CFD modeling

Analysis of the flux Analysis of the flux of biogenic substances of biogenic substances on water eutrophication on water eutrophication in the Sulejin the Sulejoow Reservoirw Reservoir

M.Sc. Aleksandra Ziemińska-Stolarska

Supervisor: Prof. Jerzy Skrzypski

Lodz University of Technology, Poland

Faculty of Process and Environmental Engineering

Plan of presentation

1. Aim of the thesis

2. Study area – Sulejow Reservoir

3. 3D CFD model of flow hydrodynamic in the Sulejow Reservoir

4. Verification of CFD model

5. Analysis of water quality in the Sulejow Resrevoir (WASP)

6. Conclusions

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EutrophicationEutrophication (Greek: eutrophia-healthy, adequate nutrition, development) resulted from the river phosphorus and nitrogen supply, effects with a disturbance of the ecological balance of the ecosystem and occurrence of blue-green algae blooms during summer.

Eutrophication

5Seasonal transience of microcystin May-September 2008

Con

cen

trat

ion

of m

icro

cyst

in μ

g/d

m3

Source: www.geoportal.gov.pl

Aim of the study

Application of coupled CFD and WASP models allows to obtain a full picture

of the ecological status of the reservoir and will enable the identification of

areas with the highest accumulation biogenic components and thus areas

particularly vulnerable to the formation of cyanobacterial blooms

Develop three dimensional model of flow hydrodynamic in the Sulejow

Reservoir using CFD technique.

Perform calculations of water quality in the Sulejow Reservoir with the use

of the WASP (Water Analysis Simulation Program) program for which

hydrodynamic data were supplied by my own CFD model. It allows to

obtain an realistic image of the distribution of temperatures, flow

velocities and concentrations of main substances responsible for the

eutrophication process.

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Application areaApplication area Associated studiesAssociated studies

Flow-field prediction

Fang and Rodi (2003), Fangkai et al. (2007),

Zinke et al. (2010), Wang et al. (2010),

Khosronejad (2010),

Analysis of particulate behaviour Stovin and Saul (1996;2000),

Adamson et al. (2003),Bridgeman et al. (2009)

Prediction of water surface profiles Ta and Brignal (1998),

Kouyi et al.(2005),Lau et al. (2007),

Anderson et al. (2013)

Residence time distribution (RTD) Faram et al. (2004),

Kennedy et al. (2006), Lau et al. (2007)

Sediment transport pattern

Faram and Harwood (2003), Dargahi (2004),

Gupta et al. (2005), Stovin et al. (2005),Townsend (2007)

State of the art

State of the art

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Dargahi (2004) Sanjiv K. Sinha (1998) Zinke et al. (2010)

Andersson (2013)

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Models Model Version Description

Streeter-Phelps models

S-P modelThomas BOD-DO

O`Connor BOD-DODobbins-Camp

BOD-DO

1D steady-state models focus on oxygen balance and one-order decay of BOD.

QUAL

QUAL IQUAL II

QUAL 2EQUAL2E UNCAS

QUAL2K

1D river and stream water quality models suitable for dendritic river and non-point source pollution

including steady-state or dynamic models.

WASP(Water Analysis

Symulation Programme)

WASP 7.1

Dynamic compartment-modeling program for aquatic systems, including water column and the

underlying benthos. Allows to investigate 1, 2, and 3D systems, and a variety of pollutant types. Can be linked with hydrodynamic and sediment transport

models that can provide flows, depths velocities, temperature, salinity and sediment fluxes.

BASINS (Better Assessment Science Integrating point & Non-point Sources)

BASIN 1BASIN 2BASIN 3BASIN 4

GIS tool for watershed analysis and monitoring. Multipurpose environmental analysis systems, which integrate point and non-point pollution

suitable for water quality analysis at watershed scale.

State of the art

GEMSS(Generalized

Environmental Modeling System

for Surface waters)

GEMSS

3D hydrodynamic and transport models embedded in a geographic information and environmental data system (GIS). Compute time-varying velocities, water surface

elevations, and water quality constituent concentrations in rivers, lakes, reservoirs, estuaries, and coastal

waterbodies.

QUASAR QUASAR1D dynamic model suitable for dissolved oxygen simulation

in large rivers.

MIKE Mike 11Mike 21Mike 31

1,2,3D models simulate flow and water level, water quality and sediment transport in rivers, flood plains, irrigation

canals, reservoirs and other inland water bodies.

EFDC EFDC

Hydrodynamic model used to simulate aquatic systems in 1, 2,3D. Solves 3D, vertically hydrostatic, free surface, turbulent

averaged equations of motion for a variable-density fluid. Dynamically-coupled transport equations for turbulent

kinetic energy, turbulent length scale, salinity and temperature are also solved.

CE-QUAL-W2CE-QUAL-

W2

2D hydrodynamic and water quality model, assumes lateral homogeneity. Best suited for long and narrow water bodies exhibiting longitudinal and vertical water quality gradients.

Can be applied to rivers, lakes, reservoirs, estuaries.10

Models Model Version Description

State of the art

Phytoplankton kinetics

Where:Sk4j = reaction term, mg carbon/L dayPj = phytoplankton population, mg carbon/LGp1j = growth rate constant, day-1

Dp1j = death plus respiration rate constant, day-1

ks4j = settling rate constant, day-1j = segment number, unitless

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GRTj= the temperature adjustment factor, dimensionless GRIj= the light limitation factor as a function of I, f, D, and Ke, dimensionless GRNj= the nutrient limitation factor as a function of dissolved inorganic phosphorus and

nitrogen (DIP and DIN), dimensionless: T= ambient water temperature, °C I= incident solar radiation, ly/day f= fraction day that is daylight, unitless D= depth of the water column or model segment, m Ke= total light extinction coefficient, m-1

Io= the average incident light intensity during daylight hours just below the surface, assumed to average 0,9 I/f, ly/day

Is= the saturating light intensity of phytoplankton, ly/day

js4j p1jp1jk4j P ) k -D - (G S =

RNjRIj RTj1c P1j G G G k G ⋅⋅⋅=

201RTjG −Θ= Tc

++

=DIPK

DIP

DINK

DINMinG

mPnMRN ,

−−

⋅−−⋅

=s

oe

s

o

eIj I

IDK

I

If

DK

eG expexp(exp

Study Area - Sulejow Reservoir

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Lodz Name Value

Total length 17,1 km

Maximum width 2,1 km

Average width 1500 m

Average depth 3,3 m

Maximum depth 15 m

Shoreline length 58 km

Surface area 22 km2

Usable capacity 61 x 106 m3

Maximum capacity 75 x 106 km3

Retention time ~30 days

Table 1. Parameters of the Sulejow Reservoir.

Fig.1. Location of the Sulejow Reservoir.

Study Area - Sulejow Reservoir

Fig.2 Pilica catchment Source: Corine Land Cover 2006 13

Characteristic Pilica Luciaza

Catchment area A [km2] 3919 766

River lenght L [km] 160 49A/L ratio 25 16

Mean discharge [m3/s] 22,8 2,48

Table 2. Characteristic of main rivers supplying the Sulejow Reservoir.

Nutrient LoadSulejow (Pilica River)•43,3 TP/year (2005-2009 IMGW, WIOŚ)•986 TN/year (2005-2009 IMGW, WIOŚ)

Kludzice (Luciaza River)•8,68 TP/year (2005-2009 IMGW, WIOŚ)•215 TN/year (2005-2009 IMGW, WIOŚ)

3D CFD MODEL OF HYDRODYNAMIC IN THE

SULEJOW RESERVOIR

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3D CFD modeling Geometry modeling & Grid generation

Fig.5. 36 cross section profiles of the Sulejow Reservoir.

Source: Regional Board of Water Management, Warsaw, (2008)

3D CFD modeling Computational mesh

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air

Fig. 7 Fragment of the structural mesh with the boundary layer.

Boundary layerFirst raw – 0,01Growth factor -1,01

3D CFD modeling Parameters, Boundary & Initial Conditions

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Boundary conditions: two inlets (Pilica and Luciaza rivers), one outlet (dam).

Pressure value was equal to the atmospheric, which enable to simulate the flow as the open channel.

Bottom and sides were treated as a wall. At the walls including the reservoir base, the no-slip conditions were applied. The bottom and sides were assigned a 0,02 m roughness height.

At the water table the moving wall function was used. Simulated inflow boundaries were specified with mass flow rate, normal

to the boundary. The k-ω SST turbulence model was applied to the calculations.

ModelSpace Three dimensionalTime SteadyTurbulence k- SSTɷ

Discretization methodPressure StandardPressure-velocity coupling scheme SIMPLECMomentum Second order UpwindTurbulence energy kinetic First order UpwindTurbulence dissipation rate First order Upwind

Table 2. Solution conditions and methods for the Sulejow Reservoir simulation.

Results of CFD calculations

19Fig. 9. Velocity field (m/s) in the Sulejow Reservoir in A) July B) December.

Simulation results under steady-state conditions were first reviewed to understand the general flow behavior indicated by the model.

A B

3D CFD modelingTwo-phase flow model

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Two-phase flow model:

Lenght – 80 mWidth – 3mWind speed – 2m/sWind direction - southeast

Results of CFD calculations

21Fig. 9. Velocity field (m/s) in the Sulejow Reservoir in October.

No wind conditions Wind

Wind direction (SE)~2 m/s

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Acoustic Doppler current profilers (ADCPs) are highly efficient and reliable instruments for flow measurements in rivers and open-channel environments.

Fig.8. Acoustic Doppler current profilers ADCP

(StreamPro)

3D CFD modeling Model verification


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1

2

3 4

Moving boat ADCP measurements provided spatially overall picture of flow conditions in the Sulejow Reservoir.


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3 4

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InletsSSQ [m3*s-1]

March July December

Pilica River 31,74 9,76 17,55

Luciaza River 1,64 1,91 2,55

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InletsSSQ [m3*s-1]

March July December

Pilica River 31,74 9,76 17,55

Luciaza River 1,64 1,91 2,55

WATER ANALYSIS SIMULATION PROGRAME

(WASP)

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Analysis of water quality in the Sulejow ResrevoirWater Analysis Symulation Programme (WASP)

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Table 6. Parameters of the segments.

Nutrient cycling in WASP

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PhytoplanktonPhytoplankton

NHNH33

Respiration

Dis.Dis.Org. POrg. P

Dis.Dis.Org. NOrg. NCBODCBOD11

CBODCBOD22

CBODCBOD33

POPO44

SSinorgSSinorg

Settling

Photosynthesis

Denitrification

Nitrification

atmosphereDODO

NONO33

Adsorption

Oxidation

Mineralization

Reaeration

NN22

NNPPCC

DetritusDetritus

PeriphytonPeriphyton

Death&Gazing

Nitrogen

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PhosphorusPhosphorus

32

Chlorophyll „a”Chlorophyll „a”

33

WASP verification

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WASP verification

35

WASP verification

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Case study

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Load reduction

SummarySummary

A 3D single-phase CFD model of flow hydrodynamic in the Sulejow Reservoir with accurate depiction of basin bathymetry was developed and verified.

The results generated by the model indicate that the flow field in the Sulejow Reservoir is transient in nature, containing turbulent structures and swirl flow. Analysis of the flow velocities show that main path of flow is approximately along the bad of the Pilica River.

The WASP eutrophication model was applied to simulate the complex nutrient transport and cycling in the Sulejow Reservoir.

Proper correlation between the measured and calculated values ware obtained, which is a result of application a realistic hydrodynamics in the lake, determined from the CFD calculations in the WASP analysis.

Analysis of the results shown correlation between hydrodynamics and concentrations of selected nutrients in the reservoir.

The resulting model is accurate, robust and the methodology develop in the frame of this work can be applied to all types of storage reservoir configurations, characteristics, and hydraulic conditions.

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THANK YOU FOR YOUR ATTENTION

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