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The Alan G. Davenport Symposium
UWO, June, 2002
Dispersion Modelling
What did the wind tunnel ever do for us?
Major contributions by topic area:
Basic dispersion processes Complex terrain
Plume rise and entrainment Dense gas dispersion
Building effects Concentration fluctuations
Urban boundary layer Convective boundary layer
… and water tanks ....
Methodologies
Examples:
Plume rise + buildings
Leaks and losses
Urban dispersion
Dense gases
Inverse modelling
Density ratio, speed ratio, Richardson number
ro
r
Wo
U
gH
U 2Þ
Ufs
Uwt
= e
e , the length scale ratio
'Relaxed' scaling
dimensionless momentum and buoyancy fluxes
roW
o
2
rU 2
gDrWoH
rU 3Þ
Ufs
Uwt
= ea
m
afs
æ
èç
ö
ø÷
1/2
1-afs
1-am
é
ë
êê
ù
û
úú
1/2
a , the density ratio
Methodology
Wind tunnel
Neutral
Slightly stable
Slightly unstable
Water tank
Neutral
Very stable
Very unstable
Source modellingInflow simulation
Implications
Working section
H ~1 m working section length ~ 10 - 20 m; height ~ 1.5 to 2 m; width at least two
to three times the height ~ 4 m; wind speed range 0.5 to 5 ms-1.
boundary layer simulation emissions simulation/model maximum glc
physical model
plume rise &
spreadH
h
Dh
U(z), u'(z), etc.
10 H 5 - 10 H
Boundary layer simulation
Standard methods for neutral conditions
– vorticity generators, rough surface.
No standard approaches for stable and
unstable conditions.
Fast
FFID system
Chilled water
supply ~ 10˚CTwin
fans
20 x 3.5 x 1.5 m
working section
0 - 3 ms-1
Inlet and heater section
15 layers 400 kW capacity
ambient to ~ 80˚C
Rough wall
cooled ~ 15˚C
heated ~ 100˚C
Mechanical simulation
devices & rough wall
Heat
exchanger
Turntable
Heater control
with thermistor
feedback
Gas
supplies
Run control
system
Computer control, data
collection & data analysis
Thermistor
systems
Speed
control
Traverse and
turntable
control
LDA, PIV, hot
wire, cold wire,
web-cams etc
Source
Example 1: Buoyant plume and buildings
0.0
0.1
0.2
0.3
0 5 10 15 20 25
Dim
ensi
on
less
co
nce
ntr
atio
n
Wind speed at 10m, m/s
Maximum glc at
500 - 600m downwind
Wind tunnel, density ratio = 0.30
= 0.37
= 0.75
= 0.92
Field data, density ratio 0.83
H = 54m,W
o=12ms-1, Q
H= 5.8 MW , DT = 60 K
Example 2: Leaks and losses
Chemical process plant
---
an example of a
complex site that, in
turn, creates complex
flow and dispersion
conditions in short
range
---
conditions that are not
easily parameterised.
Wake flows
Characteristics of the
wake flow downwind of
obstacles of different
porosities.
There is often a need to
interpret measurements
of wind speed and
pollutant concentration in
such flows to detect and
quantify loss rates.
Interpretation
Direct application of results is sometimes feasible …
… more likely that the outcome of wind tunnel work is integrated into a
dispersion model to allow extrapolation to all wind/weather conditions
… a process model is built to link the experiments and the model.
That might be as simple as a virtual source definition or as complex as a
full, near-field dispersion module.
Example 3: Urban dispersion
Street network dispersion –
multiple pathways set by pattern
of intersections …
... plus travel above roof level.
Urban dispersion – exchanges at
intersections and with flow above
roof level.
What are the best
parameterisations?
Concentration decay – passive release
Street network dispersion
– multiple pathwaysCombined wind tunnel and field data from
London (DAPPLE) led to a robust correlation:
confirmed and refined by later wind tunnel
work using a 1:350 scale model of central
Paris.
Note that the blue symbols represent upwind
dispersion.
C*
R/H
Upper bound dimensionless concentration
as function of separation.
CU (H )H 2
Q= 12
R
H
æ
èçö
ø÷
-2
Source-receptor relationships
Contributions from sources in and around
Marylebone Road to concentration observed
at the AURN site.
C* = CUH2/Q
Regions responsible for 80% of the
concentration at the AURN site:
180˚, -45˚, -90˚, 45˚.
Urban areas and dense gas dispersion
Source at
ground level
Carbon dioxide
Q = 50 litre min-1
U = 1 ms-1
Extensive
upwind and
lateral spread
from the
source.
How is chanelling (wind along major, straight street) to be treated?
How in this
parameterised?
Inverse modelling
Use data series to
determine source
properties (Q, To, x, y).
Understand response to
data quality; e.g.
sampling time.
How soon can a reliable
estimate be attained?
Four detectors returning C(t).
Example: response to averaging time in open terrain
-50.0
0.0
50.0
100.0
150.0
200.0
10 100 1000 10000 100000 1000000
So
urc
e p
osi
tio
n, x
, m
Averaging time, s
power law spread
linear spread
This is performance in a
simple boundary layer,
where the dispersion model
can be made accurate.
Near obstacles, or with
dense gas or buoyant
plumes, the errors in the
dispersion model dominate.
Search for source properties
that minimise mean square
error in predictions.
… to summarise …
Wind tunnel simulation - a proven technology for research and practice
Neutral, slightly stable, slightly unstable conditions; stable/unstable in water tank
Stand alone or used in conjunction with models
LES makes the most fruitful combination
Current interests at EnFlo include
the urban environment indoor and out MAGIC
hazardous gas dispersion/inverse modelling MODITIC
Automation an essential operational requirement