Cooperative project for CFD prediction of pedestrian wind

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Journal of Wind Engineering
and Industrial Aerodynamics 95 (2007) 1551–1578
www.elsevier.com/locate/jweia
Cooperative project for CFD prediction
of pedestrian wind environment in the
Architectural Institute of Japan
R. Yoshiea,, A. Mochidab, Y. Tominagac, H. Kataokad,
K. Harimotoe, T. Nozuf, T. Shirasawag
a
Department of Architecture, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297 Japan
b
Graduate School of Engineering, Tohoku University, Japan
c
Niigata Institute of Technology, Japan
d
Technical Research Institute, Obayashi Corp., Japan
e
Technology Center, Taisei Corp., Japan
f
Institute of Technology, Shimizu Corp., Japan
g
The University of Tokushima, Japan
Available online 23 April 2007
Abstract
CFD (computational fluid dynamics) is being increasingly applied to the prediction of the wind
environment around actual high-rise buildings. Despite this increasing use, the prediction accuracy
and many factors that might affect simulation results are not yet thoroughly understood. In order to
clarify ambiguities and make a guideline for CFD prediction of the wind environment, a working
group was organized by the Architectural Institute of Japan. This group has carried out various
comparative studies as follows.
First stage: Flow fields around two types of single high-rise buildings.
Second stage: Flow field around a high-rise building located in a city.
Last stage: Flow fields around two types of Building Complexes in actual urban areas.
This paper describes some of the results of the investigation by the working group, and discusses
the influences of various calculation conditions on CFD results, and also on the present status and
the problems in CFD prediction of the wind environment.
r 2007 Elsevier Ltd. All rights reserved.
Keywords: CFD; Pedestrian wind environment; RANS model; Guideline
Corresponding author. Tel./fax: +81 46 2429545.
E-mail addresses: [email protected] (R. Yoshie), [email protected] (Y. Tominaga).
0167-6105/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jweia.2007.02.023
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Nomenclature
U
V
W
k
Pk
average wind velocity in stream-wise direction (x direction), m/s
average wind velocity in transverse direction (y direction), m/s
average wind velocity in vertical direction (z direction), m/s
Turbulent kinetic energy, m2/s2
Dissipation rate of turbulent kinetic energy, m2/ s3
Production of turbulent kinetic energy, m2/s3
1. Introduction
Progress in high-speed processing by personal computer and rapid propagation of
software for numerical analysis of fluid dynamics in recent years have enabled prediction
of the pedestrian wind environment around high-rise buildings based on CFD
(computational fluid dynamics). It is becoming common for calculations to be performed
for 16 wind directions of the situation before and after construction of proposed buildings,
and for the pedestrian wind environment to be assessed by probability evaluation.
However, there have been very few reports on the prediction accuracy of CFD simulations
of the pedestrian wind environment around buildings in urban areas. Furthermore, the
influences of various calculation conditions (such as size of computational domain, grid
resolution, boundary conditions, selection of turbulence model, etc.) on the results of CFD
simulation are not yet thoroughly understood.
Thus, a working group named ‘‘Working Group for CFD Prediction of the Wind
Environment around a Building’’ has been organized by the Architectural Institute of
Japan. The name of this working group has been subsequently changed to ‘‘Working
Group for Preparation of Wind Environment Evaluation Guideline based on CFD’’.
Since its inception, it has been making continuous efforts to prepare guidelines for proper
use of CFD for calculation of the wind environment. Comparative and parametric studies
have been carried out on several building configurations to elucidate the problems on
setting or selecting various calculation conditions and turbulence models for CFD
simulation of the pedestrian wind environment in urban areas. Although there have been
the recommendations with similar objectives proposed by COST group (Franke et al.,
2004), those are mainly based on the results published by other authors. On the other
hand, we are intended to propose the guidelines based on the results of our own
benchmark tests.
The present article introduces some of the results achieved by the working group and
discusses the influence of calculation conditions and turbulence models on CFD
calculation results and also on the present status and problems in CFD prediction of
the pedestrian wind environment around buildings.
2. General features of comparative and parametric studies
Figs. 1–6 show models for comparative and parametric studies as investigated by the
working group. The results of studies will be introduced here on the flow field around a
single square prism of 2:1:1 (height:width:depth) placed in a turbulent boundary layer
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b
Wind
H = 2b
b
Fig. 1. Single high-rise building (2:1:1 square prism).
wind
4b
4b
b
Fig. 2. Single high-rise building (4:4:1 square prism).
wind
Z
0.2m
Y
X
0.2m
0.2m 0.2m
Fig. 3. Simple city block.
Fig. 4. A high-rise building in city.
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Fig. 5. Building complexes in actual urban areas (Niigata).
Fig. 6. Building complexes in actual urban areas (Shinjuku).
flow (Fig. 1), the flow field around a high-rise building located in a city (Fig. 4), and flow
fields around building complexes in actual Urban areas in Niigata and Shinjuku, Japan
(Figs. 5 and 6).
In the studies discussed here, the standard k– model or modified k– models or DSM
were used, but LES (large eddy simulation) was not applied except for flow fields around
two types of single prisms (Figs. 1 and 2). It is desirable to use LES for highly accurate
CFD. However, it is very difficult to use because it requires a lot of time for calculation in
practical analysis due to the limited conditions of computer resources currently available.
This is because, prediction and evaluation of the wind environment around buildings in
practical application requires a wide computational domain including surrounding
building groups and a vast number of grids associated with it. In addition, a number of
calculation cases (such as multiple wind directions, situations before and after construction
of a proposed building, and measures after construction) are required, and time for
evaluation is also limited in the practical design stages. Therefore, the guidelines currently
under preparation in the working group are also based on the assumption that the analysis
is performed using standard k– model or modified k– models.
3. Flowfield around a single square prism of 2:1:1
3.1. Outline of wind tunnel experiment
In the comparative and parametric studies on the flow around a square prism of 2:1:1 as
carried out in the first step of the working group’s investigation, the experimental results by
Meng and Hibi (1998) were used to validate the results of the CFD simulation. In this
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Fig. 7. Configuration of experimental model.
experiment, a detailed measurement was made of the flow field around a 2:1:1
(height:width:depth) shaped square prism placed in a turbulent boundary layer, in which
the exponent for the power law of the vertical profile of average wind velocity was
approximately 0.27 (Fig. 7). A split film probe was used to measure wind velocity, and the
average wind velocity in each direction of three-dimensional space and the standard
deviation of fluctuating wind velocities were determined. The model building was 0.08 m
square (b and d) and 0.16 m high (h). The turbulence statistics were measured on a vertical
1
cross-section (Fig. 8(a)) and on horizontal planes at 16
ðz=b ¼ 0:125Þ and 10
16 ðz=b ¼ 1:25Þ of
the building height (Fig. 8(b)).
3.2. Calculation conditions for comparative study (standard calculation conditions)
In the working group, the conditions shown in Table 1 and Figs. 9 and 10 were given as
the standard calculation conditions for the comparative studies (hereinafter referred as
‘‘standard calculation conditions’’). In addition to the standard calculation conditions, we
investigated the influence on the calculation results of changing the boundary conditions,
the computational domain, the grid resolution, and the turbulence models, etc.
3.3. Results of calculation by standard k– model based on standard calculation conditions
The CFD results utilizing the standard k– model and based on the standard calculation
conditions are compared below with experimental results of average wind velocities.
(1) Wind velocity distribution on vertical cross-section: The distribution of average wind
velocity U and W on the vertical cross-section at the center of the building is shown in
Figs. 11 and 12. In the figures, the longitudinal dotted lines represent the positions of the
measuring lines in the experiment. Wind velocities are plotted transversely using this as the
origin. (Positive values are plotted on the right side of the measuring line, and negative
values on the left side.) The calculated values agree fairly well with the experimental values.
However, near the roof surface of measurement line x=b ¼ 0:25 (the third measuring line
from the left), U is negative in the experiment and reverse flow occurs, but this is not
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Fig. 8. Outline of wind tunnel experiment. (a) Measuring points in vertical cross-section ðy ¼ 0Þ. (b) Measuring
points in horizontal plane (z ¼ 0:125b and 1:25b).
Table 1
Standard calculation conditions
Computational domain
Grid resolution
Scheme for advection term
Building wall surface
Surface of wind tunnel side wall
Surface of wind tunnel ceiling
Surface of wind tunnel floor
Outflow boundary condition
Inflow boundary condition
21b ðxÞ 13:75b ðyÞ 11:25b ðzÞ
60 ðxÞ 45 ðyÞ 39 ðzÞ ¼ 105; 300 mesh (Fig. 9). The building was
discretized into 9 9 15.
Quick scheme for U, V , W , k, Logarithmic law for smooth surface wall
Logarithmic law for smooth surface wall
Logarithmic law for smooth surface wall
Logarithmic law with roughness length z0 (z0 ¼ 1:8 104 m)
Zero gradient condition
Interpolated values of U and k from the
experimental approaching flow.
¼ C m1=2 k dU=dz ð ¼ Pk Þ (Fig. 10)
C m ¼ 0:09
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y
Side wall of wind tunnel
Side wall of wind tunnel
x
z
Ceiling of wind tunnel
x
Floor of wind tunnel
Fig. 9. Computational domain and grid arrangement. (a) Horizontal plane. (b) Vertical section.
ceiling
12
10
8
Inflow B.C.
6
Exp.
height form floor z/b
height form floor z/b
10
4
2
8
Inflow B.C.
Exp.
6
4
Inflow B.C.
8
6
4
0
0
0 1 2 3 4 5 6 7
U (m/s)
10
2
2
0
ceiling
12
ceiling
height form floor z/b
12
0.0 0.2 0.4 0.6 0.8
k (m2/s2)
0
1 2 3 4 5
ε (m2/s3)
Fig. 10. Inflow boundary conditions.
reproduced in the calculation. On the lower portion of measuring line x=b ¼ 3:25 (the
rightmost measuring line), the calculated U is lower than the experimental value.
(2) Wind velocity distribution on a horizontal cross-section: The distributions of U and V
on the horizontal plane ðz=b ¼ 0:125Þ near the ground surface are shown in Figs. 13
and 14, respectively. The calculated values and the experimental values agree relatively well
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Exp
U (m/s)
CFD
0
5 m/s
4
z /b
3
2
1
0
−1
0
1
2
3
4
x /b
Fig. 11. Distribution of U in vertical section ðy ¼ 0Þ.
Exp
CFD
W (m/s)
0
1
0
2 m/s
4
z/b
3
2
1
0
−1
2
3
4
x /b
Fig. 12. Distribution of W in vertical section ðy ¼ 0Þ.
except that the calculated U is lower than the experimental value in the wake region. The
reattachment length behind the building is longer in the calculation.
(3) Wind speed increase ratios near the ground surface: Fig. 15 compares the experimental
and calculated scalar wind velocity. The scalar wind velocity (wind speed) near the ground
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Exp
CFD
U (m/s)
0
1
0
5 m/s
y/b
0
1
2
−1
2
3
4
x /b
Fig. 13. Distribution of U in horizontal section ðz ¼ 0:125bÞ.
Exp
CFD
V (m/s)
0
2 m/s
y/b
0
1
2
−1
0
1
2
3
4
x/b
Fig. 14. Distribution of V in horizontal section ðz ¼ 0:125bÞ.
surface ðz ¼ 0:125bÞ is normalized according to the wind speed at the same height when
there is no building (i.e. wind speed increase ratio). The results based on the modified k–
models (Launder–Kato (LK) type k– model (Kato and Launder, 1993), and ReNormalization Group (RNG) k– model by Yakhot and Orszag (1986)) are also given. If it
is limited to the region where the wind speed has increased (region where wind speed
increase ratio is 1.0 or more), which is important in the evaluation of the pedestrian
wind environment, it is predicted within an accuracy of approximately 10%. However, in
the weak wind region behind the building, the wind speed ratio is evaluated lower
in the calculation than in the experiment. When the modified k– models are compared
with the standard k– model, prediction accuracy is slightly higher in the modified models
in the strong wind region, while it is lower in the weak wind region. The reattachment
length behind the building is longer in the modified k– models than in the standard k–
model.
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1.6
Standard
k-ε model
1.4
x/b = -0.75
x/b = -0.5
x/b = -0.25
x/b = 0
x/b = 0.5
x/b = 0.75
x/b = 1.25
x/b = 2
x/b = 3.25
± 0%
± 1 0%
Calculation
1.2
1
0.8
0.6
0.4
0.2
In front of building
Side of building
Behind building
0
0
0.2 0.4 0.6 0.8 1
Experiment
1.2 1.4 1.6
1.6
1.6
LK k-ε
model
RNG k-ε
model
1.4
1.2
1.2
1
1
Calculation
Calculation
1.4
0.8
0.6
0.8
0.6
0.4
0.4
0.2
0.2
0
0
0
0.2 0.4 0.6 0.8 1
Experiment
1.2 1.4 1.6
0
0.2
0.4 0.6 0.8 1
Experiment
1.2 1.4
1.6
Fig. 15. Wind speed increase ratio near floor ðz ¼ 0:125bÞ.
3.4. Influence of various calculation conditions on the calculated results
Here, the results obtained by changing the calculation conditions are summarized
without diagrams. Furthermore, the findings obtained from the benchmark test on the flow
field around a square prism of 4:4:1 (Fig. 2) are also given. Detailed information on the
studies is in the following references; Working Group for CFD Prediction of Wind
Environment Around Building (2001), Mochida et al. (2002); Shirasawa et al. (2003) and
Tominaga et al. (2003, 2004).
(1) When the modified k– models were used, a reverse flow on the roof was reproduced,
and the prediction accuracy in the strong wind region near the separation region closer to
the ground surface was improved. However, in the region behind the building, the
reattachment length was longer than for the standard k– model, and agreement with the
experimental results deteriorated.
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(2) When LES was performed, the prediction accuracy in the flow field behind the
building was dramatically improved. This is mainly because the periodic vortex shedding
behind the square prism was well reproduced by LES (Tominaga et al., 2003).
(3) When the calculation was conducted on a finer grid (by decreasing the grid width of
the standard calculation grid to 12), the results were very close to those for the standard
grid. Thus, the grid arrangement of the standard condition was sufficient.
(4) When the standard computational domain ð21b 13:75b 11:25bÞ was narrowed
down to a small region ð13:8b 7:56b 7:75bÞ, there was almost no change in the results.
(5) When k and in the inflow were varied, the calculated results varied widely. Thus, it
is important to provide appropriate values for inflow k and .
(6) When a first-order upwind scheme was used for the advection term, the velocity
distribution at the side region of the building, where wind enters the computational grids
diagonally, become less steep. This is not desirable.
(7) Comparative studies performed by unifying the calculation conditions showed
small differences between the calculated results for three commercial codes and two selfmade codes.
4. Flow field around a high rise-building located in a city
This chapter describes the results of the study on the flow field near a high-rise building
in a typical (regular) urban block.
4.1. General features of the wind tunnel experiment
The flow field analyzed here is that around a high-rise building in a simple urban area,
for which the wind tunnel experiment was carried at the Niigata Institute of Technology.
The low-rise urban block was assumed to be 40 m square and 10 m high as shown in Fig. 16
(simulating a condition where low-rise houses are densely packed), with a high-rise
building 25 m square and 100 m high (1:1:4) in a block at the center of this area. One urban
block is assumed to be enclosed by two roads (each 10 m wide) and roads 20 and 30 m
wide. This is one of the typical Japanese situations where a high-rise condominium is built.
Wind direction
0°
22.5°
20m road
30m road
45°
10m road
10m road
Fig. 16. General features of wind tunnel experiment.
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10m
0°
10m
45°
Experiment
Scale: 1/400
Measuring height :5 mm
40m
20m
40m
30m
40m
Fig. 17. Measuring points.
Table 2
Standard calculation conditions
Computational domain
Grid resolution
Scheme for advection term
Building wall and ground surface
Upper and side surface of computational domain
Turbulence model
Inflow boundary condition
Outflow boundary condition
1:8 m 1:8 m 1:8 m
(the size of the test section of the wind tunnel)
132 ðxÞ 130 ðyÞ 76 ðzÞ ¼ 1; 304; 160 mesh (Fig. 18)
Quick scheme for U, V , W , k, Logarithmic law for smooth surface wall
Free slip wall condition (symmetric plane)
Standard k– model
Interpolated values of U and k from
the experimental approaching flow
¼ C m1=2 k dU=dz ð ¼ Pk Þ, C m ¼ 0:09
Zero gradient condition
The wind velocity measuring points are shown in Fig. 17. The scale of the experimental
1
model was 400
and the measuring height was 5 mm above the floor of the wind tunnel (2 m
above ground in real scale). Wind velocity was measured in three wind directions (0 , 22:5
and 45 ) using a thermister anemometer. In addition, for wind direction 0 only, the wind
velocity was measured using a split film probe. The inflow wind velocity U H at the height
of the central high-rise building H (H ¼ 250 mm in the experiment and 100 m in real scale)
was 6:6 m=s.
4.2. General outline of calculation
The problems with the CFD analysis on the urban area, as described above, are: (1)
How wide should the computational domain be maintained in the horizontal and vertical
directions? (2) How fine should the grid resolution be? (3) To what extent should the
surrounding urban blocks be reproduced? (4) What model should be used as a turbulence
closure? Based on the standard calculation conditions shown in Table 2 and Fig. 18, the
prediction accuracy of CFD simulation was examined. By varying the calculation
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1.8m
1.8m
y
x
b
High-rise building
was divided into
12(x) 12(y) 27(z)
Fig. 18. Computational domain and grid resolution for standard calculation condition. (a) Whole computational
domain and grid resolution. (b) Macrograph of central area.
conditions of (1)–(4), the influences of the calculation conditions on the CFD results were
investigated.
4.3. Comparison of CFD results with experimental results based on standard calculation
conditions
The calculated results based on the standard calculation conditions and the experimental
results are compared in Fig. 19(a) (wind direction 0 ) and in Fig. 19(b) (wind direction
45 ). (In these figures, experimental data at measuring points very close to the high-rise
building (points 26–29 and 40–43) were omitted because their reliability was considered to
be low.) The wind speed ratio between the scalar wind velocity and U H at the measuring
point is represented on the ordinate. At measuring points 35, 38 where the wind velocity
was highest for wind directions 0 and 45 , the calculated results were about 15% lower
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Wind Speed Ratio
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1
CFD
Exp.
0.8
0.6
0.4
0.2
0
Wind Speed Ratio
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75
Measuring Point No.
1
CFD
Exp
0.8
0.6
0.4
0.2
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75
Measuring Point No.
Wind Speed Ratio
Fig. 19. Comparison between CFD based on standard calculation conditions and experiment. (a) Wind
direction ¼ 0. (b) Wind direction ¼ 45 .
0.8
Small(1.5m×1.5m)
Standard(1.8m×1.8m)
Larqe(3.6m×3,6m)
0.6
0.4
0.2
0
0
5
10
15 20 25 30 35 40 45 50 55
Measuring Point No.
60 65 70 75
Fig. 20. Influence of horizontal computational domain.
than the experimental results, while relatively good matching was observed for the other
strong wind regions.
4.4. Influence of various calculation conditions on CFD results
(1) Influence of size of horizontal computational domain: The calculation was carried out
in experimental scale. To evaluate the influence of the horizontal computational domain, it
was expanded from the standard domain of 1:8 m 1:8 m to one of 3:6 m 3:6 m, and
contracted to one of 1:5 m 1:5 m, which is near the rim of the surrounding block. The
results are shown in Fig. 20. When the horizontal computational domain was large
ð3:6 m 3:6 mÞ, the wind speed tended to decrease slightly with the decrease of the
obstruction ratio in the horizontal direction. On the other hand, when the calculation was
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0.8
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Low(2H)
Middle(3H)
Standard(7.2H)
0.6
0.4
0.2
0
0
5
10
15 20 25 30 35 40 45 50 55
Measuring Point No.
60 65 70 75
Wind Speed Ratio
Fig. 21. Influence of vertical computational domain.
0.8
Coarse mesh
Standard mesh
Fine mesh
0.6
0.4
0.2
0
0
5
10 15 20 25
30 35 40 45 50 55 60 65 70 75
Measuring Point No.
Fig. 22. Influence of grid width.
carried out in the smaller domain ð1:5 m 1:5 mÞ, the wind speed became higher. The
horizontal computational domain has a considerable influence on the results. It is
somewhat difficult to interpret this result to real situation. We think if the similar urban
area is spread outside of the computational domain, side space of the computational
domain should be narrow. But if the outside of the urban area is open terrain, the
computational domain should be large.
(2) Influence of vertical computational domain: Fig. 21 shows the results obtained when
the vertical computational domain was lowered from the standard height of 7:2H to 3H
and 2H. When the upper computational domain was 2H, the wind speed was just slightly
higher. There was almost no difference between the cases of 7:2H and 3H. It appears that
no substantial problem occurred even when the vertical computational domain was
lowered to about 3H.
(3) Influence of grid resolution: Fig. 22 shows the calculated results when a fine grid and a
coarse grid were used. For the fine grid (215ðxÞ 202ðyÞ 101ðzÞ ¼ 4; 386; 430 grids), the
1
grid width was set to about 1:5
of the standard grid in all three directions x, y and z. For the
coarse grid (74ðxÞ 68ðyÞ 48ðzÞ ¼ 241; 536 grids), it was about 1.5 times the standard
grid. The difference between the calculated results for the standard grid and the fine grid
was very small. The difference between the calculated results for the coarse grid and the
other cases was also small. The standard grid would be satisfactory, i.e. with each side of
the high-rise building divided into 10 portions or more.
(4) Influence of reproduction range of surrounding urban blocks: Fig. 24 shows the results
of calculation with two rows and three rows each deleted from the peripheral region of the
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Wind Speed Ratio
Fig. 23. Reproduction range of surrounding urban blocks. (a) Deleting 2 rows. (b) Deleting 3 rows.
0.8
Standard
Deleting 2 rows
Deleting 3 rows
0.6
0.4
0.2
0
0
5
10 15
20
25 30 35 40 45 50 55
Measuring Point No.
60
65 70 75
Wind Speed Ratio
Fig. 24. Influence of reproduction range of surrounding urban blocks.
1
Standard k-ε
0.8
RNG k-ε
Exp.
0.6
0.4
0.2
0
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75
Measuring Point No.
Fig. 25. Influences of deferent turbulence models.
surrounding urban blocks, as shown in Fig. 23. The difference from the standard case was
very small except at measuring points 1, 2, 3 and 4 on the roads on the windward side.
Therefore, the reproduction range of the surrounding urban blocks would be satisfactory
for practical application if at least one block was maintained in the surroundings of the
region to be evaluated (Figs. 23 and 24).
(5) Influence of modification on turbulence modelling: Fig. 25 shows calculated results
based on the modified k– model (RNG k– model). At measuring points 35, 38, etc.,
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where the wind speed was high, the results from the modified k– model was higher than
that of the standard k– model, and matching with the experimental results was improved.
However, in the weak wind regions such as at measuring points 15–25, where the wind
speed was low, matching with the experimental results deteriorated.
4.5. Summary
According to the results of the present study, the influence on the calculated results of
the computational domain, the grid resolution, and the reproduction range of the
surrounding urban block was relatively low. In the calculation for practical applications,
the criteria would be as follows: at least one urban block of several tens of meters
should be reproduced in the area surrounding the region to be evaluated, and the upper
space region should be maintained at 3H or more, and each side of the high-rise building
should be divided into 10 portions or more. And when the similar urban block is
spread outside of the computational domain, side space of the computational domain
should be narrow, but when the outside of the urban area is open terrain, the
computational domain should be large. If it is limited to the highest wind region, which
is important for evaluation of the pedestrian wind environment around the building, the
wind speed difference between CFD and experiment was about 15% at most for
the standard k– model. For the RNG model, more accurate prediction can be made in the
strong wind region.
5. Flow field in an actual urban area (Niigata)
Up to this point, we have reported results of bench mark tests for relatively
simple shapes such as single buildings and relatively regular model urban blocks. In
actual urban areas, however, buildings have complicated shapes and are distributed
in an irregular manner. In order to accurately reproduce them in a CFD simulation,
a great number of grids are required. In particular, when an orthogonal structured grid
system is used, it is difficult to provide a grid configuration that matches well with
the building configuration, and special care must be taken to allow for the influence
of mismatching on the prediction accuracy. This problem can be solved if an
unstructured grid system is used, although there are some difficulties in preparing this
kind of grid.
Furthermore, for the wind environment in urban areas, prediction is normally made for
16 wind directions on two patterns, i.e. before and after the construction of the proposed
building. In some situations, it is necessary to consider protection against wind such as
planting trees. This increases the number of cases that need to be considered, and
calculation accuracy must be maintained under restrictive conditions for practical
application.
This chapter describes a study in an actual urban area in Niigata, where low-rise houses
are packed closely together. Wind tunnel experiments and CFD simulation were
performed to predict the wind speed distribution and to assess the pedestrian wind
environment, and the results were compared. Furthermore, the differences are shown
between three types of grid system: a single structured grid system (orthogonal mesh), an
overlapping structured grid system, and an unstructured grid system (Tominaga et al.,
2004).
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Fig. 26. Actual urban model (CAD data) and measuring points.
Table 3
Common calculation conditions specified
Inflow boundary
Computational domain
Upper surface of computational domain
Outflow boundary condition
Ground surface boundary
Building surface boundary
Interpolated values of U and k from the experimental
approaching flow. ¼ CD1=2 k dU=dz ¼ PkÞ
Area about 500 m ðxÞ 500 m ðyÞ 300 m ðzÞ that includes
the whole urban block
Free slip wall condition (symmetric plane)
Zero gradient condition
Logarithmic law with roughness length z0 ðz0 ¼ 0:024 mÞ
Logarithmic law for smooth wall
5.1. Urban area model under study and outline of wind tunnel experiment
The study was performed on an urban area model. This model consisted of an actual city
block in Niigata city, Niigata prefecture, Japan with low-rise houses packed closely
together. A target building 60 m high (Building A) and two target buildings 18 m high
(Buildings B and C) were assumed to be constructed 26 (Fig. 26). Wind tunnel experiments
1
at 250
scale were performed on this model in a turbulent boundary layer with a power law
exponent of 0.25. Scalar wind velocities at 8 mm above the wind tunnel floor (2 m above
the ground surface in real scale) were measured by multi-point thermister anemometers.
5.2. Outline of numerical calculation
The items shown in Table 3 were specified as common calculation conditions in the
comparative study. For the configuration of the urban block, input data for each CFD
code were prepared from the same CAD data obtained from the drawing of the wind
tunnel model. The features of the compared CFD codes and the different calculation
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Table 4
Outline of CFD codes and calculation conditions
CFD Code
(1) Computational method and time
integral scheme.
(2) Turbulence model.
(3) Scheme for advection term.
Code O (Self-made code)
(1) Overlapping structured grid, artifical
compressibility.
(2) Standard k2.
(3) Third-order upwind.
Code M (Commerical code:
STREAM for Windows)
(1) Structured gird, SIMPLE, steady
state.
(2) Standard k2.
(3) QUIK.
Code T (Commerical code:
SCRYU/Tetra for Windows)
(1) Unstructured grid, SIMPLE, steady
state.
(2) Standard k2.
(3) MUSCL(2nd-order).
Grid arrangements
conditions are summarized in Table 4, as well as general features of the grid systems used
in each CFD code.
5.3. Results of numerical analysis
(1) Comparison based on wind speed ratio: CFD simulations were conducted for 16 wind
directions. Here, predicted results and experimental results of the wind speed ratio are
compared for the wind direction NNE, which is the wind direction most frequently
occurring in Niigata. The wind speed ratio is the ratio of wind speed (scalar velocity) at
each measuring point ðheight ¼ 2 mÞ and the wind speed at the same height at the inflow
boundary. There was no substantial difference among codes for overall distribution of
wind speed ratio. As a representative example, the distribution of wind speed ratio based
on CFD code T (Table 4) is shown in Fig. 27. Regions with very high wind speed were
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Fig. 27. Distribution of wind speed ratio near ground surface ðz ¼ 2 mÞ (Code T) (blue line indicates the wake
region).
found at the corners on the northwest and east sides of Building A. Furthermore, strong
wind caused by contraction flow was seen between Buildings B and C.
Fig. 28 represents the correlation of wind speed ratio obtained from CFD codes with the
results of wind tunnel experiments. Plotting is shown separately inside and outside the
wake region of the target buildings (The wake region is indicated in Fig. 27). The predicted
results in the CFD codes were almost identical, and there was no clear difference among
the codes. In the wake region of the target buildings, there was a tendency to underestimate
the wind speed compared with the results of the wind tunnel experiment, while in other
regions the matching was relatively satisfactory. The CFD results often tended to
underestimate the wind speed in the wake region of the building in benchmark tests on
other configurations.
Fig. 29 compares the wind speed ratios at each measuring point. For the positions of the
measuring points, see Fig. 26. The CFD results generally agree well with the results of the
wind tunnel experiment. In particular, the prediction accuracy is high ‘‘outside the wake
region & near building’’. The difference from experimental results was large at several
measuring points, but these were mostly in alleys in the wake region of the target buildings.
This may be because a slight difference in the prediction of the separation shear layer due
to the difference in reproducibility of the building configuration may have influenced the
calculated results, and because there were shape errors in the actual wind tunnel model and
CAD data used, and also errors in the positions of the evaluation point.
(2) Comparison based on criteria for assessing wind environment: A rank evaluation was
performed on the CFD predicted results according to criteria for assessing the wind
environment proposed by Murakami et al. (1986). This was based on the occurrence
frequency of daily maximum gust wind speed using observation data from the Niigata
Regional Meteorological Observatory. The results are summarized in Fig. 30. The gust
factor GF was assumed to be 2.5 to convert the calculated average wind speeds to the
maximum gust wind speeds. Including the results at the measuring points with rank 4 on
the northeast and south sides of Building A, the CFD results generally showed good
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1.4
1.2
CFD
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4 0.6 0.8
1
1.2 1.4
Experiment
1.4
1.2
CFD
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1
1.2
1.4
Experiment
1.4
1.2
CFD
1
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
Experiment
Fig. 28. Correlation of wind speed ratio between CFD result and experimental result (wind direction NNE): (a)
Code O; (b) Code M; (c) Code T.
matching with the wind tunnel experiment results. However, as described above, there were
differences between some of the experimental results and CFD codes for the measuring
points near low-rise houses in alleys or along large avenues.
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40
32
17
8
11
20
Code T
CodeM
1
63
Code O
42
30
27
24
3
14
78
69
66
54
51
48
35
72
62
58
Exp.
56
1.4
1.2
1
0.8
0.6
0.4
0.2
0
23
Wind speed ratio
1572
Fig. 29. Comparison of wind speed ratio at each measuring point (wind direction NNE).
Fig. 30. Comparison of rank for criteria for assessing wind environment at each measuring point.
6. Flow field in an actual urban area (Shinjuku)
6.1. General description of wind tunnel experiment and field measurement to be compared
To validate the CFD predicted results, it is important to compare them not only with the
wind tunnel experiments but also with field measurements. However, adequate field
measurement results are not always available, and this has almost never been performed in
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Fig. 31. Urban area reproduced and measuring points.
the past. Thus, a benchmark test was conducted on the Shinjuku Sub-central Area in
Tokyo, Japan in its early development stage, for which detailed wind tunnel experiments
and field measurements had been carried out in cooperation with many research
organizations during construction (Yoshida et al., 1978; Asami et al., 1978). Using these
data, the validity of the prediction accuracy of CFD was assessed.
(1) Wind tunnel experiment: A number of wind tunnel experiments had been performed
on the Shinjuku Sub-central Area. Here, the CFD simulations were carried out based on
the condition of the 1977 Experiment.
(2) Field measurements: Field measurements were carried out from December 1975 to
November 1983. The present CFD simulations were performed for conditions in 1977
when the situation of field measurements was similar to those of the wind tunnel
experiment model. In the field measurements, three-cup anemometers were used. The
measurement heights differed according to the measuring point, being 3–9 m above the
ground surface. The reproduced urban area and the measuring point distribution are
shown in Fig. 31. Measured wind speeds at encircled points (Nos. 6, 7, 13, 15) were
compared with the calculated ones.
6.2. Outline of numerical calculations
As for the study on the urban area of Niigata City, common calculation items were
designated. For the configuration of urban blocks, no wind tunnel experiment models at
the time of measurement were remained. Thus, we made CAD data of the urban area
configuration and topography based on the old drawings, photographs, and white maps in
1977. Because no details on the topographical height difference were available, the
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Fig. 32. CAD data of urban area.
Fig. 33. Grid arrangements of CFD codes: (a) CFD_A (Self made code); (b) CFD_B (SCRYU/Tetra for
Windows); (c) CFD_C (Fluent).
reproduction was made stepwise every 5 m by referring to the maps in 1977. The CAD data
thus prepared are shown in Fig. 32. Fig. 33 shows the grid arrangements of the CFD codes.
Here, only CFD_A uses a structured grid system, while the others are based on a nonstructured grid. However, CFD_A uses an overlapping grid.
6.3. Comparison of CFD results with wind tunnel experiment results and field measurements
Similar to the wind tunnel experiment and the field measurements, the wind speed
obtained by CFD was normalized by the wind speed at reference points. The reference
points were the top of the Shinjuku Mitsui Building (D in Fig. 31; height of observation
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Wind speed ratio
1.0
No. 6
0.5
0.0
NE
E
SE
S
SW W NW
Wind direction at reference point
N
Wind speed ratio
1.0
No. 7
0.5
0.0
NE
Wind speed ratio
1.0
E
SE
S
SW W NW
Wind direction at reference point
N
No.13
0.5
0.0
NE
E
SE
S
SW
W NW
Wind direction at reference point
N
Wind speed ratio
1.0
No. 15
0.5
0.0
NE
E
SE
S
SW
W
NW
Wind direction at reference point
N
Fig. 34. Comparison of wind speed ratio for 16 wind directions.
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1
field meas. (mean)
Normalized Velocity
field meas. (+σ)
0.8
field meas. (-σ)
wind tunnel
CFD_A
0.6
CFD_B
CFD_C
0.4
0.2
0
23
27
28
29
36
14
26
Measuring Point
7
33
13
15
Fig. 35. Comparison of wind speed ratio at each measuring point with wind direction S.
237 m) for the wind directions NE–N–NW, and the top of the KDD Building (C in Fig. 31;
height of observation 187 m) for the other wind directions. The measurement values for
reference wind speeds of 5 m/s or more were extracted from the observation data in the
year 1977, and the average value of normalized wind speed for each wind direction was
used as the measurement value.
Fig. 34 compares the experimental results and the field measurements for wind speed
ratios (normalized wind speeds) at representative measuring points. As a general trend, the
wind tunnel experiment results are within the standard deviation of the field measurements,
and the CFD results are also generally within the same range. The difference among
calculation results is relatively small regardless of the code and the grid system.
But the CFD results deviate from the experiment results and field measurements
depending on the measuring point and wind direction as follows.
(1) Measuring point 7, wind direction E: Calculated wind speed is higher than the measured
one. Measuring point 7 is located in the contracted flow region when the wind direction
is E. As mentioned above, the reproduction accuracy of the CAD data was not
necessarily sufficient. The small difference in position between CFD and measurement
might cause large difference in wind speeds between them in this contracted flow region.
(2) Measurment point 13, wind direction SSW– WSW: Calculated wind speeds are lower
than the measured ones. For these wind direction, the measuring point 13 is located in
wake region of the high-rise buildings. This underestimation in wake region is common
to that for the case of the 2:1:1 square prism and for the case of the actual urban area in
Niigata.
(3) Measurment point 15, wind direction NE: The measured wind speeds largely change
from wind direction NE to ENE, while the calculated ones largely change from NNE
to NE. The small deference in reference wind directions between measurement and
CFD might bring large difference in wind speeds.
Next, Fig. 35 compares the wind speed ratios at the measuring points with wind direction
S. In general, the difference among the results of the three CFD codes is small, and the
CFD results showed good matching with the experimental results and field measurements
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except at nos. 27, 28, 29, 36. These measuring points are located near the boundary of the
computational domain. We should have taken the horizontal computational domain
larger, and should have reproduced more urban blocks around these measuring points for
the better prediction.
7. Conclusion—current status and problems in CFD prediction
This paper has described some results of a study by the ‘‘Working Group for
Preparation of Wind Environment Evaluation Guideline based on CFD’’.
In general, prediction accuracy for the weak wind regions behind buildings was not
satisfactory, while prediction accuracy for the strong wind region was fairly high. In single
building models, which are considered to give the highest accuracy in the experiment, the
CFD analysis results were consistent with experimental results within an accuracy about 10%
in the strong wind region. However calculated U was lower than the experimental value in
the wake region behind the building. The reattachment length behind the building was longer
in the calculation. For the urban block model, which is considered to give the next most
accurate results in the experiment, the CFD analysis results in strong wind regions showed a
prediction accuracy within 10 or so % for the standard k– model and better accuracy for the
modified k– models. In actual urban area models, the CAD data for CFD simulation did
not completely match with experiment and field measurements, and it is difficult to
quantitatively describe the prediction accuracy. However, relatively good matching was
found in the strong wind region. The reason why the CFD analysis results underestimate the
wind velocity in the wake regions may be because it is not possible to reproduce the vortex
shedding in RANS type models such as the k– model. In LES, this can be improved, and the
maximum instantaneous wind velocity can also be evaluated in LES. However, in order to
use LES in general-purpose applications for predicting the wind environment around
buildings, we need a dramatic increase in computer processing speed in the future. For the
time being, we must be content with RANS type models currently in use. In RANS type
models, it is only possible to evaluate average wind velocity. To evaluate pedestrian wind
environment based on maximum gust wind speed we need to convert from average wind
speed to maximum gust wind speed based on the assumption of the gust factor.
Although there are problems as described above in the CFD analysis using the RANS
type turbulence models, it is advantageous that the detailed and overall spatial distribution
of wind velocity can be identified in CFD analysis, while only limited information on wind
velocity can be obtained from wind tunnel experiments. Strong wind points that may have
been missed in wind tunnel experiments may be identified by the CFD simulation. Further,
there are some uncertainties inherent in wind tunnel experiments (such as measuring
instrument errors, incidental errors, errors in the position where the sensor is installed,
etc.), but there are no such uncertainties in CFD. Furthermore, it is very difficult to have
an arbitrary approach flow in the wind tunnel, while this can be freely done in CFD. It thus
appears possible to reduce the differences in prediction accuracy between wind tunnel
experiment and CFD on practical assessment.
Acknowledgment
The authors would like to express their gratitude to the members of the ‘‘Working
Group for Preparation of Wind Environment Evaluation Guideline based on CFD’’. Note:
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The working group members are: A. Mochida (Chair, Tohoku Univ.), Y. Tominaga
(Secretary, Niigata Inst. of Tech.), Y Ishida (Kajima Corp.), T. Ishihara (Univ. of Tokyo),
K. Uehara (National Inst. of Environ. Studies), R. Ooka (I.I.S., Univ. of Tokyo),
H. Kataoka (Obayashi Corp.), T. Kurabuchi (Tokyo Univ. of Sci.), N. Kobayashi (Tokyo
polytechnic Univ.), N. Tuchiya (Takenaka Corp.), Y. Nonomura (Fujita Corp.), T. Nozu
(Shimizu Corp.), K. Harimoto (Taisei Corp.), K. Hibi (Shimizu Corp.), S. Murakami
(Keio Univ.), R. Yoshie (Tokyo Polytechnic Univ.).
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