Simulation of flow in an idealised city using various CFD...

Author version of the paper published in:

Int. J. Environment and Pollution, Vol. 44, Nos. 1/2/3/4, 2011, pp. 359-367

359

Simulation of flow in an idealised city using various CFD codes

István Goricsán, Márton Balczó∗∗∗∗, Miklós Balogh, Károly Czáder,

Anikó Rákai, Csilla Tonkó

Department of Fluid Mechanics,

Budapest University of Technology and Economics (BME),

1111, Bertalan L. 4-6., Budapest, Hungary *Corresponding author, [email protected]

Abstract: In the framework of the COST 732 action several teams run models to verify

the proposed Best Practice Guideline (Franke et al. 2007). The data set concerns the

MUST experiment, where wind field and dispersion measurements were conducted for

an array of obstacles as a full scale experiment, as well as in a wind tunnel. The

pollutant transport research group at the Department of Fluid Mechanics of the BME

started simulations of flow within this idealised city using two commercial CFD codes,

FLUENT and MISKAM. The paper presents results of the flow simulations in the 0

degree MUST case through velocity profiles, contour plots and validation metrics.

Keywords: atmospheric dispersion, CFD, MISKAM, FLUENT, validation

Reference to this paper should be made as follows Goricsan et al. (2011) Simulation of flow in an idealised city using various CFD codes, Int. J. Environment and Pollution, Vol. 44, No. 1/2/3/4, pp. 359-367.

Biographical notes: István Goricsán received his PhD in Mechanical Engineering at the Budapest University of Technology and Economics (BME). His research interests include issues related to dispersion in atmospheric boundary layers, wind tunnel measurement of wind loads acting on buildings and structures, as well as pollutant transport related CFD.

Márton Balczó graduated as mechanical engineer 2001 from the BME and the University of Karlsruhe and is research assistant at the Department of Fluid Mechanics. His research area is pollutant transport in urban environment.

Miklós Balogh received his diploma degree in meteorology at Eötvös Loránd University, Faculty of Science. Since 2006 he is doctoral candidate at the Department of Fluid Mechanics, focussing on CFD code development.

Károly Czáder, Anikó Rákai and Csilla Tonkó are undergraduates at the BME. They study Environmental Engineering and made a contribution to the simulations performed in the framework of COST 732.

1 Introduction

Urban air pollution is becoming one of the most important issues in environmental problems in cities. To

determine the wind field and the concentration distribution in urban micro-scale areas (length scale up to

5 km) it is necessary to use obstacle resolving methods. Traditional solution is the physical modelling in



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wind tunnels, but numerical simulation (CFD) can also be used for this purpose. Different requirements at

the environmental regulatory side (e.g. large amount of modelled buildings, fast model preparation,

limited simulation time and computer capacity) make simpler methods necessary, at the expense of more

limited accuracy. The micrometeorological flow and dispersion models fulfil the mentioned requirements.

The COST Action 732 is addressed to the improvement and quality assurance of micro scale obstacle

accommodating meteorological models and their application to the prediction of flow and transport

processes in urban or industrial environment. Within the framework of this COST action an exercise is

performed where several groups run models applying the Best Practice Guideline for the CFD Simulation

of Flows in the Urban Environment (Franke et al., 2007) to evaluate the models against the

measurements. The experimental data set comes from the Mock Urban Setting Test (MUST) where wind

field and dispersion experiments were conducted for several wind directions in a 10x12 array of 120

standard 40-feet containers (LWH = 12.2x2.4x2.54m) settled in flat terrain, thus simulating an idealised

city (see Figure 1). A detailed description of the test is given by Yee and Biltoft (2004). The experiment

was also measured in a wind tunnel and an extensive validation dataset was produced by Leitl et al.

(2007). The pollutant transport research group at the BME performed simulations of flow within this

idealised city using two commercial CFD codes. MISKAM (Eichhorn, 2003) is a special code for

simulating flow and dispersion processes in urban environment, widely used for regulatory purposes in

Europe, while FLUENT is a dominant, general purposed CFD code. This paper presents the results of

flow simulations in the 0 degree MUST case, which means that the flow is perpendicular to the longer

side of the containers (see Figure 1). Results are compared to the wind tunnel measurements and several

metrics are determined for validation.

2 Used models, grids and simulation parameters

Both FLUENT and MISKAM solve the Reynolds-averaged Navier-Stokes equation using turbulence

models to take the unsteady effect of turbulence into account. Table 1 summarizes all important

parameters of the codes and of the simulations.

The FLUENT simulations ran on the same grid in full scale (F-FS) and wind tunnel scale (F-WTS)

and with different reference velocities.

Because MISKAM uses a non-equidistant Cartesian grid, from which buildings are simply blocked

out, in case of the coarse MISKAM grid (M-1) both the position and the size of the containers had to be

adapted. In case of two finer MISKAM grids (M-08, M-05) only the position had to be adapted due to the

Cartesian grid.

The inlet boundary conditions of FLUENT were taken from wind tunnel measurements. MISKAM

generated its own inlet profile by the assumption of an equilibrium boundary layer for a given reference

velocity Uref at a given height Zref, resulting in a logarithmic velocity profile.

Goricsan et al. (2011):

Simulation of flow in an idealised city using various CFD codes 361

Table 1 Model and grid description, boundary conditions and simulation parameters

Simulation name F-WTS-1 F-WTS-10 F-FS-1 F-FS-10 M-1 M-08 M-05

numerical code used

turbulence model

wall treatment

velocity-pressure correlation

advection scheme

model scale

grid type

number of grid cells [million] 1.3 2 4.8

domain area

domain height 21m

distance of buildings from boundary as a 23/33/20/7

multiple of container height (in/out/side/top)

average grid resolution at the containers 1x1x0.5m 0.8x0.8x0.5m 0.5x0.5x0.5m

as a multiple of container height (approx.) 0.4x0.4x0.2 0.3x0.3x0.2 0.2x0.2x0.2

max. cell growth rate

Inlet velocity profile

reference height zref

reference velocity at zref 1 m/s 10 m/s 1 m/s 10 m/s

inlet turbulence profile

ground surface and wall boundaries

surface roughness

building roughness

top boundary

outlet boundary

logarithmic profile with roughness length

z0=0.02m

symmetry constant values taken from the top of the inlet

profile

0.01m0.017m 0.017m

0.017m 0.017m

7.29m

Model description

0.02m

1.21.2

no-slip

1 m/s

generated from the assumption of equilibrium

boundary layer

no-slip

obtained from wind tunnel measurement

obtained from wind tunnel measurement

splitting method by Patrinos and Kistler (1977)

FLUENT 6.3.26 MISKAM 5.01

realizable k-ε (Shih et al., 1995)

non-equilibrium wall function (Kim, 1995)

modified k-ε (Kato and Launder, 1993 and

López, 2002)

logarithmic wall function

SIMPLE method

outflow

0.24x0.51x0.1

97.2mm 7.29m

no-flux with pressure correction

Computational domain and grid

Boundary conditions

300x314m

130m

4x4.19m 300X314m

full scale

8x17.3x3.6 mm 0.6x1.3x0.27m

1st order upwind2nd order upwind

wind tunnel scale (1:75) full scale

Arakawa-C non-equidistant Cartesian grid,

with buildings blocked out from the grid

block structured, body fitted with hexahedral cells

23/33/20/5023/33/20/7

1.5

0.28m 21m

3 Results

The results are presented here through velocity profiles, contour plots and validation metrics. The place of

velocity profiles and contour plots are adjusted to the wind tunnel measurements, therefore the values are

compared with values obtained from wind tunnel measurements normalized to the reference velocity Uref .

3.1 Vertical velocity profiles

Vertical profiles of U and W velocity components at 21 positions among the array of obstacles were

compared to the measurement. Here only a few of them can be shown in Figure 1 and 2. Note that

container height corresponds to Z/Zref = 0.35. The following observations can be made:

• the full scale FLUENT simulations (F-FS) provide closer horizontal velocity component values

than the wind tunnel scale (F-WTS);

• reference velocity has no effect (F-FS-1 and F-FS-10 are almost similar);

• the fine grid MISKAM run (M-05) slightly underpredicts the longitudinal velocity component U,

but gives in most profiles a better agreement than the coarse one, showing that grid resolution has

considerable effect in the MISKAM simulations;



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Figure 1 Vertical velocity profiles for selected cases with profile positions



• the negative vertical velocity W, observed in the wake of obstacles, which is a consequence of the

top separation bubble above the obstacles, can be seen clearly in wind tunnel profiles (3, 9, 18) but

is only partially shown by FLUENT and is not resolved by MISKAM. This was also observed in

MISKAM simulations of the flow above an array of buildings by Eichhorn (2004), and can be

explained by the suppression or the absence of the top separation bubble which is a known

problem of k-ε turbulence models.

Figure 2 Velocity profiles in the middle of the array of obstacles (For position see Fig. 1)

3.2 Velocity field in horizontal plane

Wind tunnel and simulation results in a horizontal cross section are shown in Figure 3.

Points and vectors show the wind tunnel data, the contour and the stream-traces represents the

MISKAM (M-1) and FLUENT (F-FS1) values at about 1/3 of the container height. The flow can be

clearly separated in two regions: (a) unhindered flow in the longitudinal street; (b) separation area behind

the containers. It can be observed that:

• both models resolve the two regions well as it can be seen from the stream-traces;

• the vectors are mostly parallel to the stream-traces, fore-spoken that beside the absolute values, the

direction of simulated velocity vectors are correct.

• Comparison of the contour colour and the colour of the measurement dots shows similarity of the

longitudinal U and cross-flow V velocity components. FLUENT seems to agree better, while

MISKAM is underestimating the U component in the wake and gives longer separation bubble

than the FLUENT simulation (Figure 3, top left and right).

• Both models predict cross-flow V velocities on lower magnitude, especially at container corners.

The observations made above and at the vertical profile diagrams suggest that simulated flow

velocities in the wake of obstacles are smaller in magnitude and air exchange between the separation and



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the free flow is –especially in MISKAM simulations– weaker. This would mean e. g. that in later

dispersion simulations based on these wind fields an unrealistic high accumulation of pollutants can be

expected if sources are located near the surface between buildings.

Figure 3 Velocity in Z=0.9 m plane, comparison of wind tunnel and MISKAM/FLUENT data

3.3 Validation metrics

Flow variables obtained from MISKAM and FLUENT runs were validated against the wind tunnel

experiments in 566 points of vertical profiles using the BOOT software (Chang and Hanna, 2004), which

calculates the metrics shown in Table 2.



Table 2 Determining the membership function for various validation metrics

Validation metrics (ideal value) Range Membership function

-0.3<FB<0.3 Good 1<FB<1.2 Fair, Underestimation

-1.2<FB<-1 Fair, Overestimation

FB, Fractional Bias (0)

FB<-1.33 or FB>1.33 Poor

NMSE<4 Good 9<NMSE<16 Fair

NMSE, Normalized Mean Square Error (0)

25<NMSE Poor

R>0.8 Good

0.8>R>0.5 Fair

R, Correlation Coefficient (1)

R<0.5 Poor

0.5<FAC2 Good 0.3<FAC2<0.4 Fair

FAC2, within a factor of two (1)

FAC2<0.2 Poor

Typical magnitudes of these performance measures and estimates of model acceptance criteria have been

summarized by many investigators including Chang and Hanna (2004), Ziomas, Tzoumaka & Balis

(1998), Zawar-Reza (2005) and Park and Seok (2007).

Based on validation metrics shown in Table 3, the full scale FLUENT simulations (F-FS-1 and F-FS-

10) delivered the best results, followed by the finest grid MISKAM simulation (M-05). Underprediction

of W (hit rate below the limit of 0.66 given e.g. in VDI, 2005) is a common problem of both models (see

also Figure 4), as it was explained in section 3.1. Opposed to this the metrics of mean flow velocity U

fulfil all acceptance criteria for all simulations, while turbulent kinetic energy TKE is underpredicted.

Table 3 Collected validation metrics calculated for vertical profile measurements

|R| FAC2 FB NMSE Hit rate |R| FAC2 FB NMSE Hit rate |R| FAC2 FB NMSE Hit rate

F-WTS-1 0.92 0.86 0.72 0.90 0.25 0.20 0.75 0.50 0.64 0.67 0.16

F-WTS-10 0.94 0.86 0.72 0.89 0.35 0.27 0.75 0.42 0.74 0.88 0.13

F-FS-1 0.94 0.88 0.64 0.89 0.41 0.31 0.80 0.78 0.40 0.28 0.41

F-FS-10 0.95 0.90 n.a. n.a. 0.66 0.87 0.39 n.a. n.a. 0.25 0.79 0.80 0.34 0.23 0.48

M-1 0.97 0.89 0.81 0.83 0.12 0.15 0.66 0.44 0.78 1.05 0.02

M-08 0.96 0.90 0.79 0.83 0.12 0.14 0.65 0.47 0.74 0.95 0.04

M-05 0.95 0.91 0.75 0.74 0.31 0.20 0.69 0.67 0.54 0.52 0.24

U/Uref W/Uref TKE/Uref2



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Figure 4 Simulated versus measured data in case of the F-FS-1 and the M-05 simulations

4 Summary

This paper presents results of FLUENT and MISKAM numerical simulations of flow in an idealised city

(MUST experiment). Both codes are able to resolve main features of the flow as it was proven by data

visualization of the results and also by validation metrics.

On the other hand, not all details of the flow measured in wind tunnel could be recognized in the

simulations. While FLUENT with realizable k-ε closure performed slightly better, both models need

further improvement. Application of higher order schemes for advection (MISKAM), changes in the

turbulence closure and the use of dense computational grids can help to overcome these weaknesses.

Until then, wind tunnel measurements are also necessary to give fully trusted answers for micro scale

flow problems in urban environment.

Acknowledgements

The authors would like to thank for the support of the NKFP 3A/088/2004 project. The MUST data set

has been provided by the Defence Threat Reduction Agency (DTRA) / Dugway Proving Ground (DPG)

for use in COST732.

References

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Britter, R., Schatzmann, M. (eds.) (2007) Background and Justification Document to Support the Model Evaluation Guidance and Protocol, COST Action 732, COST Office: Brussels.



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