretical residence time, (defined as tt = V/Q, where V is the volume of the
64
HS unit, and Q is the flow through the system); the volumetric efficiency (VE) (VE := t10/t90,
where tx is the time at which x% of the tracer had eluted from the HS) (Levenspiel 1972;
Letterman 1999).
Results
Comparison Physical and CFD Modeling
Results of separation and washout function for the three HS units are compared with
results from experimentally validated CFD models, as shown in shown in Table 3-1 and Figure
3-2 as a function of flow rates. The absolute relative percent difference (RPD) is used to evaluate
CFD model results with respect to the full-scale physical model. Absolute RPD is calculated by
the following equation.
100datameasured
data)modeleddata(measuredRPDabsolute
(3-16)
Results indicate that the CFD model predictions of PM removal and re-suspension rate
reproduce the measured data with an absolute RPD less than 10% across inflow rate (0.10 Qd to
1.25 Qd).
Physical Modeling of Separation and washout Function
Results for separation and washout function are measured as Δ mass (% and g) for BHS,
VHS, and SHS units as a function of flow rates ranging from 10 to 100% of Qd for each unit.
BHS and SHS were also tested at 1.25 Qd. The same results for separation and washout function
are summarized in Table 3-1, along with SOR. For separation function, PM removal for the
tested gradation ranged from 52.3 to 77.6%, at 0.10 to 1.25 for the BHS Qd, and for the SHS it
ranged from 42.3 to 70.0%, at 0.10 to 1.25 Qd. On the other hand, the CFD results showed PM
removal for BHS ranging from 51.7 to 74.6%, with RPD less than 5.1% for all flow rates (up to
9.1 L/s in BHS). CFD results for SHS show PM removal ranging from 42.0 to 69.9%, with RPD
65
less than 8.6% (up to 31.2 L/s in SHS). Physical modeling of separation function was not
conducted for the VHS. However, the CFD results indicate that PM removal has a decreasing
trend as flow rate is increasing, ranging from 42.8 to 62.3%, under the specified operating range
of flow rate. PM removal varies slightly for the three HS units in the SOR range of 47.3 to
1280.2 L/min-m2 which indicates that the PM removal significantly depends on SOR.
In order to describe this phenomenon, the fundamental separation mechanisms utilized by
BHS, VHS, and SHS are identified. The influent flow is directed into the lower chamber of
system by the head created at the weir and orifice configuration in BHS. This system
incorporates gravitational settling. The cylindrical design of the BHS is to avoid turbulent eddies
and dead zones during high flow rates which might re-suspension the settled PM from the
bottom of the system. The influent flow in VHS is directed to the swirl chamber where it forms,
a vortex. Vortex makes coarser PMs to settle down in the swirl chamber. Then, flow goes into
second settling chamber containing baffles. The internal hydraulic geometry of SHS is designed
such that the entire influent flow enters the volute area by passing through the screen. Therefore,
the overall particle separation in a SHS is accomplished by two serial UOPs. This conceptual
process flow model also requires that the particle gradation entering the volute area be directly
influenced by the separation performance in the screen area.
Figure 3-2 illustrates the variation of mean effluent SSC and washout rate for HS units as a
function of flow rates. Both the mean washout mass load and washout rate show a generally
linear increase with increasing flow rate for all units. There was higher washout and a wider
range in PM washout from the VHS, and SHS than BHS – ranging from 1.1 to 25.1 g/min (from
47.3 L/min-m2 to 590.9 L/min-m
2 in BHS), from 20.8 to 964.9 g/min (from 128.0 L/min-m
2 to
1280.2 L/min-m2 in VHS), and from 2227.3 to 3046.0 g/min (from 52.3 L/min-m
2 to 653.5
66
L/min-m2 in BHS), at 100% of pre-deposited PM capacity. In contrast, the CFD results showed
washout rates for BHS ranging from 1.0 to 25.5 g/min, with RPD less than 9.1% for all flow
rates (up to 590.9 L/min-m2). CFD result for VHS indicates that washout rate has an increasing
trend while flow rate increases, ranging from 19.4 to 977.0 g/min (up to 1280.2 L/min-m2).
Physical modeling for washout function was not conducted at low flow rates for the SHS,
however, CFD results for SHS showed PM removal ranged from 263.3 to 3299.9 g/min, with
RPD less than 8.3% (up to 653.5 L/min-m2) under the specified SOR. The washout rates vary
slightly for the three units in the SOR range of 47.3 to 1280.2 L/min-m2 which means that the
PM removal significantly depends on SOR. Also, comparing the types of HS units, it is clear that
the geometry of HS has a significant impact on washout, especially in regards to VHS and SHS.
SHS has more than 9 times higher washout rate at similar SORs. Overall, BHS had lower
washout than VHS, and SHS across operating flow rates. One reason for this is that in BHS unit,
hydraulic energy is dissipated as the flow hits the drop tee inlet pipe, thereby creating a more
quiescent environment. On the other hand, the VHS, and SHS units have no energy dissipation
and also have the added washout due the vortex in the swirl chamber.
PSD Result
A comparison was made among the performance of the HS units (BHS, VHS, and SHS)
to evaluate the impact of changing the flow rate on PSD. Figure 3-1 illustrates the variations in
the effluent and washed out d50m across flow rates. CFD results show that the absolute RPDs for
each flow rate in all the HS units are less than 10%. Results clearly demonstrate that the effluent
d50m becomes coarser with increasing flow rate for all 3 HS units. The d50m increased linearly
from 4 to 17 μm through 10 to 125% of Qd in BHS, from 21 to 43 μm through 10 to 125 %of Qd
in VHS, and from 15 to 21 μm through 10 to 125% of Qd in SHS for separation function.
67
Compared to changes in the d50m for effluent PSD, the particle size variability is more
pronounced as shown in Figure 3-1 which illustrates the d50m for PSD gradations.
Figure 3-1 illustrates PSDs in the effluent at constant flows from 10 to 125% of design
flow rate with SM gradations. As hypothesized, the HS unit discharged finer PSDs when the
effluent flow had lower flow rates. Except for a few discrepancies, the results indicate that the
effluent PSD became increasingly coarser with increasing flow rate. The general trends of PSDs
in Figure 3-1 for VHS and SHS did not vary as much as BHS across the range of flow rates.
Effluent PSDs were more significantly influenced by the geometry of the HS.
Washout PSD data obtained from each experimental run is compared to investigate the
performance of each unit as function of flow rates. Results demonstrate that washout PSDs from
the BHS consist predominantly of fine particles, indicating that coarse particles are not washed
out from the BHS. Washout PSDs are finer at low flow rates than at higher flow rates due to the
correspondingly smaller stream power available to suspend particles. This translates into a
relatively stable washout rate at these flow rates, which agrees with the overall system washout
rate previously discussed. It appears as if the mildly increasing washout rates possibly vary
linearly over the range of flow rates studied. The minimum particle diameter not washout from
the BHS was approximately 38 μm at 100% PM capacity at the Qd. A larger fraction of coarse
particles (> 75 μm) was not washout in the BHS, in contrast with the VHS and SHS in Figure 3-
1. Plot (D) in Figure 3-1 illustrates PSDs in the washout at constant flows from 10% to 100 % of
design flow rate at 100% of PM capacity for the SM gradation in VHS. The results from the
VHS show that a larger gradation of particles was eluted from the unit with increasing flow rates.
Washout PSDs were closer to the SM gradation with increasing flow rates. The minimum
particle diameter not washout from the VHS was 425 μm at 100% PM capacity at Qd. Washout
68
PSDs from SHS are illustrated on plot (F) in Figure 3-1 from 100 to 125% of Qd at 100% PM
capacity for the SM gradation in SHS. The PSD results from the SHS were in between those of
BHS and VHS, with an increasing sizes eluted at increasing flow rates. The minimum particle
diameter not washout from the SHS was 180 μm at 100% PM capacity at Qd. Therefore, the
washout particle gradations in VHS and SHS were coarser than that in the BHS.
PM Dynamics
The particle trajectories were calculated by a Lagrangian DPM for 3 different HS units
for separation and washout function, across a range inflow rates. Figure 3-3 and 3-4 compare
trajectories of treated and re-suspended discrete particles of selected diameters for three different
hydrodynamic separators: BHS, VHS, and SHS. For separation function, (A), (B) and (C) in
Figure 3-3 correspond to particle diameters of 25, 106, 300 μm, respectively. For washout
function in Figure 3-4, (A), (B) and (C) correspond to particle diameters of 10, 25, 75 μm,
respectively.
Figure 3-3 illustrates the dynamics of a specific PM size for separation function in HS
units. Three different PM diameters of 25, 106, and 300 μm are chosen for illustration. The
dependence of particle separation on particle size is clear. It can be observed for the coarse end
of the size spectrum that particles are influenced predominantly by gravitational forces from all
three HS units, whereas for the fine end of the size spectrum the suspended particle behavior is
largely a function of inertial hydrodynamic forces.
As illustrated in plot (A), and (B) of Figure 3-4, 10, and 25 μm, PMs (suspended and
settleable) are significantly washed out from the HS units. However, in plot (C) of Figure 3-4,
there is no movement of pre-deposited PM detected. The BHS showed significantly less PM re-
suspension than did the VHS or SHS. Compared to BHS and SHS, VHS has significant re-
69
suspension and washout, because VHS has about 2 times higher Qd than SHS, and 7 times higher
Qd than BHS.
Fluid Velocity Magnitude
In order to understand the hydrodynamics within the three HS units, CFD results were
examined in the form of graphical representations of fluid velocities in the HS by means of fluid
pathlines, and fluid velocity frequency distributions.
Figure 3-5 provides fluid velocity magnitude frequency distributions in the BHS, inner
swirl and outer chamber of the VHS, and the sump and volute chamber of SHS. As shown in
Figure 3-5, the BHS has the lowest fluid mean velocities. Figure 3-5 (A) depicts mean fluid
velocities in the BHS. As may be seen, low magnitude velocities necessary for quiescent
conditions and discrete settling dominate this distribution. Figure 3-5 can be utilized to determine
the possible separation mechanisms in the three HS units. Figure 3-5 (B) and (C) depict the
higher magnitude of velocities which result in the swirl chamber and outer chamber of the VHS,
and SHS, respectively. This is due to the presence of a swirling forced vortex in the chamber.
Separation within the swirl chamber of the VHS tends to rely upon inertial separation due to the
presence of vortics in a small swirl chamber with high SOR (128.0 to 1280.2 L/min-m2). It is
noted that while the swirl chamber diameter in the VHS is identical to that of the BHS, the Qd of
VHS is 2.7 times higher than that of BHS. The correspondingly higher SOR results in higher
washout rates in the VHS as compared to the BHS. The same comparison gives different results
when compared to the SHS, which has a similar range of SOR. The fluid velocity profile shows
that SHS has higher velocities in the unit. The inlet configuration and sedimentation chamber
configuration also affect the hydraulic behavior of the HS.
70
Probability of PM Separation and Washout
Modeled PM separation and washout probabilities as a function of PM diameter and flow
rate are illustrated in Figure 3-6 via 3-D graphs. Plots (A), (C) and (E) show separation function
for BHS, VHS, and SHS. BHS has the most efficient PM removal as a function of flow rates.
BHS separates PM diameters larger than 25 µm at 0.10 Qd, and larger than 150 µm at 1.25 Qd.
VHS separates PM diameter larger than 150 µm at 0.10 Qd, and larger than 500 µm at 1.00 Qd.
SHS separates PM diameter larger than 106 µm at 0.10 Qd, and larger than 250 µm at 1.25 Qd.
The washout function of modeled PM washout probabilities are shown in plots (B), (D)
and (F) in Figure 3-6. As shown in plot (B), BHS has significantly lower PM washout probability
compared to VHS, and SHS. PM diameters larger than 25 µm in BHS do not re-suspension at all
at the highest flow rate, 1.25 Qd. In contrast, PM diameters around 300 µm were re-suspended
from VHS at 1.00 Qd, and PM diameters of 250 µm were re-suspended from the SHS as well. A
gamma distribution function is used to model PM separation and washout.
)(
)(
)(
1
x
ex
xf (3-17)
In this expression, Γ is the gamma function; γ is the shape factor and β is the scaling
factor. f(x) is the cumulative gamma distribution. These parameters are shown in Figure 3-7 as a
function of flow rates for each eluted PSD. Conceptually, the shape factor may be thought of as
uniformity of the eluted PSD as compared to the heterogeneity of the influent PM and pre-
deposited PM gradation. The shape factor values are decreasing as the flow rate is increased
while the scaling factor values are increasing across the flow rates. It is observed that a more
hetero-disperse PSD is eluted from the VHS, with the effluent PM approaching to influent PM
and pre-deposited PM gradations as the flow rate increases.
71
Summary
This study examined the PM separation capacity of two types of HS units across a range
of influent loading conditions, with the SOR ranging from 47.3 to 590.9 L/min-m2 for BHS, and
from 52.3 to 653.5 L/min-m2 for SHS. Measured effluent PM removal ranged from 52.3 to
77.6%, for BHS, and SOR ranged from 47.3 to 590.9 L/min-m2. PM removal for SHS ranged
from 42.3 to 70.0% and SOR ranged from 52.3 to 653.5 L/min-m2. Results indicate that SOR has
a significant influence on PM removal in HS units. Comparing among effluent PSDs from the
three HS units, PSD from VHS is consistently coarser across flow rates than in the other two HS
units.
Secondly, physical modeling of washout from three types of HS units was performed
with same SM gradation of 100% pre-deposited PM. SOR ranges were same condition as
separation function. Washout rates ranged from 1.1 to 25.1 g/min from BHS; from 20.8 to 964.9
g/min from VHS; and from 2227.3 to 3046.0 g/min from SHS. BHS has significantly lower
washout rate than other two HS units. The interesting thing is that SHS has the highest washout
rate for the same SOR, which ranged from 128.0 to 590.9 L/min-m2. The BHS has significantly
lower washout rates than other two HS units. Even though SHS has lower Qd than VHS, the SHS
has a significantly higher washout rate. The reason that SHS has such a high washout rate is that
the geometry of the SHS directs, inflow from the screened area to the pre-deposited PM, causing
PM re-suspension.
Three different HS units are successfully modeled in terms of PM behavior with CFD,
using FVM, a standard k-ε model for turbulent conditions, and a Lagrangian DPM to track
particles. CFD models are validated for PM concentration, mass and PSDs with less than 10%
RPD. Lagrangian particle trajectory results show that VHS has coarsest eluted and washout PM,
as well as, the highest washout rates. The vortexing inner chamber results in a higher rate of re-
72
suspension of finer PM in the SHS. A CFD based probability function is developed for each HS
for particle elution as function of flow rate and diameter. Such probability functions, combined
with any available physical modeling data can provide a reliable method of predicting PM yield
from a HS, thereby reducing error in subsequent operations in treatment trains.
The volumetric efficiency of the three HS units is illustrated in Figure 3-8. It is noted that
the BHS behaves differently from the SHS and VHS. The primary hydraulically distinguishing
aspect of the BHS is the absence of a turbulent vortexing region. Volumetric efficiency, while
commonly used as an index to determine the deviation of a given flow regime from plug flow,
can misrepresent the dynamics of particles in a HS. Clearly, for a similar volumetric efficiency,
the VHS exhibits more washout than the SHS.
In consideration of these results, the use of CFD to quantify treatment and washout from
HS units is validated and holds great promise for future studies.
73
Table 3-1. Summary of measured and modeled separation and washout function result with RPD.
Type
of
HS
Surface
overflow
rate
(L/min-m2)
Separation
function
(Δ Mass - %)
RPD
(%)
Washout
function
(Δ Mass - g)
RPD
(%) Measured Modeled Measured Modeled
BHS
47.3 77.6 74.6 3.9 1.1 1.0 9.1
118.2 67.3 63.9 5.1 3.2 3.0 5.9
236.4 61.5 58.4 5.0 7.8 7.2 7.9
354.5 58.6 55.8 4.7 18.6 18.4 1.1
472.7 53.7 53.1 1.1 22.1 21.7 1.9
590.9 52.3 51.7 1.1 25.1 25.5 1.7
VHS
128.0 N/A 62.3 N/A 20.8 19.4 6.8
320.1 N/A 57.0 N/A 59.0 62.7 6.3
640.1 N/A 46.0 N/A 235.4 243.7 3.5
960.2 N/A 39.8 N/A 635.4 642.5 1.1
1280.2 N/A 42.8 N/A 964.9 977.0 1.2
SHS
52.3 70.0 69.9 0.2 N/A 263.3 N/A
130.7 62.1 65.8 5.9 N/A 556.4 N/A
261.4 52.4 56.7 8.3 N/A 1214.3 N/A
392.1 46.8 50.8 8.6 N/A 1596.8 N/A
522.8 43.9 46.0 4.8 2227.3 2408.3 8.1
653.5 42.3 42.0 0.7 3046.0 3299.9 8.3
74
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
PM diameter (m)
0.11101001000
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
Washout function
0.11101001000
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
PM diameter (m)
0.11101001000
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
Effluent Washout
Separation Washout
Separation Washout
BHS
Separation function
0.11101001000
Per
cent
finer
by m
ass
(%)
0
20
40
60
80
100
Influent
BHS
(Qd = 9.1 L/s)
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Separation
Q
BHS
(Qd = 9.1 L/s)
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Q
Influent
VHS
(Qd = 79.3 L/s)
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
Q
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
VHS
(Qd = 79.3 L/s)
Q
SHS
(Qd = 31.2 L/s)
SHS
(Qd = 31.2 L/s)
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Q
Influent
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Q
Pre-deposited
Pre-deposited
Pre-deposited
(a)
(c)
(e)
(b)
(d)
(f)
Figure 3-1. Modeled PSD plots of treated and washout PM from BHS, VHS, and SHS.
75
Surface overflow rate (L/min-m2)
0
200
400
600
800
1000
1200
1400
Sep
arat
ion f
unct
ion (
mas
s (%
))
40
50
60
70
80
90
100
BHS
VHS
SHS
Surface overflow rate (L/min-m2)
0
200
400
600
800
1000
1200
1400
Was
hout
rate
(g/m
in)
0
1000
2000
3000
4000
BHS
SHS
Modeled
Measured
BHS
VHS
SHS
BHS
VHS
SHS
Modeled
Measured
Figure 3-2. Plots of measured and modeled Δ mass and washout rate at constant SOR for BHS,
VHS, and SHS units.
76
Figure 3-3. Effluent PM trajectories inside the BHS, VHS, and SHS for particle with diameters
of 25 μm, 106 μm, and 300 μm, respectively. Particle density (ρp) is 2.65 g/cm3.
77
Figure 3-4. Washed out PM trajectories inside the BHS, VHS, and SHS for particle with
diameters of 10 μm, 25 μm, and 75 μm, respectively. Particle density (ρp) is 2.65
g/cm3.
78
Cu
mu
lati
ve
freq
uen
cy d
istr
ibu
tio
n (
%)
0
20
40
60
80
100
Fluid velocity magnitude (m/s)
0.001 0.01 0.1 1
Cu
mu
lati
ve
freq
uen
cy d
istr
ibu
tio
n (
%)
0
20
40
60
80
100
Fluid velocity magnitude (m/s)
0.001 0.01 0.1 1
Cu
mu
lati
ve
freq
uen
cy d
istr
ibu
tio
n (
%)
0
20
40
60
80
100
BHS
Qd = 79.29 L/s
Qd = 31.15 L/s
Qd = 9.05 L/s
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
VHS
SHS
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
0.10 Qd
0.25 Qd
0.50 Qd
0.75 Qd
1.00 Qd
1.25 Qd
Q
Q
Q
Figure 3-5. Fluid velocity magnitude as a function of flow rates in BHS, VHS, and SHS.
79
Figure 3-6. Probability of PM separation and washout by the BHS, VHS, and SHS.
80
Percent of Qd
0 20 40 60 80 100 120 140
0.7
0.8
0.9
1.0
1.1
1.2
1.3
BHS (Qd = 9.1 L/s)
VHS (Qd = 79.3 L/s)
SHS (Qd = 31.2 L/s)
Percent of Qd
0 20 40 60 80 100 120 140
0
20
40
60
80
100
120
Percent of Qd
0 20 40 60 80 100 120 140
0
1
2
3
4
BHS (Qd = 9.1 L/s)
VHS (Qd = 79.3 L/s)
SHS (Qd = 31.2 L/s)
Percent of Qd
0 20 40 60 80 100 120 140
0
10
20
30
40
SM ( = 0.56,
= 232.64)
Separation function Separation function
Washout function Washout function
SM ( = 0.56,
= 232.64)
Figure 3-7. Gamma parameters as a function of flow rates for BHS, VHS, and SHS (Qd for BHS
is 9.1 L/s, Qd for VHS is 79.3 L/s, and Qd for SHS is 31.2 L/s).
81
Percent of Qd
20 40 60 80 100 120 140
Volu
met
ric
effi
cien
cy (
%)
0
10
20
30
40
BHS (Qd = 9.1 L/s)
VHS (Qd = 79.3 L/s)
SHS (Qd = 31.2 L/s)
Figure 3-8. Volumetric efficiency (VE) as a function of flow rates in BHS, VHS, and SHS.
82
CHAPTER 4
STEPWISE STEADY CFD MODELING OF UNSTEADY FLOW AND PM LOADING TO
UNIT OPERATIONS
Overview
Rainfall-runoff transports nutrients, particulate matter (PM), and organic materials that
affect the water volume and quality of a water body (Pathapati and Sansalone 2009; Kim and
Sansalone 2008; Wang et al. 2003; Lee and Bang 2000). Designing treatment systems or unit
operations (UOP) in urban areas is further challenged by unsteady hydrologic loads, complexity
of PM and chemical constituents (Liu et al. 2008). Traditional treatment options such as
detention/retention basins are often difficult to implement in urban areas, due to lack of available
land area.
Typically, the performance of UOPs has been assessed by physical modeling. Wilson et
al. (2009) assessed PM removal in baffled hydrodynamic separator (BHS) by pilot-scale testing.
Hunt et al. (2008) tested bioretention pollutant removal and peak flow mitigation. Physical
modeling, while valuable for global performance assessment, is not easily amenable to
retrofitting and iterative design; physical modeling at various scales for UOPs is often limited by
available funds and infrastructure. In recent years, a coupled physical and numerical modeling
approach has proved effective in describing the separation mechanisms of UOPs. Computational
fluid dynamics (CFD) has been applied for both controlled steady flows and particle size
distributions and fully transient rainfall runoff events. Lee et al. (2010) physically tested a vortex
hydrodynamic separator (VHS) under steady flow conditions and applied CFD modeling to
predict PM removal for particle size and flow rates. Dickenson and Sansalone (2009) examined
PM discretization requirements with a CFD model for selected levels of granulometric size
hetero-dispersivity. Pathapati and Sansalone (2009) report that a CFD model was able to
accurately reproduce physical model data for PM separation for a screened HS for steady flow
83
rates. This was then extended to unsteady flows and PM loadings (Sansalone and Pathapati
2009), comparing storm event-based captured and effluent PM from a monitored empty bed-
SHS to unsteady CFD model predictions of PM fate. Garofalo and Sansalone (2011)
demonstrated that CFD model accuracy for simulating elution of hetero-disperse PM under
transient hydraulic loadings is dependent on time resolution of the flow field, spatial
discretization of the computational domain, and the PSD size discretization.
CFD can predict fluid flow, mass transfer, chemical reactions, and related phenomena by
solving governing fluid equations using numerical methods. In urban stormwater, CFD has
enhanced the modeling of PM separation for transient flows (Sansalone and Pathapati 2009;
Garofalo and Sansalone 2011) and heterodisperse particle size distributions (Dickenson and
Sansalone 2009) as well as re-entrainment of PM by washout mechanisms (Pathapati and
Sansalone 2012). However, a 3-D numerical model needs longer computational time to simulate
the transient hydrodynamics with variable PSDs than steady state flow (Pathapati and Sansalone
2011). For example, the unsteady computational time for the hydrodynamic separator (HS) is
approximately 3±0.5 days using a workstation equipped with dual quad-core 2.6 GHz processors
and 32 GB of random access memory (Pathapati and Sansalone 2011). To minimize
computational time, a stepwise-steady flow model is suggested as a possible solution. Stepwise-
steady CFD modeling is a series of discretized steady flow steps of rainfall-runoff events.
Identifying numbers of flow steps needed for economical computational time is crucial.
In this manuscript, the PM separation is characterized for stepwise-steady flow, for three
commonly used UOPs: BHS, SHS, and a volumetric clarifying filter (VCF) unit. The main
purpose of this study is to demonstrate modeling of unsteady flow utilizing a series of discretized
Stepwise-steady flow rainfall-runoff model.
84
Objectives
There primary objective are addressed in this manuscript. The first objective is to
characterize the PM separation by three HS units across 4 discrete storm events from an urban
impervious surface area. The second objective is to develop a CFD model for stepwise step flow
rates and influent PSDs for three UOPs. The third objective is to compare the results of the
numerical model to paired experimental results and to quantify the differences in the two
approaches.
Methodology
Watershed and Three Hydrodynamic Separator Configurations
The first area of interest is the University of Florida Reitz Union paved surface parking
facility catchment in Gainesville, FL. The schematic plan view of catchment with BHS is shown
in Figure 4-1(A). Considering the minimal slope values characterizing the monitored area,
particularly the paved surface parking facility area (approximate E-W 3% and N-S 1.5% slope),
and the contributing area has the potential to be influenced by rainfall intensity and wind
direction. Depending on the storm event the watershed area is approximately 500 m2.
The second area of interest is directly adjacent to Interstate-10 and City Park Lake in
urban Baton Rouge, Louisiana. Rainfall-runoff directed to SHS and VCF system. A schematic
plan view of the catchment with SHS and VCF is shown in Figure 4-1(A). The drainage system
was designed to intercept the lateral pavement sheet flow from the concrete-paved watershed.
The watershed area is approximately 1088 m2.
Isometric views of three different UOPs are shown in Figure 4-2: BHS, SHS, and VCF.
The design hydraulic operating volumetric flow rates (Qd) are 9.1 L/s for BHS, 15.9 L/s for SHS,
and 5.7 L/s for VCF based on the manufactures specifications. The BHS consists of a bypass
weir and drop tee for directing flow into the sedimentation chamber. The SHS consists of static
85
cross-flow cylindrical screen (2.4 mm screen opening diameter). The VCF consists of five
gravity-driven radial cartridge filters (RCF) in a vault. Each RCF contains approximately 49.8 kg
of mono-dispersed Al-Ox coated granular media (AOCM)p with a median diameter of 3.5 mm ±
0.8 mm and specific gravity of 2.35 ± 0.01. The total porosity of each RCF is 0.71 ± 0.041. The
five cartridges with filled (AOCM)P are housed in a 1.17 m by 2.12 m detention vault structure.
Detailed description of these three UOPs can be found elsewhere (Cho and Sansalone 2012;
Pathapati and Sansalone 2011; Sansalone et al. 2009). These UOPs operate primarily based on
gravitational PM separation, however, the SHS also performs size-separation screening and VCF
performs filtration through (AOCM)P cartridge.
Physical Modeling Methodology
Monitoring station design was driven by monitoring procedures related to the physical
processes investigated; in particular representative monitoring that requires manual sampling.
Rainfall-runoff PM is monitored across the inlet and outlet of the treatment unit during rainfall
events. Two different watershed areas were investigated: carpark loading in Gainesville, FL, and
highway loading in Baton Rouge, LA. Table 4-1 shows hydrologic indices across storm events
for BHS, SHS, and VCF. Detailed results regarding the watershed, hydrology, pollutant loads
and water chemistry for SHS and VCF are available elsewhere (Sansalone et al. 2009: Kim and
Sansalone 2008). Four events for the each UOPs were monitored and modeled. Table 4-1 and 4-
2 show the hydrologic indices, influent/effluent PM data and PSD data. 10 to 18 samples were
manually taken from influent and effluent drop boxes depending on duration and rainfall-
intensity of each event. The flow rate at the time of sampling, and throughout the storm duration,
was recorded automatically by the flow meter. The mean numbers of sample sets are 13, 13 and
11 for the VCF, PC and HS respectively.
After collecting all the rainfall-runoff samples from the events, the samples were taken
86
back to the laboratory for immediate analyses. The efficiency of the system was assessed using
laboratory analyses for suspended sediment concentration (SSC) (ASTM 1999), PSD, and mass
balance. PSD was determined using a laser diffraction particle analyzer (Malvern Instruments:
Hydro 2000G) in a batch mode analysis. SSC analysis was performed to quantify particle
concentration for each effluent sample collected from each run and calculate the effluent mass
load for each storm event. Laboratory analyses were conducted for the replicate influent and
effluent samples consisting of the individual replicate (A or B) samples. Analyses are conducted
within a few hours of the run completion to maintain the same conditions (temperature) and
minimize flocculation.
In order to define unsteadiness of storm events, a unsteadiness parameter ( ) is defined
in this study. The calculation of is as follows:
50
1
mediQdt
dV (4-1)
In this expression dV represents derivative of inflow volume [L3]; dt represents derivative
of time [T]; and Qmed represents median flow rate of storm event [L3/T].
CFD Modeling Methodology
CFD is a branch of fluid mechanics that uses numerical methods to integrate the Navier-
Stokes equations in order to solve fluid flow problems. Three types of full-scale UOPs are
modeled in 3-D using FLUENT v 6.0. A finite volume method (FVM) is applied to discretize the
governing equations into the physical space directly. Modeling in 3-D is less susceptible to the
complications which arise from the lack of geometric symmetry, complex static screen geometry,
vortex flow and gravitational forces on the motion of particles in UOP.
87
The governing equations are derived for incompressible flow. The conservation of mass
and momentum are determined using the RANS equations as following
0
z
w
y
v
x
u (4-2)
The momentum equations are as follows:
x momentum:
xg
z
u
y
u
x
u
x
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(4-3)
y momentum: ygz
v
y
v
x
v
y
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(4-4)
z momentum:
zg
z
w
y
w
x
w
z
p
z
uw
y
uv
x
uu
)()(
2
2
2
2
2
2
(4-5)
In these equation, is fluid density, u, v, and w are Reynolds averaged fluid velocities, g
is sum of body forces, and p is the Reynolds averaged pressure. The momentum continuity
equation for the x, y and z directions can be obtained by assigning values of u correspondingly,
where u is combining of the x, y, and z velocity vector component, since the hydrodynamics of
HS vary as a function of x, y, and z spatial coordinates. The 3-D Navier Stokes equations for a
Newtonian fluid are determined by 3-D velocity vector components.
A k-ε model is suited for clarification and re-suspension function (Morin et al 2008;
Liang et al 2005; Pathapati and Sansalone 2011). Two-equation Reynolds Averaged Navier-
Stokes (RANS) models are applied to swirling multiphase flows in the HS (Pathapati and
Sansalone 2009; Garofalo and Sansalone 2011). The standard k-ε model has been applied to
turbulent flow model in HS successfully (Pathapati and Sansalone 2009; Garofalo and Sansalone
2011).
88
Turbulence modeling is widely applied using the two-equation k-ε model. k and ε
equations allow one to determine the turbulent velocity and length scales independently. The
transport equations of the standard k-ε model are expressed by the following equations.
For k and ε:
k
jk
t
j
i
i
Gx
k
xku
xk
t)()(
(4-6)
kCG
kC
xxu
xtk
j
t
i
i
i
2
21)()(
(4-7)
In this expression, is the generation of due to the mean velocity gradients; ,
, are constants; , are turbulent Prandtl numbers for and . The values of C1ε, C2ε, C3ε,
σk and σε used in this model are 1.44, 1.92, 0.09, 1.0 and 1.3 respectively (Launder and Spalding
1974). Newton’s law of viscosity is applied to illustrate the relationship between viscous stresses
and “Reynolds stresses”. It should be noted that eddy viscosity (µ) is a non-physical quantity,
and is expressed by the following equation.
(4-8)
In this expression, is turbulent kinetic energy per unit mass, [L2T
-2] and is the rate of
dissipation of turbulent kinetic energy per unit mass, [L2T
-3].
Per standard application of k-ε model, μ is assumed to be isotropic. Information regarding
flow field and turbulent field, such as velocity profiles, kinetic energy, eddy energy and eddy
diffusivity, are generated from standard k-ε model equations. The standard k-ε model with
standard wall functions is an effective approach to modeling flow through the BHS.
kG k1C
2C
3C k k
2kf
k
89
Particulate Phase Modeling
The Euler-Lagrangian approach is applied to model the particle behavior in the UOPs.
This approach is valid for dilute multiphase flows when PM volume fraction is less than 10%
(Elgobashi 1991). A Lagrangian discrete particle model (DPM) is applied to track particles. Due
to the extremely dilute nature of the flow, the DPM assumes there are no particle-particle
interactions. Particle trajectories are calculated by integrating the particle force balance equation.
The Lagrangian DPM is derived from force balances based on Newton’s law describing particle
settling (Pathapati and Sansalone 2009).
p
p
pD
p guuF
dt
du
)()(
(4-9)
24
Re182
pD
pp
D
C
dF
(4-10)
pp
D
aaaC
2
321
ReRe (4-11)
uud pp
p
Re (4-12)
where, up is a particle velocity, u is fluid velocity; ρp is particle density, ρ is fluid density; dp is a
particle diameter; µ is viscosity; a1, a2, and a3 are empirical constants that apply to smooth
spherical particles as a function of the Reynolds number (Morsi and Alexander 1972); and Rep is
a particle Reynolds number.
The PSD is divided into 22 classes of particles based on a standard sieve. The particle
diameter is constant within same class. Particles are tracked for each steady flow rate. The
particles that become trapped in the HS are considered to have been removed through HS. The
particle removal efficiency is calculated by the following equation.
90
(4-13)
In this expression, is the number of particles that remain in the baffled HS, and is
the number of particles injected at the inlet.
Modeling of Static Screen and Cartridge
The static screen and cartridge filter are modeled as a porous perforated plate with the
addition of a momentum source term to the standard fluid flow equations (Pathapati and
Sansalone 2009). The source term is composed of two parts: a viscous loss term and an inertial
loss term. For simple homogeneous media and surficial filter, the sink term equation is as
follows:
imagiii vvCvS
2
12
(4-14)
In this expression, S i is the source term for the i th momentum equation, α is permeability,
C2i is the inertial resistance factor, v i is the velocity in the i th momentum equation, and vi is the
velocity in a computational cell. The momentum sink contributes to the pressure gradient in the
porous computational cell, creating a pressure drop that is proportional to the fluid velocity in the
cell.
Flow in porous media has traditionally been modeled analytically using comparisons to
pipe/conduit flow and specifying analogous parameters such as the hydraulic diameter and
roughness coefficient. Laminar flow (Re < 10) through porous media has been successfully
modeled by applying Darcian-type equations. Models such as Blake-Plummer and Carman-
Kozeny equations were developed to account for transitional flow regimes. These models were
extended by Ergun (1952) to account for turbulent flow. The Ergun equation for packed beds
applies to flow regimes from laminar to turbulent and is expressed by the following equation.
100*I
HS
N
Np
H SNIN
91
2
33
2
2
)1(75.1)1(150S
m
S
m ddL
p
(4-15)
In this expression, Δp is the pressure drop across the media, L is the length of the packed
bed, μ is the fluid viscosity, dm is media particle diameter, η is the total porosity of the packed
bed, v is the superficial velocity through the packed bed and ρ is fluid density.
The permeability (α) and the inertial resistance coefficient (C2) can be expressed as
follows.
2
32
)1(150
md
(4-16)
32
)1(5.3
mdC
(4-17)
CFD Parameters
CFD parameters for three UOPs are shown in Table 4-3. The volume fraction of
secondary phase (particle) is less than 0.1% in Table 4-3 and can be applied to a Lagrangian
particle tracking approach. Second order tetrahedrons element type is utilized to discretizing the
computational domain (Qi and Lin 2006). The standard k-ε model was applied in this study. A
secondary-order upwind scheme was utilized to solve for flow parameters (Barth and Jespersen
1989; Pathapati and Sansalone 2009). The operating pressure was 14.7 psi (atmospheric). DPM
particle density is 2.65 g/cm3, and particle diameters range from 1 micron to 9750 micron. DPM
boundary conditions include a ‘reflection” at the wall and an ‘escape’ at the effluent outlet. The
iterative convergence limit was set at 10-3
and applied to continuity, momentum, turbulent
kinetic energy, and dissipation rate (Ranade 2002).
Stepwise Step Modeling Removal and PM Separation
The number of stepwise-steady steps is determined based on creating a cumulative
92
distribution function (CDF) of flow rates entering the UOPs. The first step was to develop a CDF
for flow for each storm event. Following this step, the influent samples and effluent samples are
plotted on the CDF. This provides an idea of the number of discretization steps needed. The
number of stepwise-steady steps required to accurately represent the transient PM clarification
varied predominantly as a function of the fluctuation of influent flow rates – monitored by
correlating volumes, peak flow rates, elapsed time and sampling time.
Influent PSD was input into the model at the appropriate flow rate for the stepwise steady
model. The influent particles were tracked through the UOP after tracking for a given tracking
length. An unbiased tracking length was obtained by tracking massless ‘tracer’ particles through
the UOP and iteratively increasing tracking length until no more tracer particles would exit the
system. Typically, this was done with a ± 1% particle balance error.
Since rainfall-runoff flow is discretized with CDF throughout entire storm event duration,
PM separation can be calculated at the each stepwise steady flow rate. PM separation at the ith
stepwise-steady flow rate, ηPM(i) is calculated as follows.
100)( Inf
UOPiPM
N
N (4-18)
Ni is the number of particles injected in the influent and NUOP is the number of particles
that incomplete in the unit operation after the specified tracking length. Overall PM separation,
ΣηPM is calculated as follows.
InfiPM
nn
n
tq
q
PM M )(
)(
)1(
)(
)0(
(4-19)
In this expression, q(0) and q(t) are the influent flow rates, n(t1) and n(tn) are influent
samples at time t1 and tn respectively. MInf is the influent PM mass.
93
Result
Event Hydrology Indices
Event-based hydrological indices including PDH, drain, trunoff, as well as Vin and Qp and
Qmed,, were monitored and recorded for a total of 4 storm events for each UOPs as shown in
Table 4-1. Observed events varied in duration from 31 to 415 min, total rainfall-runoff volume
ranged from 594 to 48306 L, median flow rate ranged from 0.1 to 2.4 L/s, peak flow rate ranged
from 0.6 to 25.3 L/s, and rainfall depths ranged from 2.5 to 71.4 mm. The watersheds had the
dominant anthropogenic activity of traffic resulting from the highway and parking lot.
The values of for each hydrographs are summarized in Table 4-1. Unsteadiness
parameter equation was modified from Garofalo and Sansalone (2011). The normalized value of
time with respect to the peak time was not applied to this study due to the difference of peak time
among all storm events. Based on measured data of Pathapati and Sansalone (2009) as well as
Garofalo and Sansalone (2011), the of actual hydrographs from a watershed are typically
ranging from 0 to 6; considered quasi-steady, 6 to 14; considered unsteady, and 14 or greater;
considered highly unsteady.
The values of for each hydrographs are summarized in Table 4-1. The values of for
BHS ranged from 2.99 to 15.73. These values indicate that the storm events for BHS have from
quasi-steady to highly unsteady influent flows. The values of for SHS ranged from 1.42 to 8.24.
These values indicate that the storm events for SHS have from quasi-steady to unsteady influent
flows. The values of for VCF ranged from 3.59 to 17.46. The quasi-steady value ranged from
0 to 6 is comparable to 3.59 and 4.25 for the physical model hydraulic and PM loading (Garofalo
and Sansalone 2011). Also, unsteadiness of two storm events for VCF, which ranged from 11.89
to 17.46, are considered unsteady to highly unsteady.
94
)(
)(
)(
1
x
ex
xf (4-20)
In this expression, Γ is the gamma function; γ is the shape factor and β is the scaling
factor. γ and β parameters were estimated by minimizing the sum of squared errors (SSE),
resulting in maximizing the coefficient of determination between the measured and modeled data.
Below, F(x) is the cumulative gamma distribution and x represents flow rate in Table 4-1.
x
dxxfxF0
)()( (4-21)
Influent and effluent flow rates are heterodisperse throughout all storm events for the
three UOPs, BHS and SHS have same influent and effluent flow rates. Table 4-2 summarizes the
influent PM mass of each storm event, modeled effluent PM mass comparison with measured
effluent PM mass, the median influent and effluent PSDs across runoff events monitored for the
BHS, SHS, and VCF as a mass-based cumulative PSD. Cumulative PSDs were examined and
results were described by an optimized cumulative gamma distribution function summarized in
Table 4-2. The runoff median PM diameter (d50m) ranged from 72 to 182 μm for BHS, from 43 to
300 μm for SHS, from 15 to 99 μm for VCF.
Results indicated that BHS has same shape and scaling factor from both influent and
effluent. The influent and effluent flow rates are equal in the BHS since it does not have a screen
or cartridge in the unit. The drop tee section splits the inflow to the sediment chamber providing
dissipation of turbulence and flow velocity.
SHS consists of screen area with 2400 μm aperture size. The screen area did not result in
change in a difference in influent and effluent flow rates. The dominant mechanism of PM
separation of SHS is gravitational although the inertial separation occurs at low flow rates.
95
VCF consists of a sedimentation chamber with five RCF. The RCF results in dissipation
of turbulent energy and flow velocity (Pathapati 2009). The RCF generates 5 mm median head
loss (Sansalone et al. 2009) resulting in a lag between inflow and outflow from the VCF unit.
Gamma parameter values are different between inflow and outflow. The mechanisms of PM
separation for VCF are gravitational separation and filtration. Filtration with (AOACM)P
increases PM removal.
Probabilities of PM Separation by the BHS, SHS, and VCF
Modeled stepwise PM separation as a function of PM diameter and flow rate is illustrated
in Figure 4-4 via 3-D surface plots. The upper limit flow rate was chosen as the maximum
monitored flow rate among UOPs. The maximum flow rate was approximately 25 L/s from April
26, 2009 storm event. BHS and VCF separated all PM larger than 300 µm across the entire
range of flow rates up to 25 L/s. BHS displays a fairly consistent probability of PM separation
throughout the entire flow rate range (0 to 25 L/s). One advantage of the BHS The prevention of
separated PM from being re-suspended due to the internal bypass and drop tee configuration.
In contrast, the SHS tends to separate coarser particles (> 800 µm) across the entire range
of flow rates up to 25 L/s. At the highest flow rate (25 L/s), all three UOPs separated PM larger
than 2000 µm.
Table 4-2 summarizes PM separated by each UOPs modeled with a cumulative gamma
distribution function. In this section, the gamma probability density function represents PM
separation as a function of flow rate, Q, and particle diameter, symbolized as x in equation 4-17.
Γ is the gamma function; γ is the shape factor and β is the scaling factor. Also, F(x) is the
cumulative gamma distribution in the equation. These parameters are shown in Figure 4-3 as a
function of flow rates for each eluted PSD. Conceptually, the shape factor may be thought of as
uniformity of the eluted PSD as compared to the heterogeneity of the influent PM and pre-
96
deposited PM gradation. These parameter trends illustrate that BHS and VCF are able to separate
a large range of PM throughout entire flow rate ranges. In contrast, SHS depends on coarser PM
removed according to widely ranged γ.
Stepwise Steps Comparison to Measured Data
The stepwise steady flow CFD model was performed with each discretized steady flow
level. The discretized steady flow steps were determined using a cumulative density function
(CDF) across flow rates during the unsteady storm events, illustrated in Figure 4-4 to 4-6. The
CDFs show the frequency of flow rates entering the UOPs. Results of stepwise steady flow
modeling for the three UOPs are compared with results from experimentally validated CFD
models, as shown in shown in Figure 4-4 to 4-6. The CFD model results of PM mass are
compared to measured data throughout 4 storm events for the three UOPs in Table 4-2. There is
a significant difference in PM removal among the three UOPs, especially between SHS and the
two other UOPs (BHS and VCF). BHS has 64.7 to 95.1% PM removal; SHS has 38.3 to 62.0%
PM removal; and VCF has 84.0 to 97.1% PM removal. Results shows that PM removal is a
significantly less for the SHS, while VCF has the highest PM removal.
The absolute relative percent difference (RPD) is used to evaluate the CFD model results
with respect to the full-scale physical model. RPDs are shown in each Figure for the three UOPs.
Absolute RPD is calculated by the following equation.
100datameasured
data)modeleddata(measuredRPDabsolute
(4-22)
Results from the lowest flow rate to the highest flow rates indicate that the stepwise steady
CFD model predictions of PM removal reproduce the measured data with an absolute RPD less
than 10% across 12 total events. The separated PM mass and RPD between measured and
modeled PM separated in the three UOPs is provided for each event in Table 4-2.
97
Hydrographs are shown in Figure 4-4 to 4-6, and stepwise steady models to monitored
effluent PM mass cumulatively are shown utilizing rainfall-runoff PSDs. In additional analyses
the discretization steps for the stepwise steady flow model are further increased by utilizing flow
rates and event duration time between monitoring points of PSDs. The mean number of steps is
is 35 for BHS, 38 for SHS, and 38 VCF. According to numbers of stepwise steady steps, BHS
had mild storms than other two UOPs since the more steps needed depending on fluctuation of
runoff flow rates. The solid and dashed lines represent effluent PM mass predicted by the
stepwise steady and monitored PM mass, respectively. All three figures are representative of
monitored storm event PM loads. These results illustrate that the stepwise steady model for the
three UOPs is representative of monitored PM across each events.
The error at each step is calculated relative to the monitored PM separation for each UOP.
Figure 4-7 depicts overall stepwise steady CFD modeling error as a function of integrating over
an increasing number of stepwise steps predictions for PM separation. The RPDs for each UOP
has a steep decreasing trend in error, with increasing discretization of flow rates. Due to the
inherent effect of the filters on dissipating turbulent energy, the effect of changing flow rates on
the PM separation response is lower for the VCF compared to the BHS and SHS. This is
evidenced by the lower RPD for the VCF compared to the BHS and SHS. Stepwise-step CFD
modeling for three UOPs reproduce measured PM within 10% RPD ranges as well as
significantly lower computational time.
According to previous studies (Pathapati and Sansalone 2011), the computational times
of the unsteady flow CFD simulations for HS is a factor of approximately 16 times greater than
for the stepwise steady CFD model. Stepwise steady flow modeling was used to successfully
98
reproduce unsteady flow results and PM loadings in this study. Stepwise steady steps modeling
will therefore be utilized for unsteady HS modeling with less computational times in the future.
Summary
Unsteady flow events and PM separations with three UOPs in urban source area water
sheds were examined, providing variable volumetric control. In this study, four source area
rainfall-runoff events are monitored and treated real time by the three UOPs. The BHS unit is
installed in a carpark area in Gainesville, FL, and the other two UOPs are installed in a high way
area in Baton Rouge, LA. The main source constituent from both watershed areas is primarily
influenced by traffic.
The main parameters in the CFD model that can be "tuned" are standard Morsi and
Alexander drag coefficients and the wall functions in the k-epsilon turbulence model. Stepwise
steady modeling was able to successfully reproduce experimental data s utilizing standard values
for the drag coefficients and wall functions. No calibration or "tuning" was required.
The main purpose of this study was to model unsteady flow PM separation across a
rainfall-runoff event by utilizing CFD model predictions at discrete steady flow rates. Stepwise
steady CFD modeling was conducted without any tunable parameters. The only thing that can be
changed as CFD parameters was the standard Morsi and Alexander drag coefficient; however,
this drag coefficient was not tuned. The standard Morsi and Alexander drag coefficient was
given in Table SI-1 (Morsi and Alexander (1972).
Stepwise steady modeling showed that if PM separation data as a function of particle
diameter is available at steady flow rates, this can be integrated at a fine degree of discretization
to predict PM separation for unsteady rainfall-runoff events. Once a steady flow CFD database is
established across a wide range of flow rates, this can be used to predict fully transient rainfall-
99
runoff events. This results in faster modeling time for predicting unsteady behavior, compared to
transient modeling for an entire storm.
CFD modeling of PM separation is based on the assumption of discrete particle settling.
In order to check if this assumption holds true across different flow configurations, validation of
models with experimental data is advised. Physical modeling of UOPs with careful QA/QC is
required to be carried at at least representative flow rates, with the required PSDs.
This study accomplished that each stepwise steady flow results compared to monitored
data of PM separation with RPD less than 10% for three UOPs. Stepwise steady steps are
determined by based on CDF. The number of stepwise steps depends on volume, storm duration
time, and strength of storm events. Comparing PM mass from each three UOPs, the modeled
data consistently match with the monitored data although runoff flow rates are randomly
distributed through storm duration time. Results indicate that the stepwise steady flow model
effectively predicts the monitored storm event data in UOPs.
Fully unsteady CFD modeling can provide more accurate results than other modeling
results; however, a stepwise steady flow CFD model gives less than 10% error for monitored
unsteady storm events with more than 35 flow steps. All three devices show a steep decrease in
error with increasing steady steps in Figure 4-7. The error is hypothesized to theoretically reach
zero as the number of steady flow steps approaches infinity. Overall we see that the VCF has the
lowest range of error. In general, the effect of unsteady flow on the VCF is mitigated by the
presence of filters - which act like a resistance, and reduce the impact of smaller fluctuations in
flow rates. This study concludes that a stepwise steady approach is able to successfully model
PM separation, across 3 mechanistically different UOPs, and is validated with representative
monitored data. The stepwise-steady step model reproduced PM separation with significantly
100
more efficient computational time than a fully unsteady CFD model. This stepwise-steady CFD
modeling approach can reduce simulation time approximately 14-18 times when compared to a
fully transient analysis.
In consideration of these results, the use of stepwise steady CFD modeling to quantify
PM separation from UOPs is validated with unsteady hydrologic indices and infers economic
with reducing computational time as well as holding a great promise for future implication.
101
Table 4-1. Hydrologic indices across storm events for BHS, SHS, and VCF.
Type of
UO
Storm
event
Vin
(L)
PDH
(hr)
drain
(mm)
Qmed
(L/s)
Qpeak
(L/s)
trunoff
(min)
Gamma distribution
parameters
Influent
hydrograph
Effluent
hydrograph
γ β γ β
BH
S
(2010)
29 June 7504 192 20.6 1.5 17.6 15.73 40 0.74 3.26 0.74 3.26
11 July 2362 261 11.7 0.3 5.2 9.47 42 0.34 23.37 0.34 23.37
28 July 8316 36 19.1 1.2 16.0 7.87 50 0.36 7.82 0.36 7.82
14 August 594 28 2.5 0.3 2.5 2.99 31 0.61 0.32 0.61 0.32
SH
S
(2004 –
Ju
ne
30,
2005)
14 March 28464 204 26.2 0.7 6.4 2.66 415 0.74 1.46 0.74 1.46
20 August 17687 26 17.3 0.3 17.5 7.82 60 0.22 18.56 0.22 18.56
14 October 1672 84 2.6 0.1 0.6 1.42 201 0.40 0.41 0.40 0.41
30 June 5856 143 19.1 0.3 9.4 8.24 57 0.46 7.23 0.46 7.23
VC
F
(2006)
21 April 2927 927 4.1 0.1 13.4 17.46 49 0.12 3.16 0.87 0.26
29 April 48306 84 71.4 2.4 25.3 11.89 177 0.69 6.45 0.88 4.35
04 July 2779 352 3.8 0.2 6.2 3.59 68 0.31 2.40 0.46 1.42
05 July 3838 25 5.6 0.2 7.9 4.25 94 0.25 2.77 0.21 3.26
Vin, PDH, drain, Qmed, Qpeak, turnoff represent volume of rainfall-runoff, previous dry hours, event
duration, median flow rate, peak flow rate, rainfall-runoff duration, respectively.
102
Table 4-2. CFD model comparisons to measured data across storm events for BHS, SHS, and
VCF.
Type of
UO
Storm
event
Influent
PM
Effluent
PM d50m
Gamm distribution
parameters
Influent PM Effluent PM
Measured
(g)
Measured
(g)
Modeled
(g)
Influent
(μm)
Effluent
(μm) γ β γ β
BH
S
(2010) 29 June 6997.6 1285.1 1218.8 72 49 1.18 78.17 1.26 50.89
11 July 1001.6 195.4 202.2 113 36 0.90 196.99 1.13 45.17
28 July 2404.5 848.8 769.9 110 48 0.90 193.13 0.99 98.09
14 August 195.1 9.6 10.3 182 43 0.85 336.20 0.79 99.73
SH
S
(2004 –
Ju
ne
30,
2005)
14 March 4949.1 2539.3 2441.9 43 20 0.70 137.02 1.42 18.41
20 August 10591.0 4022.4 3774.7 300 52 0.44 2740.89 1.06 69.92
14 October 541.3 266.5 246.1 45 13 0.63 124.07 1.88 8.83
30 June 4043.8 2494.5 2328.8 69 39 0.73 212.51 1.28 39.94
VC
F
(2006) 21 April 4161.7 119.7 123.8 15 5 0.70 34.93 1.26 5.27
29 April 10466.2 1678.7 1622.6 99 24 0.48 452.09 0.65 71.68
04 July 831.8 100.8 91.6 26 21 0.67 63.14 1.23 27.34
05 July 1065.1 138.7 148.6 18 18 0.68 56.14 1.51 16.64
103
Table 4-3. CFD parameters for BHS, SHS, and VCF.
CFD parameters BHS SHS VCF
Mesh size of geometry 3.2e+06 2.1 e+06 5.2e+06
Element type Second order tetrahedrons
Turbulence model Standard -
Wall functions Realizable
Solution method SIMPLE, segregated
Momentum Second order upwind
Turbulent kinetic energy Second order upwind
Turbulent dissipation rate Second order upwind
Operating pressure (kPa) 101.35
Operating temperature (K) 288.16
Volumetric design flow rate (L/s) 9.1 15.9 5.7
Volume fraction of
secondary phase (%) 0.01-0.07 0.01-0.05 0.01-0.03
DPM particle density (g/cm3) 2.65
DPM particle diameter (µm) 1-9750
DPM drag coefficients Morsi and Alexander
DPM boundary conditions Walls – ‘reflect’ polynomial, Outlets – ‘escape’
Conver
-
gen
ce
lim
its
Continuity 10-3
Momentum 10-3
Turbulent kinetic energy 10-3
Dissipation rate 10-3
SIMPLE represents semi-implicit method for pressure linked equation. (Baffled hydrodynamic
separator – BHS, screened hydrodynamic separator – SHS, volumetric clarify filtration – VCF)
104
Figure 4-1. Plot A is a plan view schematic of a BHS testing watershed in Gainesville, FL. Plot
B is a plan view schematic of SHS and MFS testing watershed in Baton Rouge, LA.
105
Figure 4-2. Isometric views of the geometries of A) baffled hydrodynamic separator (BHS), B)
screened hydrodynamic separator (SHS), and C) volumetric clarifying filtration
system (VCF).
106
0.0
0.2
0.4
0.6
0.8
1.0
110
1001000
05
1015
2025
Pro
bab
ilit
y o
f P
M s
epar
atio
n
dp (m)Q (L
/s)
0.0
0.2
0.4
0.6
0.8
1.0
110
1001000
05
1015
2025
Pro
bab
ilit
y o
f P
M s
epar
atio
n
dp (m)Q (L
/s)
0.0
0.2
0.4
0.6
0.8
1.0
110
1001000
05
1015
2025
Pro
bab
ilit
y o
f P
M s
epar
atio
n
dp (m)Q (L
/s)
BHS
Qd = 9.1 L/s
SHS
Qd = 15.9 L/s
VCF
Qd = 5.7 L/s
VCF
Q (L/s)
0 5 10 15 20 25
1
10
100
0
50
100
150
200
250
300
1.67Q-0.05
= 0.87Q1.56
SHS
0 5 10 15 20 25
1
10
100
0
50
100
150
200
250
300
= 2.66Q-0.17
= 18.45Q0.83
BHS
0 5 10 15 20 25
Q (L/s)
0 5 10 15 20 25
1
10
100
0
50
100
150
200
250
300
= 5.63Q-0.26
= 0.26Q1.91
Figure 4-3. Probability of PM separation by the baffled hydrodynamic separator (BHS), screened
hydrodynamic separator (SHS), and volumetric clarify filtration (VCF). γ and β
represent shape factor and scaling factor.
107
Elapsed Time (min)
0 10 20 30 40F
low
rat
es (
L/s
)
0
4
8
12
16
20
Eff
luen
t P
M m
ass
(g)
0
400
800
1200
1600
2000Runoff
Modeled
Measured
= 15.73
Vin = 5704 L
Qmed = 1.5 L/s
Qpeak = 17.6 L/s
RPD = 5.4%
29 June 2010
Flow Rate (L/s)
0 3 6 9 12 15
CD
F (
%)
0
20
40
60
80
100
Runoff
Influent Sample
(n = 10)
Effluent Sample
(n = 10)
BHS
(Qd = 9.05 L/s)
Figure 4-4. Plot A) is a cumulative distribution function (CDF) for the range of rainfall-runoff
flow rate (L/s) in baffled hydrodynamic separator (BHS). Plot B) is flow rates (L/s)
and effluent PM mass (g) as a function of elapsed time in BHS (Number of flow steps
= 37). , Vin, Qmed, Qpeak, and RPD represent unsteadiness parameter, median flow
rate, peak flow rate, and relative percentage difference.
108
Elapsed Time (min)
0 10 20 30 40 50 60F
low
rat
es (
L/s
)
0
4
8
12
16
20
Eff
luen
t P
M m
ass
(g)
0
1000
2000
3000
4000
5000
Runoff
Modeled
Measured = 7.82V
in = 17687 L
Qmed
= 0.3 L/s
Qpeak
= 17.5 L/s
RPD = 6.2%
20 August 2004
Flow Rate (L/s)
0 3 6 9 12 15 18
CD
F (
%)
0
20
40
60
80
100
Runoff
Influent Sample
(n = 15)
Effluent Sample
(n = 14)
SHS
(Qd = 15.9 L/s)
Figure 4-5. Plot A) is a cumulative distribution function (CDF) for the range of rainfall-runoff
flow rate (L/s) in screened hydrodynamic separator (SHS). Plot B) is flow rates (L/s)
and effluent PM mass (g) as a function of elapsed time in SHS (Number of flow steps
= 40). , Vin, Qmed, Qpeak, and RPD represent unsteadiness parameter, median flow
rate, peak flow rate, and relative percentage difference.
109
Elapsed Time (min)
0 30 60 90 120 150 180F
low
rat
es (
L/s
)
0
6
12
18
24
30
Eff
luen
t P
M m
ass
(g)
0
400
800
1200
1600
2000Runoff
Modeled
Measured
29 April 2006
Flow Rate (L/s)
0 5 10 15 20 25
CD
F (
%)
0
20
40
60
80
100
runoff
Influent Sample
(n = 18)
Effluent Sample
(n = 18)
VCF
(Qd = 5.7 L/s)
= 11.89
Vin
= 48306 L
Qmed
= 2.4 L/s
Qpeak
= 25.3 L/s
RPD = 5.7%
Figure 4-6. Plot A) is a cumulative distribution function (CDF) for the range of rainfall-runoff
flow rate (L/s) in volumetric clarify filtration (VCF). Plot B) is flow rates (L/s) and
effluent PM mass (g) as a function of elapsed time in VCF (Number of flow steps =
49). , Vin, Qmed, Qpeak, and RPD represent unsteadiness parameter, median flow rate,
peak flow rate, and relative percentage difference.
110
(A) BHS
Number of steady flow steps (#)
0 10 20 30 40 50
Abso
lute
RP
D (
%)
10
100
Mean of error
(B) SHS
Number of steady flow steps (#)
0 10 20 30 40 50
Abso
lute
RP
D (
%)
10
100
Mean of error
(C) VCF
Number of steady flow steps (#)
0 10 20 30 40 50
Abso
lute
RP
D (
%)
10
100
Mean of error
BHS
Abso
lute
RP
D (
%)
10
100
SHS VCF
(D) Variation of absolute RPD
Figure 4-7. Mean and variation of the stepwise steady model absolute relative percentage
difference (RPD) with increasing number of monitoring points for BHS, SHS, and
VCF. The lower right quartile box plot is the variation of absolute RPDs across the
number of steps.
111
CHAPTER 5
REMOVAL AND PARTITIONING OF NITROGEN AND PHOSPHORUS OF NUTRIENTS
IN HYDRODYNAMIC SEPARATOR ON URBAN RAINFALL-RUNOFF PARTICULATE
MATTER GENERATED FROM IMPERVIOUS SURFACE CARPARK
Overview
Stormwater runoff can be a significant source of Nitrogen (N) and Phosphorus (P)
leading to eutrophication (Field and Sullivan, 2002; Heaney et al., 1999; Lee and Bang, 2000;
Novotny and Witte, 1997). Sources of N (P) include non-point sources in non-urban areas such
as biogenic materials and fertilizers (Turner et al., 1999; Zhang and Jørgense 2004) as well as
urban pavements (Wang et al. 2003). Partitioning controls the transport and cyclic dynamics of P
in land/water ecosystems and is key to understanding impacts on aquatic ecosystems (Brezonik
and Stadelmann 2002). The export of N in stormwater runoff poses several threats to
environmental and human health and consumes a large share of public resources (Parker et
al.2000, NRC 2001). Nitrogen loading is primarily a function of processes that affect
concentrations rather than of the geometry of the catchment which conveys precipitation into
runoff (Lewis and Grimm 2007).
In addition to the partitioning of total nitrogen (TN) and total phosphorus (TP) into the
dissolved phase, N (P) distributes across the PM size spectrum ranging from less than 1 μm to
greater than 4,750 μm in rainfall-runoff. Partitioning, mobility and fate of P are dependent on
PM size ranges (Shinya et al. 2003; Zhou et al. 2005; Berretta and Sansalone 2011). Several N
and P treatment methods were investigated by previous studies, such as infiltration and detention
basins (Bartone and Uchrin 1999; Dechesne et al. 2005); constructed wetland systems (Gervin
and Brix 2001; Seo et al. 2005); sedimentation tank systems (Stenstrom et al. 2002); vegetative
controls (Barrett et al. 1998); filtration with floating media filters (Visvanathan et al. 1996); and
urban wet detention ponds (Wu et al. 1996; Comings et al. 2000; Wang et al. 2004).
112
Control of PM is challenging in an urban environment, due to complex hydrology, and
constraints of land availability and infrastructure. Many of treatment methods are based on
volumetric control and therefore may not be applicable in situations where land availability is
limited. In such situations, hydrodynamic separators (HS) are commonly used to treat runoff
(Kim and Sansalone 2008). However, while HSs can be effective in removing particulate bound
N (P), they face the problems of re-uptake of N (P) from captured PM deposited during previous
storm events. Additionally, because HS are not designed to capture the entire runoff volume of a
storm, determining efficiency is dependent on understanding the transport and partitioning of N
(P) during an individual storm event. Understanding efficiency and P and N distributions as a
function of particle size is necessary for appropriate sizing, retrofitting and optimizing of HS
type units.
In this study, separation and partitioning of particulate matter (PM) bound nitrogen (N)
and phosphorus (P) in a baffled hydrodynamic separator (BHS) located in an urban car park with
an impervious surface are investigated during 10 storm events. The results are examined as a
function of hydrology. A wide gradation of captured PM from BHS was analyzed after 10 storm
events were completed.
Objectives
The first objective is to determine the distribution of N and P across suspended, settleable
and sediment fractions in rainfall-runoff for 10 discrete storm events from an urban carpark with
an impervious surface. The second objective is to examine equilibrium partitioning of N and P
between dissolved and particulate phases in rainfall-runoff. The last objective is to investigate
the N and P separation in BHS as a function of particle size distribution (PSD) and treated
volume.
113
Methodology
Catchment
The experimental site is illustrated in Figure 5-1. The site is located at an urban
impervious surface area (Parking lot in University of Florida). Depending on the intensity of the
storm event, the catchment area ranges around ~500 m2. The catchment area slope is
approximately 3% E-W and 1.5% N-S. There are vegetated islands between parking spaces in
the watershed which can contribute biogenic materials to impervious surface area. The overall
experimental setup consists of the following components: a system pipe to deliver the rainfall-
runoff from the catchment to a BHS (10.1 m), a Parshall flume equipped with an ultrasonic
sensor for measuring flow rates, a drop box for influent sampling, and a gate valve to divert flow
to BHS. This make it possible to monitor the performance of each device for a given storm or
series of storms. The installation is shown in Figure 5-1.
Data Acquisition, Management, and Sampling
The rainfall depth was collected by tipping bucket rain gauges manufactured by Texas
Electronics Inc. (0.254 mm bucket capacity). Flow measurement from the watershed was
monitored with 25.4 mm Parshall flume. A 30 kHz ultrasonic sensor (model Shuttle Level
Transmitter, MJK Inc.) connected to a Campbell-Scientific CR 1000 (Campbell Scientific, Inc.)
is used for flow depth monitoring in the Parshall flume and for real-time data logging.
Both influent and effluent samples from each event were sealed within five separate
Nalgene polypropylene screw closure bottles. Three of the bottles were 1-L bottles and were
used for the particle size distribution (PSD) analysis, PM separation and analysis, nutrient (N and
P) analysis and metal analysis. The other two bottles were 0.5-L and were both used for the
probe analysis and some of the PM analysis. All of the bottles were taken in succession of one
another and thus considered identical in composition. Influent samples were taken manually by
114
the grab method following the parshall flume and before the runoff reached the dropbox.
Effluent samples were also taken manually by the grab method immediately following the
effluent pipe located on the side of the BHS unit.
PM Separation
The 1.1-L samples were used to recover a sizeable quantity of sediment particles by
passing the stormwater through the 75 μm (No. 200) sieve. All the sediment particles recovered
on the sieve for each sample were carefully collected using a spatula and a stainless-steel pick
and then placed in a clean, labeled and tared Petri dish. The 1.0-L samples are also used for
turbidity analysis and then screened to separate the sediment size particles by using the 75 μm
(No. 200) sieve. The actual volume of the nominal 1-L runoff sample was first measured (since
approximately 100-mL were utilized) using a graduated cylinder. The 1.0 L of solution that was
passed through the sieve was recovered and placed into an Imhoff Cone without the loss of any
additional PM. Each Imhoff Cone with a nominal 1-L of stormwater was set aside for a quiescent
settling of 60 minutes. The settleable PM settled to the bottom of the Imhoff cone and these
particles were carefully recovered from the Imhoff Cones by slowly decanting the supernatant
from the cone and obtaining the particles from the bottom of the cone in clean, labeled and tared
Petri dishes. Replicate samples were obtained for each sample; hence all particle fractions were
recovered from the replicate (A and B) runoff samples. Part of the supernatant recovered from
each Imhoff Cone (almost 50 mL) was then passed through a fractionation column using an air
pump. The filtrate was directly used to determine concentrations of Ions (50 mL) like Nitrate
(NO3-), Phosphate (PO4
3-) and dissolved COD. All these constituents were measured by
spectrophotometer (Hach - DR/5000).
The remaining supernatant for each sample replicate was then used to determine the
suspended PM fraction. 100 mL from the well-mixed supernatant was used to separate the
115
suspended PM through a vacuum pump and membrane filters. The 0.45 μm membrane filters
were then dried in the oven at 105 °C. The filters were prepared in advance and tare-weighed
before use. The difference in weight gives the mass of the PM. A sub-sample of the supernatant
was used to calculate the suspended particulate-bound total nitrogen (TN) (Suspended-TN). As
for the measurement of metals, the Inductively Coupled Plasma - Mass Spectrometry (ICP-MS)
was used.
Water Chemistry Analysis
Water quality parameters, such as pH, salinity, dissolved oxygen (D.O), redox,
conductivity, total dissolved solids (TDS), and alkalinity were measured on rainfall-runoff
samples within 24 hrs. Alkalinity was measured in triplicate using Method 2320B (APHA 1998).
Conductivity and TDS were measured with a conductivity meter in replicate. The probes are
standardized with a 3-point curve prior to analyses. A pH meter with a 3-point calibration was
used to measure the pH of the samples in replicate. The samples were filtered through a pre-
weighed 1.2 µm glass-fiber filter and dried to determine the suspended solid concentration
(SSC).
Nitrogen and Phosphorus Analysis
Samples were fractionated into dissolved and particulate phases. The particulate bound
phosphorus and nitrogen analyses are performed using the method from Standard Methods for
Water and Wastewater (APHA et al. 1998). After acid digestion of particles and the filtrate, the
concentration values of total dissolved and particulate bound phosphorus and nitrogen were
obtained by Cadmium Reduction Method # 8039. To determine the concentration of nitrate and
phosphate, a spectrophotometer (Hach - DR/5000) is used.
The quantity of suspended PM bound N is obtained by subtracting the total dissolved
nitrogen (TDN) fraction from the supernatant N fraction. In particular a sample from the
116
supernatant obtained after the removal of the sediment and settleable particles from the 1.0 L
sample solutions is acid-digested. Similarly a sample from the filtrate is also acid-digested as
mentioned. The TN from both the supernatant and filtrate samples is obtained and the difference
of these values gives the TN bound to suspended particles.
Partitioning Indices for Nitrogen and Phosphorus
The TN and Total Phosphorus (TP) are the sums of the dissolved fraction concentration
and particulate-bound fraction concentration of N (P), respectively. Therefore TN and TP can be
expressed with dissolved and particulate-bound fraction, as in the following equations:
pd
d
pd
dd
MM
M
CC
Cf
(5-1)
pd
p
pd
p
pMM
M
CC
Cf
(5-2)
In this expression, Md is dissolved mass; and Mp is the particulate-bound mass. If fd > 0.5, N or P
is mainly in dissolved form, otherwise N (P) is predominantly in particulate-bound form
(Sansalone and Buchberger 1997).
The partitioning coefficients, Kd, is defined as the ratio of the equilibrium concentration
of a dissolved fraction mass with respect to particulate-bound fraction mass. The equation is as
follows.
d
Sd
C
CK (5-3)
In this expression Kd is the equilibrium partitioning coefficient between particulate bound mass
and dissolved mass (L/Kg) while Cs is the particulate bound N (P) mass (mg/g of dry particulate
mass). The partitioning coefficient can be used to evaluate the distribution between dissolved and
particulate bound N (P).
117
Hydrologic and Loading Parameters
Hydrologic and transport parameters were measured for each rainfall event, and are
shown in Table 5-1. The parameters include previous dry hours (PDH), event duration (train),
rainfall depth (drain), maximum rainfall intensity (irain-max), initial pavement residence time
(IPRT), runoff volume (Vrunoff), maximum flow rate (Qp), median flow rate (Qmed), runoff
coefficient (C), and number of samples (n).
Analysis of Recovered Sediment Deposit from Hydrodynamic Separator
Sieve analysis is used to determine the particle size distribution as required or gradation
of an aggregate. Analysis followed ASTM D422-63 with additional sieve sizes (ASTM 1993;
Sansalone et al. 1998). After being air-dried at a constant temperature of 40°C and having their
dry proper weights measured PM, dried PM samples are disaggregated and sieved through a set
of graded mechanic sieves. The aggregates are placed in the top of the sieve stack and covered
with a lid. The sieves are properly secured in the mechanical shaker and then the shaker is
turned on for five minutes. The materials retained on each of the sieves are weighed, including
the weight retained on the pan, and the results are recorded. The Fineness Modulus for each PM
Sample is then computed. Sieve analysis follows the standard procedure ASTM D422 (ASTM
1998). Dry PM separated on each of the stainless steel sieves is weighed and stored separately in
round clear sample bottles. A 95 to 98% recovery of PM is required for sieve analysis.
Results
Event Hydrology
Event-based hydrological indices including PDH, drain, irain-max, as well as Vrunoff and Qp
and Qmed, (both influent and effluent), were monitored and recorded for a total of 10 storm events
occurring between May 24th
, 2010 and August 21th
, 2010 as shown in Table 5-1. Monitored
storm events during the field test program varied in duration from 25 minutes on August 24th
,
118
2010 to 60 minutes on July 31st, 2010. The storm events ranged from 1.8 mm on May 24
th, 2010
to 23.6 mm of rainfall depth on July 31st, 2010. The PDH was from 15 hours to 261 hours in
between storm events. The volume of event runoff was from 79 L to 2386 L. Resulting peak
flow rates ranged from 1.2 L/s on May 24th
, 2010 to 17.6 L/s on June 29th
, 2010, while the
median flow rates ranged from 0.03 to 1.52 L/s as shown in Table 5-1. The IPRT referred to in
Table 5-1 is the time required for rainfall to satisfy certain conditions including pavement surface
wetting and depression storage filling, as well as airborne re-suspension, and atmospheric
evaporation. In comparing all 10 storm events, IPRT results varied from 0.4 minutes to 7.3
minutes as shown in Table 5-1. In addition, IPRT is controlled by a combination of PDH and
abstractions during each storm (Sansalone et al. 2005). The site hydrology is described for each
of the 10 events by runoff hydrographs plotted with fd and fp in Figures 5-6 to 5-7. The
hydrographs were observed to respond quickly to fluctuations in rainfall intensity.
Overall Treatment Efficiency of BHS as a Function of Hydrology
A total of ten storm events, encompassing a wide range of flow rates, were routed
through the BHS between May 24th
, 2010 and August 21st, 2010 and with total volume of
approximately 32407 L of rainfall-runoff from the experimental watershed located in the
University of Florida. Each of the storms was unique in regards to their natural and
anthropogenic pollutant loadings. All ten storms had wide variations of drain (1.8 mm – 23.6
mm), period of PDH (0.4 min. – 7.3 min.), Vrunoff (871 L – 9031 L), Qp (1.2 L/s – 16.0 L/s), irain-
max (7.6 mm/hr – 137.2 mm/hr), and train (25 min. – 60 min.). These results are summarized in
Table 5-1, along with PDH, train, drain, irain-max, IPRT, Vrunoff, Qp, Qmed, number of influent/effluent
samples (ninf/neff), sampling coverage, and percent of hydraulic design utilized at Qp. Removal
efficiency for the 9 TARP qualified storm events ranged from 47% to 98%. The most significant
factor in determining the amount captured by the BHS unit was the peak flow intensity of
119
influent heading into the unit, which has a hydraulic design capacity (Qd) of 9.05 L/s. In each
high intensity storm, the level of total suspended solid (TSS), SSC, TN and TP removal dropped
significantly, resulting in a range of -46% to 98% removal. The negative values are shown
because of re-suspension in the unit. Results for PM separation, and TN, TP and SSC removal
efficiencies measured for the BHS unit with ten different runoff events are summarized in from
Table 5-1.
PM fraction and PM-based N and P fraction masses distribution
A summary of the relative fractions of the suspended, settleable, and sediment PM for
each event are shown in Figure 5-2. Sediment PM (>75 μm) gravimetrically dominates these
results in rainfall-runoff, ranging from 76.0 to 99.5% with a mean of 89.0%. The settleable PM
ranges from 0.03 to 12.9% with a mean of 6.3%, while the suspended PM fraction ranged from
0.02 to 17.7% with a mean of 4.7% in rainfall-runoff.
The fractions of N (P) in rainfall-runoff are compared to the fraction of PM mass. The
results are plotted in Figure 5-2 on an event basis for each PM fraction (suspended, settleable,
sediment, TSS and SSC). A 1:1 line of equal separation behavior is illustrated as a reference.
Results indicate that there is less than a 1:1 ratio between N (P) associated with settleable PM
and suspended PM with a few events deviating from this trend. However, N (P) fractions for
sediment PM have a larger than 1:1 ratio.
Event based Nitrogen and Phosphorus Loadings
Each storm event was analyzed for N (P) loadings across PM fractions. The
concentrations of total dissolved and total N (P) are presented in Table 5-2. The concentrations
of N (P) varied across 10 storm events. TDN of rainfall-runoff ranged from 240 μg/L to 2104
μg/L with a median value of 1321 μg/L across the 10 storm events while, effluent TDN ranged
from 177 μg/L to 1901 μg/L with a median value of 1072 μg/L. TN of rainfall-runoff ranged
120
from 668 μg/L to 7542 μg/L with a median value of 2709 μg/L, while effluent TN ranged from
528 μg/L to 2383 μg/L with a median value of 1581 μg/L. The median value of TN from this
experiment was higher than that found in previous studies within the local watershed (Rushton
2001; Passeport and Hunt 2009). For instance, TN loadings in the urban areas were previously
found to be 1.63, 0.556, and 0.548 mg/L (Rushton 2001; Passeport and Hunt 2009). TN
concentrations were higher than in previous studies because this research was performed during
the summer, during which time more biogenic materials from vegetated islands in the watershed
area are generated.
TDP of rainfall-runoff ranged from 123 μg/L to 854 μg/L with a median value of 303
μg/L across the 10 storm events, while effluent TDP ranged from 77 μg/L to 469 μg/L with a
median value of 257 μg/L. TP of rainfall-runoff ranged from 668 μg/L to 23640 μg/L with a
median value of 2459 μg/L, while effluent TP ranged from 527 μg/L to 1807 μg/L with a median
value of 1046 μg/L. From the previous research (Rushton 2001; Kayhanian et al. 2007; USEPA
1983; Brown et al. 2003), TP loadings in urban areas were found to be 0.21, 0.29, 0.33 and 0.30
mg/L. This specific carpark transports higher magnitudes of event mean concentration (EMC)
than observed in previous research. Approximately 700 vehicles per day pass by this watershed
area (Berretta and Sansalone 2011) which also has a significant load of biogenic materials from
vegetated areas in the parking lot. Leaves falling from trees and grass cuttings, also contribute to
the biogenic loads on the pavement and these are eventually conveyed to the BHS unit with the
runoff.
Nutrients Removal Efficiency as a function of Hydrology
Based upon the measurements described above, frequency distributions of N (P) fractions
were examined for both influent and effluent. The measured N (P) of total dissolved, suspended,
121
settleable, sediment, and total fraction concentrations are presented in Table 5-2 as EMC values.
Median, mean and standard deviation values are also presented.
The percentage of TP separation varied from 42% to 97% for storms that had a peak flow
below the design flow capacity of the unit and not including the first storm which had no
effluent. As a comparison, the percentage of TP separation in storms that had a peak flow above
the design flow capacity of the unit varied from 22% to 60%. Similar results were observed for
TN. The percentage of TN separation in storms that had a peak flow below the design flow
capacity of the unit and not including the first storm varied from 12% to 84%. In comparison, the
percentage of TN separation in storms that had a peak flow above the design flow capacity of the
unit varied from 7% to 59%. It is clear that the unit does perform better, when the peak flow does
not exceed the hydraulic design capacity.
The BHS unit separation behavior for PM-bound N (P) fractions is compared to
separation of PM in Figure 5-3 on an event basis for each PM fraction (suspended, settleable, and
sediment). A 1:1 line of equal separation behavior is presented as a reference. Results indicate
there is less than a 1:1 relationship between N (P) separation associated with settleable PM,
sediment PM and smaller than 25 μm PM, with a few events deviating from this trend. All
frequency distributions for N (P) are well described by a log-normal distribution (α ≤ p = 0.05).
The medians of each frequency distribution for N (P) were significantly reduced between
influent and effluent as illustrated in Figure 5-4 and 5-5.
Nutrients Partitioning
In rainfall-runoff, nutrients such as N (P) are partitioned into dissolved and particulate
bound fractions. The partitioning of N (P) in urban rainfall-runoff influenced by primary IPRT,
rainfall pH, oxidation reduction potential (ORP), conductivity and the quantity of solids present
in rainfall-runoff (Sansalone et al., 1997). The dissolved fraction (fd) and equilibrium partitioning
122
distribution (as Kd, L/Kg) between dissolved and particulate N (P) phases were examined in both
the influent and effluent. Figure 5-6 summarizes the trends in fd of TN and TP and the magnitude
of TDN and TDP concentrations as a function of hydrology for all events. The trend of variation
in fd does not correspond to each event’s hydrology. The traffic loadings and biogenic nutrients
from the catchment area are the main factor causing the variation of fd through all the events. The
role of suspended, settleable, and sediment PM based N (P) were examined with respect to fd and
Kd. Table 5-3 shows that event mean fd values for N ranged from 0.54 to 0.85, indicating that the
majority of N was associated with the dissolved gradation in the rainfall-runoff. Event mean fd
values for P ranged from 0.08 to 0.42 indicating that the majority of P was associated with the
particulate gradation in the rainfall-runoff. Results are illustrated in Figure 5-6, note that all
frequency distribution are modeled as log-normal (α ≤ p = 0.05). The SSC method provides a
gravimetric index based on all PM in the sample. Partitioning was examined based on suspended,
settleable, and sediment PM fractions of N (P). Initial TDP concentrations are greater than
concentrations at the end of event. The results indicated that the BHS unit was effective in
separation of the suspended, settleable, and sediment PM fractions of N (P). The initial TDP has
higher concentrations as compared to the end of the event. The results showed that the effluent
from the unit has a statistically significant increase in the dissolved fraction. Additionally, fd
varied by over an order of magnitude between events (which is potentially related to previous
dry hours).
Kd values in Table 5-3 are also shown in order of decreasing median value. P exhibits
much higher Kd values than N. Kd values ranged from 180.5 L/Kg to 17649.7 L/Kg for N, while
values ranged from 8408.2 L/Kg to 37625.2 L/Kg for P. Kd values are statistically significantly
higher for effluent as compared to influent for all the PM fractions for N (P).
123
Nutrient from the Recovered Sediment Deposit
Figure 5-7 illustrates the distribution of dry granulometric mass for a given particle
diameter on an incremental and cumulative basis for each PM fraction from less than 25 µm to
coarser than 9500 µm. This analysis was performed to evaluate the gradation of N (P) mass as a
function of particle diameter. Figure 5-7 illustrates a general trend for different N (P) species for
NO3-N, NH3-N, total kjeldahl nitrogen (TKN), PO43-
-P, and TP, nutrients mass is predominantly
associated with the coarse fraction of PM (coarser than 63 µm). The cumulative NO3-N, NH3-N,
and TKN recovered mass was 32 mg, 679 mg, and 45086 mg respectively. The cumulative PO43-
-P, and TP recovered mass was 1886 mg, and 24695 mg. The mass recovery for each of NO3-N,
NH3-N, and TKN significantly increased for particle sizes greater than 75 µm. The percentage of
NO3-N, NH3-N, and TKN between 75 µm and 2000 µm was 82%, 88%, and 85%, respectively.
For PO43-
-P, and TP, the percentages between 75 µm and 2000 µm were 92% and 93%. Most of
the nutrients in recovered PM deposits are from the sediment PM fraction. According to data
from recovered deposited mass, NH3-N is a less abundant species of dissolved nitrogen in runoff;
the ratio of NO3-N to NH3-N was about 22:1, which means nitrite concentration can be
neglected. NH3-N includes both ammonium and ammonia in the equilibrium aqueous phase.
Based on the neutral pH of 7 for rainfall-runoff in catchment, the major form of NH3-N is
expected to be ammonium (NH4+).
Figure 5-8 summarized the trend in the fd of TN (TP) as a function of cumulative treated
rainfall-runoff volume. The variation trend of the fd does not follow the changing of treated
volume exactly due to the hydrologic complexity. The mean fd values of influent and effluent TN
are 0.57 and 0.68, and the mean fd values of influent and effluent TP are 0.28 and 0.36. The
mean fd values of influent and effluent TP are significantly lower than TN. This result indicates
that the predominance of TP is associated with the PMs.
124
The influent and effluent PSDs were modeled as gamma distributions on an event basis.
Influent and effluent PSD differences were shown with the probability density function of a
gamma distribution as follows:
)(
)(
)(
1
x
ex
xf (5-4)
In this expression, Γ is the gamma function; γ is the shape factor and β is the scaling
factor. γ and β parameters were estimated by minimizing the sum of squared errors (SSE),
resulting in maximizing the coefficient of determination between the measured and modeled
data. Below, F(x) is the cumulative gamma distribution and x represents flow rate in Figure 5-7
and 5-9.
x
dxxfxF0
)()( (5-5)
Figure 5-9 shows that the PMs are heterodisperse through all 10 storm events.
Cumulative PSDs were examined and results were described by an optimized cumulative gamma
distribution function summarized in Figure 5-9. The runoff median PM diameter (d50m) ranged
from 72 to 182 μm for BHS. For all 10 storm events, the recovered PM mass balance error was
within 3%. Interestingly, Influent P mass trend was similar to influent PM mass as a function of
cumulative treated volume in BHS. This result indicates that P mass is associated with PM mass.
Figure 5-10 illustrates that cumulative total mass separations for PM, TP and TN were 79.0, 60.2
and 39.4% from BHS. TP mass separation follows PM mass separation; however, TN has
significantly lower mass separation efficiency. This result indicates P is associated with PM than
N.
125
Summary
For the given BHS configuration, operating under the loading conditions of the 10
rainfall-runoff events considered in this study, sediment PM bound N (P) concentrations ranged
from 76.0 to 99.5%, settleable PM bound N (P) concentrations ranged from 0.03 to 12.9%, and
suspended PM bound N (P) concentrations ranged from 0.02 to 17.7% in rainfall-runoff.
The coarser fraction of PM generated the highest N (P) concentrations and mass, because
most of N (P) is associated with PM (> 75 µm). Sediment PM (> 75 µm) represents a significant
source area inventory and requires frequent maintenance and management for in-situ unit
operations to ensure proper function.
The PM-bound N separation efficiency for rainfall intensities between 0.07 inches and
0.95 inches ranged from 5 to 100%, with an arithmetic mean of 49%, based on Δ Mass of TN.
The event mean PM bound N separation efficiency ranged from 17 to 100%, with an arithmetic
mean of 56%, based on Δ Mass of TP. The event mean PM separation for the BHS unit was
81%. Compared to PM separation, the events mean PM-bound N (P) separation efficiencies were
lower throughout all storm events. The cumulative total mass separation of PM, TP and TN are
79.0, 60.2 and 39.4% from BHS. PM and TP separation efficiencies are significantly higher than
TN, which indicate that P is predominantly associated with PM than N. Results indicate that the
partitioning of N in BHS units is one of the reasons for lower mass separation efficiencies of N.
HS type devices, while reasonably effective for removing nutrients that associate with coarser
PM, are ineffective at targeting other fractions. The effects of re-suspension of removed
sediment should not be underestimated.
126
Table 5-1. Hydrologic characterization of the 10 rainfall-runoff events monitored between May
24, 2010 and August 21, 2010 in Gainesville, FL
Event date
(2010)
PDH
(hr)
train
(min)
drain
(mm)
irain-max
(mm/hr)
IPRT
(min)
Vrunoff
(L)
Qp
(L/s)
Qmed
(L/s)
C
(-)
n
(-)
24-May 17 36 1.8 7.6 7.3 871 1.2 0.18 0.98 12
04-June 164 42 11.2 106.7 1.2 2952 13.2 0.32 0.53 20
17-June 26 36 7.1 30.5 1.5 1946 6.1 0.76 0.55 20
29-June 192 40 20.6 106.7 0.4 5704 17.6 1.52 0.34 20
11-July 261 42 11.7 76.2 5.6 2377 5.2 0.32 0.41 20
28-July 36 50 19.1 137.2 1.0 8316 16.0 1.20 0.75 20
31-July 15 60 23.6 76.2 5.2 9031 13.2 0.60 0.76 20
13-August 112 25 2.8 55.9 2.0 314 3.2 0.06 0.23 20
14-August 28 31 2.5 22.9 1.6 594 2.5 0.25 0.47 20
21-August 83 31 2.8 45.7 2.1 299 1.5 0.03 0.21 20
Median 60 38 9.2 66.1 1.8 2162 5.7 0.32 0.50 20
Mean 93 39 10.3 66.6 2.8 3240 8.0 0.52 0.52 19
SD 87 10 8.3 41.7 2.3 3295 6.4 0.50 0.25 2.5
PDH, train, drain, irain-max, IPRT, Vrunoff, Qp, Qmed, C and n represent previous dry hours, event
duration, rainfall depth, maximum rainfall intensity, initial pavement residence time, runoff
volume, maximum flow rate, median flow rate, runoff coefficient and number of samples,
respectively.
127
Table 5-2. Summary of EMCs and ΔMass for total dissolved nitrogen (TDN), total nitrogen
(TN), total dissolved phosphorus (TDP), and total phosphorus (TP).
Event
Date
(2010)
TDN TN TDP TP
EMCi EMCe ΔM EMCi EMCe ΔM EMCi EMCe ΔM EMCi EMCe ΔM
[μg/L] [μg/L] (%) [μg/L] [μg/L] (%) [μg/L] [μg/L] (%) [μg/L] [μg/L] (%)
24-May 1688 N/A 100 2709 N/A 100 854 N/A 100 3186 N/A 100
04-June 1188 1098 8 1384 1316 5 209 257 -23 2367 1487 37
17-June 1454 1547 -6 3859 1897 51 123 77 37 5807 610 90
29-June 1787 491 73 3840 1581 59 275 221 20 4564 1807 60
11-July 2104 1901 10 2709 2383 12 424 425 0 1793 1046 42
28-July 939 656 30 2005 1599 20 238 228 4 2379 1366 43
31-July 240 177 26 668 528 21 240 177 26 668 527 17
13-Aug 1159 1072 8 7542 1239 84 331 451 -36 23640 654 98
14-Aug 572 715 -25 2502 978 61 349 469 -35 1869 866 54
21-Aug 1674 1104 34 3708 1995 46 437 415 5 2538 1085 57
Mean 1281 973 26 3093 1502 46 348 302 10 4881 1050 60
Median 1321 1072 18 2709 1581 49 303 257 5 2459 1046 56
SD. 578 532 37 1884 561 31 203 141 40 6751 436 28
128
Table 5-3. Summary of event mean value and range of variation of the dissolved fraction (fd) and
partition coefficient (Kd) of nitrogen and phosphorus for influent and effluent runoff.
Event
date
(2010)
Nitrogen Phosphorus
fd Kd fd Kd
Influent Effluent Influent Effluent Influent Effluent Influent Effluent
24-May 0.85 N/A 1476.5 N/A 0.42 N/A 21290.9 N/A
04-June 0.66 0.92 180.5 873.1 0.08 0.28 8453.6 36181.5
17-June 0.42 0.79 1065.3 5793.9 0.06 0.20 20187.8 154042.6
29-June 0.54 0.38 1606.2 21301.1 0.11 0.18 14247.5 64775.9
11-July 0.79 0.87 957.2 3093.3 0.37 0.42 8408.2 42781.4
28-July 0.47 0.66 3288.1 9490.3 0.15 0.27 20769.4 51380.6
31-July 0.44 0.40 11232.5 38769.2 0.40 0.34 11023.3 68619.6
13-August 0.66 0.83 3575.5 19817.1 0.27 0.66 29570.7 47462.9
14-August 0.27 0.67 17649.7 38361.7 0.21 0.52 23770.1 78332.8
21-August 0.58 0.58 7654.8 91307.3 0.26 0.35 37625.2 206288.3
Median 0.56 0.67 4868.6 25423.0 0.24 0.34 19534.7 83318.4
Mean 0.57 0.68 2447.2 19817.1 0.23 0.36 20478.6 64775.9
SD 0.18 0.20 5673.3 28449.9 0.13 0.15 9416.0 57964.4
129
Figure 5-1. Profile section of 1.21 m diameter BHS deployed for physical modeling loaded by
urban source area catchment.
130
Fraction of P mass (%)
0 20 40 60 80 100
Fra
ctio
n o
f P
M m
ass
(%)
0
20
40
60
80
100
Suspended
Settleable
Sediment
Fraction of N mass (%)
0 20 40 60 80 100
Fra
ctio
n o
f P
M m
ass
(%)
0
20
40
60
80
100
Suspended
Settleable
Sediment
Susp
ended
Set
tlea
ble
Sed
imen
t
Fra
ctio
n o
f m
ass
(%)
0
20
40
60
80
100
B. Phosphorus C. PM
Fra
ctio
n o
f m
ass
(%)
0
20
40
60
80
100
Susp
ended
Set
tlea
ble
Sed
imen
t
E. Nitrogen F. PM
Susp
ended
Set
tlea
ble
Sed
imen
t
Susp
ended
Set
tlea
ble
Sed
imen
t
n = 102 n = 102
n = 102n = 102
A
D
Figure 5-2. PM fraction and PM-based N and P fraction masses distribution within each
monitored rainfall-runoff event. Each symbol represents a rainfall-runoff event.
Range bars represent standard deviation.
131
PM fraction separation (%)
-200 -100 0 100 200
PM fraction separation (%)
-200 -100 0 100 200
PM
bas
ed T
P s
epar
atio
n (
%)
-200
-100
0
100
200
Suspended PM
Settleable PM
Sediment PM
PM fraction separation (%)
-200 -100 0 100 200
PM fraction separation (%)
-200 -100 0 100 200
PM
bas
ed T
N s
epar
atio
n (
%)
-200
-100
0
100
200
Figure 5-3. Separation for TN, and TP in different fractions as a function of PM fractions. Range
bars represent standard deviation.
132
Sediment [mg/L]
P fraction [mg/L]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Suspended [mg/L]
P fraction [mg/L]
0.0
0.1
0.2
0.3
0.4
0.5
Settleable [mg/L]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
= 6.69
= 19.98
= 0.14
= 0.47
I = 0.22
I = 0.60
E = 0.07
E = 0.09
I = 0.53
I = 0.52
E = 0.45
E = 0.31
Dissolved [ mg/L]
P fraction [mg/L]pdf
0.0
0.1
0.2
0.3
0.4
0.5
I = 0.52
I = 0.62
E = 0.32
E = 0.17
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
Total phosphorus [mg/L]
P fraction [mg/L]
0.0
0.1
0.2
0.3
0.4
0.5
I = 7.98
I = 20.63
E = 1.00
E = 0.73
Influent
Effluent
Figure 5-4. Phosphorus mass concentration distributions for each PM fractions. All data are
modeled as log-normal distributions (p < 0.05). Influent and effluent distributions are
statistically significantly different (p < 0.05) for each PM fractions.
133
Sediment [mg/L]
N fraction [mg/L]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Suspended [mg/L]
N fraction [mg/L]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Settleable [mg/L]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Dissolved [mg/L]
N fraction [mg/L]pdf
0.00
0.05
0.10
0.15
0.20
0.25
0.30
I = 1.63
I = 1.25
E = 1.07
E = 0.55
= 2.38
= 6.36
= 0.07
= 0.22
= 0.22
= 0.41
= 0.07
= 0.06
= 0.33
= 0.50
= 0.37
= 0.33
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
103
102
101
100
10-1
10-2
10-3
10-4
Total nitrogen [mg/L]
N fraction [mg/L]
0.0
0.1
0.2
0.3
0.4
0.5
I = 4.55
I = 7.46
E = 1.59
E = 0.61
Influent
Effluent
Figure 5-5. Nitrogen mass concentration distributions for each PM fractions. All data are
modeled as log-normal distributions (p < 0.05). Influent and effluent distributions are
statistically significantly different (p < 0.05) for each PM fractions.
134
Phosphorus [mg/L]
Kd (L/Kg)
0.00
0.05
0.10
0.15
0.20
0.25
= 19.57
= 19.75
= 83.32
= 90.57
Phosphorus [mg/L]
fd
0.00
0.05
0.10
0.15
0.20
0.25
= 0.24
= 0.18
= 0.36
= 0.19
Nitrogen [mg/L]
fd
0.00
0.05
0.10
0.15
0.20
0.25
Nitrogen [mg/L]
Kd (L/Kg)
0.00
0.05
0.10
0.15
0.20
0.25
= 4.80
= 8.08
= 25.42
= 37.99
= 0.57
= 0.26
= 0.68
= 0.23
1010.10.010.001 10 102
103
104
105
106
1010.10.010.001 10 102
103
104
105
106
Influent
Effluent
Figure 5-6. fd values and equilibrium coefficient, Kd values of nitrogen and phosphorus in
influent and effluent. There are statistically significant difference between infludent fd
and Kd value and effluent fd and Kd value (p <0.05).
135
Particle diameter (m)
15
25
38
45
53
63
75
10
61
50
18
02
50
30
04
25
60
08
50
20
00
47
50
95
00
NH
4-N
(m
g)
0
30
60
90
120
150
Cum
mu
lati
ve
per
cent
NH
4-N
(%
)
0
20
40
60
80
100
NO
3-N
(m
g)
0
2
4
6
8
10
Cum
mu
lati
ve
per
cent
NO
3-N
(%
)
0
20
40
60
80
100
Particle diameter (m)
15
25
38
45
53
63
75
10
61
50
18
02
50
30
04
25
60
08
50
20
00
47
50
95
00
TK
N (
mg)
0
2400
4800
7200
9600
12000
Cum
mu
lati
ve
per
cent
TK
N (
%)
0
20
40
60
80
100
= 10.95
= 1.04
= 12.29
= 1.02
= 18.00
= 0.73
Particle diameter (m)
15
25
38
45
53
63
75
10
61
50
18
02
50
30
04
25
60
08
50
20
00
47
50
95
00
PO
4
3- -P
(m
g)
0
90
180
270
360
450
Cum
mu
lati
ve
per
cent
P (
%)
0
20
40
60
80
100
Particle diameter (m)
15
25
38
45
53
63
75
10
61
50
18
02
50
30
04
25
60
08
50
20
00
47
50
95
00
TP
(m
g)
0
1000
2000
3000
4000
5000
Cum
mu
lati
ve
per
cent
TP
(%
)
0
20
40
60
80
100
= 13.05
= 0.86
= 13.02
= 0.87
Figure 5-7. Granulometric equilibrium distribution of ammonium-nitrogen, nitrate-nitrogen,
TKN, phosphate and TP.
136
Cumulative treated volume (L)
0 5000 10000 15000 20000 25000 30000
f d o
f in
flu
ent
TN
0.0
0.2
0.4
0.6
0.8
1.0fd (A)
f d o
f in
flu
ent
TP
0.0
0.2
0.4
0.6
0.8
1.0fd
f d o
f ef
flu
ent
TN
0.0
0.2
0.4
0.6
0.8
1.0fd (B)
Cumulative treated volume (L)
0 5000 10000 15000 20000 25000 30000
f d o
f ef
flu
ent
TP
0.0
0.2
0.4
0.6
0.8
1.0
fd
(C)
(D)
fd50
= 0.57
fd50
= 0.68
fd50
= 0.28
fd50
= 0.36
Figure 5-8. The fd of influent and effluent TN (TP) as a function of cumulative treated rainfall-
runoff volume. A – fd of influent TN, B- fd of effluent TN, C- fd of influent TP, D- fd
of effluent TP. fd50 represents the mean dissolved fraction.
137
Cumulative treated volume (m3)
0 5 10 15 20 25 30 35
0.0
0.5
1.0
1.5
2.0
Influent
Effluent
Cumulative treated volume (m3)
0 5 10 15 20 25 30 35
10
100
1000
Influent
Effluent
0.0
0.5
1.0
1.5
2.0
10
100
1000
Influent
Effluent(A)
(B)
(C) (D)
Figure 5-9. The cumulative gamma distribution parameters (ɤ for shape factor and β for scaling
factor) for event-based normalized particle size distributions (PSD). Each point is
representative of an event..
138
Cumulative treated volume (L)
0 5000 10000 15000 20000 25000 30000
Cu
mu
lati
ve
PM
mas
s (K
g)
0
5
10
15
20
25
30
Influent PM
Effluent PM
Cu
mu
lati
ve
P m
ass
(g)
0
20
40
60
80
100
Influent P
Effluent P
Cumulative treated volume (L)
0 5000 10000 15000 20000 25000 30000
Cu
mu
lati
ve
N m
ass
(g)
0
20
40
60
80
100
Influent N
Effluent N
Influent PM mass = 26.7 Kg
Effluent PM mass = 5.6 Kg
Influent P mass = 85.6 g
Effluent P mass = 34.1 g
Influent N mass = 80.9 g
Effluent N mass = 49.0 g
(A)
(B)
(C)
Figure 5-10. Cumulative influent and effluent mass of PM, phosphorus (P), and nitrogen (N)
through the entire monitoring campaign for baffled hydrodynamic separator (BHS)
in Gainesville, FL.
139
CHAPTER 6
CONCLUSION
This dissertation focused on a coupled experimental and numerical approach to
characterize particulate matter (PM) separation and scour by stormwater unit operations and
processes (UOPs) for steady and transient hydrologic, hydraulic and pollutant loadings. Four
UOPs were modeled physically and numerically, inclcuding a baffled hydrodynamic separator
(BHS), a vortex hydrodynamic separator (VHS), a screened hydrodynamic separator (SHS), and
a volumetric clarifying filtration system (VCF). This study examined the inter- and intra- event
N and P removal as a function of particle size, hydrology and partitioning for an urban carpark,
treated by a BHS, with significant biogenic loadings.
A commonly used BHS was analyzed for washout of pre-deposited PM as a function of
surface overflow rates indexed as flow rates from 10 to 125% of the HS design flow. The CFD
model was validated with experimental data across a range of flow rates and particle size
distributions, PSDs. Furthermore, a SHS and a VHS were physically and numerically compared
to a BHS unit. Three different HS units were successfully modeled with CFD to access PM
behavior, using finite volume method (FVM), a standard k-ε model for turbulent conditions, and
a Lagrangian discrete phase model (DPM) to track particles. CFD models were validated for PM
concentration, mass and PSDs with less than 10% RPD. Lagrangian particle trajectory results
show that VHS has coarsest eluted and washout PM, as well as, the highest washout rates. The
vortexing inner chamber results in a higher rate of re-suspension of finer PM in the screened
hydrodynamic separator. A CFD based probability function was developed for each
hydrodynamic separator (HS) for particle elution as function of flow rate and diameter. Such
probability functions, combined with available physical modeling data can provide a reliable
method of predicting PM yield from a HS.
140
PM separation by a BHS, SHS, and VCF system for transient hydraulic and particulate
loads observed in a real-time rainfall-runoff event was modeled with stepwise steady flow CFD
model by the application of the standard k-ε turbulence model and a Lagrangian discrete phase
model. Four discrete rainfall-runoff events for each UOPs were modeled. The modeled results
agreed with the measured data (Absolute RPD <10%).
This study successfully applied stepwise steady step CFD modeling of three HS units as
validated by matching the physically observed PM mass data. Instead of computing a fully
unsteady CFD model, the stepwise steady step model provides an efficient method of simultation.
The stepwise steady CFD modeling reduces the required computing time by more than 16 times.
Even with complex unsteady flow in urban area can be representative with stepwise steady
simulation. A calibrated/validated CFD-based iterative approach to design of unit operations has
the potential to provide reduced prototyping costs with improved performance, as a result of
carefully designed experimental matrices, focused on PM control requirements for effluent
discharges. A CFD approach to modeling the PM removal characteristics and PM washout of
UOPs is a state-of-the-art approach to reducing the uncertainty that results from assuming ideal
conditions, thus providing a more effective method for pollution control.
Urban rainfall can be a significant source of N and P, both in particulate bound and
dissolved forms. Results indicate that partitioning in rainfall-runoff resulted predominantly in N
(P) binding to sediment PM (> 75 μm). Not only does the coarser PM (> 75 μm) fraction absorb
the highest concentrations of N (P), but the highest mass is also associated with the coarser PM
(> 75 μm) fraction. PM bound N removal ranged from 5 to 100 % from the BHS with a median
49%, and PM bound P removal ranged from 17 to 100 % from the BHS with a median 56%.
Results indicate that there is generally less than a 1:1 relationship between the removal of N (P)
141
and associated removal of settleable PM, sediment PM and suspended PM (< 25 μm). There are
a few events deviating from this trend. This study concludes that while particulate P and N
constitute a large portion of the total removal by the BHS, the long-term effects of re-uptake and
re-partitioning need to be studied in addition to particulate scour, as part of a maintenance
program.
142
APPENDIX A
CHAPTER 3. PHYSICAL AND CFD MODELING OF PM SEPARATION AND SCOUR IN
HYDRODYNAMIC SEPARATORS
Figure A-1. Schematic view and dimensions of baffled hydrodynamic separator (BHS). The
effective volume of the unit indicates the volume occupied of water without any
influent flow.
Baffled hydrodynamic separator (BHS) Dimensions
Unit diameter 1.22 m
Unit height 1.52 m
Effective volume of unit 1.78 m3
Bypass baffle height 0.23 m
Diameter of influent pipe 0.30 m
Diameter of effluent pipe 0.30 m
Length of influent droplet pipe 0.43 m
Length of effluent droplet pipe 0.41 m
Overall unit surface area 1.17 m2
143
Figure A-2. Schematic view and dimensions of vortex hydrodynamic separator (VHS). The
effective volume of the unit indicates the volume occupied of water without any
influent flow.
Vortex hydrodynamic separator (VHS) Dimensions
Inner vortex chamber diameter 2.13 m
Unit height 2.13 m
Unit depth 1.22 m
Unit width 1.74 m
Effective volume of unit 7.00 m3
Diameter of effluent pipe 0.30 m
Diameter of effluent pipe 0.30 m
Overall unit surface area 3.70 m2
144
Figure A-3. Schematic view and dimensions of screened hydrodynamic separator (SHS). The
effective volume of the unit indicates the volume occupied by a static column of
water without any influent flow
Screened hydrodynamic separator (SHS) Dimensions
Unit diameter 2.13 m
Unit height 1.68 m
Effective volume of unit 2.96 m3
Volume of cylindrical sump 0.15 m3
Diameter of sump 0.64 m
Diameter of effluent pipe 0.25 m
Diameter of screen 0.64 m
Aperture opening 2400 μm
Screened area 1.27 m2
Overall unit surface area 3.58 m2
145
Table A-1. Morsi and Alexander ‘a’ – values as function of Reynolds number are reported
below. (Morsi and Alexander 1972)
Re a1 a2 a3
<0.1 24.0 0 0
0.1 < Re < 1 22.73 0.0903 3.69
1 < Re < 10 29.1667 -3.8889 1.222
10 < Re < 100 46.5 -116.67 0.6167
100 < Re < 1000 98.33 -2778 0.3644
1000 < Re < 5000 148.62 -4.75 × 104 0.357
5000 < Re < 10,000 -490.546 57.87 × 104 0.46
10,000 < Re < 50,000 -1662.5 5.4167 × 106 0.5191
Figure A-4. Reynolds number for three hydrodynamic separators (HS) as a function of flow rate.
Flow rate (L/s)
0 20 40 60 80 100
Re
0
2000
4000
6000
8000
10000
12000
BHS
VHS
SHS
146
APPENDIX B
CHAPTER 4. STEPWISE STEADY CFD MODELING OF UNSTEADY FLOW AND PM
LOADING TO UNIT OPERATIONS
Figure B-1. Schematic view and dimensions of baffled hydrodynamic separator (BHS). The
effective volume of the unit indicates the volume occupied of water without any
influent or effluent flow.
Baffled hydrodynamic separator (BHS) Dimensions
Unit diameter 1.22 m
Unit height 1.52 m
Effective volume of unit 1.78 m3
Bypass baffle height 0.23 m
Diameter of influent pipe 0.30 m
Diameter of effluent pipe 0.30 m
Length of influent droplet pipe 0.43 m
Length of effluent droplet pipe 0.41 m
Overall unit surface area 1.17 m2
147
Figure B-2. Schematic view and dimensions of screened hydrodynamic separator (SHS). The
effective volume of the unit indicates the volume occupied by a static column of
water without any influent or effluent flow.
Baffled hydrodynamic separator (BHS) Dimensions
Unit outer diameter 0.89 m
Unit inner diameter 0.50 m
Unit height 1.23 m
Effective volume of unit 0.65 m3
Diameter of influent pipe 0.15 m
Diameter of effluent pipe 0.20 m
Distance from top to screened area 0.33 m
Aperture opening 2400 μm
Screened area 0.52 m2
Overall unit surface area 0.62 m2
148
Figure B-3. Schematic view and dimensions of volumetric clarifying filter (VCF). The effective
volume of the unit indicates the volume occupied of water without any influent or
effluent flow.
149
AOCM represents Aluminum oxide coated media
Volumetric clarifying filter (VCF) Dimensions
Cartridge outer diameter 0.46 m
Cartridge inner diameter 0.08 m
Cartridge height 0.53 m
Cartridge media size (d50) 3.56 mm
Cartridge media specific gravity 2.35 g/cm3
Cartridge media specific surface area 0.94 m2/g
Cartridge media porosity 36.7%
Cartridge media dry bulk density 0.68 g/cm3
Filter media AOCM
Unit height (influent) 1.69 m
Unit height (effluent) 1.87 m
Unit depth 1.17 m
Unit width 2.12 m
Effective volume of unit 1.92 m3
Diameter of influent pipe 0.15 m
Diameter of effluent pipe 0.15 m
Bottom to V-notch weir on baffle 0.39 m
Overall unit surface area 2.25 m2
150
Table B-1. Morsi and Alexander ‘a’ – values as function of Reynolds number are reported
below. (Morsi and Alexander 1972)
Re a1 a2 a3
<0.1 24.0 0 0
0.1 < Re < 1 22.73 0.0903 3.69
1 < Re < 10 29.1667 -3.8889 1.222
10 < Re < 100 46.5 -116.67 0.6167
100 < Re < 1000 98.33 -2778 0.3644
1000 < Re < 5000 148.62 -4.75 × 104 0.357
5000 < Re < 10,000 -490.546 57.87 × 104 0.46
10,000 < Re < 50,000 -1662.5 5.4167 × 106 0.5191
Figure B-4. Reynolds number for three hydrodynamic separators (HS) as a function of flow rate.
Flow rate (L/s)
0 20 40 60 80 100
Re
0
2000
4000
6000
8000
10000
12000
BHS
VHS
SHS
151
Table B-2. The injection particulate matter (PM) size.
Injection PM size
(μm)
2000
850
600
425
300
250
180
150
106
75
63
53
45
38
25
10
7
5
3
1
Table B-3. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1 (29
June 2010) - BHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.00 0.00 0.0 0.00 0.00
2.3 2.21 2444.3 133.2 50.5 0.70 117.88
4.1 5.67 1488.5 538.2 78.6 0.70 201.67
5.6 1.59 863.9 548.6 78.1 1.09 91.57
7.3 4.97 756.6 510.8 108.5 0.87 199.71
9.6 4.15 428.9 198.9 74.6 1.37 67.53
12.6 2.28 487.9 201.0 62.3 0.98 80.08
15.6 2.42 396.9 63.2 71.8 1.32 67.72
20.6 1.24 522.8 36.7 62.8 1.09 75.85
26.6 0.48 136.3 75.0 85.2 1.57 61.26
35.6 0.07 4929.9 22.4 68.8 1.24 73.97
152
Table B-4. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 2 (11
July 2010) - BHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
6.0 0.16 1760.1 18.2 124.8 0.57 493.56
8.0 0.46 445.9 6.6 109.8 0.84 192.01
10.0 0.35 195.7 8.0 125.6 0.83 220.96
12.0 0.43 1001.6 14.5 109.1 1.10 138.28
15.0 3.21 223.2 57.9 41.0 0.95 63.93
18.3 1.54 49.5 66.9 61.3 1.39 55.97
22.5 2.94 231.5 65.5 145.9 1.24 152.58
25.5 2.76 996.8 143.7 150.8 1.70 105.02
28.3 0.55 391.4 101.1 191.2 1.68 127.81
34.3 0.02 1573.6 29.5 190.3 0.66 496.34
Table B-5. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 3 (29
June 2010) - BHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
4.4 2.47 8088.6 52.1 187.4 0.67 484.48
5.9 2.14 782.8 17.0 125.6 0.57 498.67
7.3 2.83 419.7 192.8 86.4 0.81 155.24
10.7 3.82 207.9 107.0 125.6 0.83 220.96
17.2 0.04 3137.0 39.1 109.2 1.09 139.65
20.3 2.20 567.9 62.0 41.5 0.95 64.27
23.3 3.74 549.0 64.4 61.2 1.40 55.25
28.3 13.63 260.5 163.8 146.9 1.22 157.01
38.5 0.97 78.5 41.9 150.9 1.71 103.87
48.7 0.98 20.4 12.7 191.5 1.67 128.35
153
Table B-6. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 4 (11
July 2010) - BHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
8.8 0.23 977.8 8.4 249.0 1.88 165.55
10.2 0.46 267.1 8.1 170.6 0.88 314.32
11.9 2.30 739.7 15.8 183.8 0.83 371.15
12.7 2.14 240.1 25.7 171.8 0.82 357.48
13.6 0.38 108.1 25.2 129.7 0.76 321.84
14.6 0.46 700.4 29.1 428.9 1.18 474.71
15.5 0.38 115.9 43.2 406.4 1.00 541.59
16.7 0.38 56.7 63.9 182.1 0.74 398.36
22.0 0.35 33.3 18.1 85.4 0.56 324.19
27.1 0.06 13.8 16.8 50.4 0.83 97.48
Table B-7. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1 (13
March 2004) - SHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
7.0 0.01 230.9 0.4 45.2 0.77 35.36
22.0 0.05 10.6 1.5 28.4 0.56 132.35
101.0 0.02 1421.8 6.4 52.2 0.85 20.95
105.0 0.25 863.8 1.1 60.3 0.79 15.67
109.0 0.76 62.6 0.2 43.9 0.81 24.72
113.0 1.16 164.8 8.1 52.8 0.82 22.43
117.0 2.73 402.0 21.6 49.0 0.84 16.46
121.0 2.19 0.1 6.6 45.0 0.85 11.39
125.0 1.93 36.2 6.2 41.5 0.86 17.95
129.0 1.99 3.7 2.7 56.4 0.83 14.13
133.0 0.89 12.7 6.6 43.6 0.87 18.78
148.0 0.41 51.2 0.5 55.4 0.95 10.21
163.0 0.55 34.6 0.2 46.1 0.88 13.64
208.0 1.07 1.5 0.1 51.4 0.85 18.34
253.0 0.88 12.6 0.1 44.6 0.77 19.63
283.0 1.55 4.2 0.1 51.2 0.88 15.46
393.0 1.81 17.1 0.1 38.1 0.79 22.34
154
Table B-8. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 2 (20
August 2004) - SHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.00 0.0 0.0 0.00 0.00
10.0 0.01 4259.82 334.20 255.9 1.19 289.68
12.0 1.13 3859.64 866.17 232.0 0.98 341.91
14.0 3.71 374.87 307.82 188.8 1.45 169.73
17.0 5.08 2798.83 215.85 195.0 1.70 142.08
21.0 15.80 375.64 111.78 199.8 1.66 145.46
26.0 15.85 445.04 115.89 211.5 1.05 273.43
29.0 7.18 206.69 105.61 541.2 3.98 152.60
31.0 3.71 161.64 50.82 735.3 4.15 194.53
33.0 1.61 108.08 60.53 547.4 3.37 187.00
34.0 1.01 138.77 51.59 551.0 3.71 168.52
36.0 0.74 66.95 43.42 288.7 1.22 188.29
38.0 0.46 63.99 48.66 264.2 1.42 165.37
40.0 0.29 65.60 45.63 256.7 1.38 155.91
43.0 0.19 92.46 34.02 297.3 1.45 152.50
46.0 0.12 127.12 334.20 285.0 1.21 132.76
Table B-9. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 3 (14
October 2004) - SHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
31.0 0.01 230.9 0.0 94.4 0.90 174.45
63.0 0.04 618.5 379.5 13.6 0.97 21.33
69.0 0.31 849.2 347.9 38.5 0.58 156.25
73.0 0.43 604.3 303.5 28.5 0.74 71.45
79.0 0.42 458.4 283.0 116.2 1.02 172.27
86.0 0.46 384.1 257.7 133.4 0.71 350.89
106.0 0.43 332.5 232.2 64.7 0.78 116.59
122.0 0.27 256.8 209.6 115.4 2.04 62.12
132.0 0.11 200.2 198.1 85.3 1.54 63.79
140.0 0.06 197.2 200.4 22.4 0.72 58.61
155.0 0.09 237.0 210.6 90.7 0.71 152.71
165.0 0.08 236.0 218.5 80.3 0.56 175.31
176.0 0.03 192.8 217.1 60.2 0.66 133.25
197.0 0.01 194.0 217.1 54.3 0.54 116.82
155
Table B-10. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 4
(30 June 2005) - SHS.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
9.0 0.01 3075.7 0.0 133.3 0.92 226.78
11.0 0.57 1753.3 462.9 268.8 0.76 561.33
12.0 2.18 1016.7 424.3 102.1 0.54 443.80
14.0 4.03 681.9 394.4 77.7 0.57 319.00
15.0 6.81 405.8 323.3 40.0 0.59 171.56
17.0 11.06 265.0 207.7 38.9 0.56 190.10
19.0 13.21 166.5 136.4 44.6 0.53 220.46
21.0 13.53 153.5 120.2 106.0 0.53 410.79
23.0 12.75 142.8 94.5 45.7 0.55 207.91
29.0 10.22 104.6 81.9 83.4 0.66 278.95
33.0 2.50 74.9 69.7 39.8 0.58 181.34
36.0 0.59 118.1 62.2 42.9 0.53 175.11
42.0 0.54 332.8 95.8 45.1 0.57 170.60
48.0 1.50 301.3 114.2 48.2 0.58 180.12
58.0 2.30 105.4 103.1 46.3 0.58 190.47
68.0 1.97 113.0 99.9 37.1 0.56 182.15
Table B-11. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1
(21 April 2006) - VCF.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
1.0 1.72 5805.5 158.9 33.1 0.57 132.80
3.0 3.64 2695.4 130.9 24.5 0.66 78.85
6.0 4.30 931.9 122.7 18.3 0.66 54.28
10.0 0.43 767.6 116.1 11.5 0.67 34.13
13.0 0.25 603.0 108.9 12.1 0.68 33.75
16.0 0.10 254.9 109.1 7.5 0.69 21.07
19.0 0.04 183.3 108.6 7.3 0.65 23.32
23.0 0.01 174.1 100.5 35.7 1.13 41.27
27.0 0.02 247.3 118.0 25.1 0.71 52.74
32.0 0.02 227.4 112.6 5.8 0.67 18.49
37.0 0.01 161.0 126.2 6.2 0.95 11.97
42.0 0.02 183.1 106.4 33.7 0.98 47.23
49.0 0.01 183.0 117.7 16.3 0.60 43.08
156
Table B-12. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1
(29 April 2006) - VCF.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
5.0 0.20 5036.4 58.8 11.5 0.70 34.37
9.0 8.74 769.4 100.6 8.7 0.51 60.66
13.0 6.38 325.3 77.8 19.4 0.84 41.10
17.0 3.14 272.7 49.4 123.5 0.91 207.60
21.0 1.66 256.1 41.2 49.0 0.47 373.64
25.0 1.28 192.1 44.6 315.6 24.55 12.89
31.0 1.69 150.7 39.6 13.7 0.74 37.23
37.0 1.78 124.2 38.3 248.2 1.54 164.52
43.0 0.94 135.3 26.9 20.1 0.48 169.53
49.0 0.96 152.5 30.8 299.9 15.44 19.40
60.0 0.60 165.3 26.0 24.0 0.50 179.53
69.0 0.38 112.7 20.0 297.4 9.80 30.01
76.0 0.39 99.9 17.0 31.8 0.49 242.11
87.0 0.24 77.1 23.6 97.0 0.51 386.80
104.0 0.21 60.1 25.7 47.6 0.49 332.80
134.0 0.54 58.3 19.0 309.7 19.99 15.51
143.0 0.58 42.4 19.4 13.5 0.87 25.39
159.0 0.17 37.9 19.8 75.9 0.94 100.69
Table B-13. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1
(04 July 2006) - VCF.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
6.0 0.06 2236.3 40.5 40.6 0.64 139.16
8.0 0.80 631.6 46.4 76.4 0.59 250.61
10.0 0.94 253.4 34.2 22.5 0.77 55.68
12.0 0.60 245.0 45.4 129.9 0.58 332.81
13.0 0.26 211.7 43.4 19.9 0.78 45.24
15.0 0.21 328.2 39.9 40.4 0.60 174.43
18.0 0.16 166.2 45.6 17.7 1.03 23.35
20.0 0.08 171.2 41.1 23.2 2.93 9.41
26.0 0.10 477.0 42.0 14.9 0.74 34.67
29.0 0.14 154.1 48.7 47.3 0.91 93.35
31.0 0.07 111.0 39.6 16.0 0.82 30.03
34.0 0.05 87.6 39.9 27.8 0.61 183.36
37.0 0.02 87.0 32.8 13.2 0.82 23.57
157
Table B-14. Particle injection time (min), flow rate (L/s), influent and effluent PM concentration,
median particle diameter (d50m), γ – shape factor, and β – scaling factor of storm 1
(04 July 2006) - VCF.
Injection
time (min)
Flow rate
(L/s)
PMinf
[mg/L]
PMeff
[mg/L]
d50m
(μm) γ β
0.0 0.00 0.0 0.0 0.0 0.00 0.00
6.0 0.05 219.6 23.1 15.2 0.76 36.34
9.0 0.19 79.3 21.6 27.1 0.53 149.70
12.0 0.12 45.9 26.0 9.3 0.83 19.54
15.0 0.06 74.4 30.5 25.7 0.85 45.93
19.0 0.08 49.8 32.7 7.8 0.70 23.77
22.0 0.04 61.9 30.3 56.6 0.67 183.79
26.0 0.05 182.8 38.9 10.7 0.69 32.52
29.0 0.41 237.8 45.0 103.3 0.59 315.56
32.0 0.59 159.7 37.6 8.3 0.79 18.83
36.0 0.23 150.7 31.7 28.8 1.87 19.19
40.0 0.23 60.6 35.0 7.0 0.79 16.56
48.0 0.14 35.4 25.7 25.0 0.93 33.17
58.0 0.02 29.8 26.8 13.3 0.72 35.95
65.0 0.09 12.9 12.5 64.2 0.79 121.15
Table B-15. Computing time for stepwise steady CFD modeling and fully transient flow as a
function of number of steady steps.
# of steady steps
Stepwise steps
computing time
(hrs.)
Fully transient flow
computing time
(hrs.)
10 1.5 ± 1 72 ± 12
20 3.0 ± 1 72 ± 12
30 4.5 ± 1 72 ± 12
40 6.0 ± 1 72 ± 12
50 7.5 ± 1 72 ± 12
158
Table B-15. Computing time for stepwise steady CFD modeling and fully transient flow as a
function of number of steady steps.
UOP Storm
event
BHS
(2010)
29 June 15.73
11 July 9.47
28 July 7.87
14 August 2.99
SHS
(2004-2005)
14 March 2.66
20 August 7.82
14 October 1.42
30 June 8.24
VCF
(2006)
21 April 17.46
29 April 3.59
04 July 11.89
05 July 4.25
159
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169
BIOGRAPHICAL SKETCH
Hwan Chul Cho was born in Daegu, South Korea and he came to the United States in Fall
2007 after receiving his Bachelor of Engineering degree in Department of Environmental
Engineering at Ajou University, South Korea. He received his Ph.D. in environmental
engineering from University of Florida in April 2012. His doctoral research was focused on
physical and computational fluid dynamics (CFD) models of PM separation and scour in
hydrodynamic unit operations. He completed his research under the guidance of Dr. John J.
Sansalone in the Department of Environmental Engineering and Sciences. Hwan Chul pursues
life to the fullest, loves his family deeply, and enjoys deep and intimate relationships.
