Cross Comparisons of CFD Prediction for Wind Environment at Pedestrian Level around Buildings Part Comparison of Results for Flow-field around a High-rise Building Located in Surrounding City Blocks Ryuichiro Yoshie ) Akashi Mochida 2) Yoshihide Tominaga 3) Hiroto Kataoka 4) Masaru Yoshikawa 5) ) Tokyo Polytechnic University, Iiyama 583, Atsugi-City, Kanagawa, Japan ; yoshie@arch.t-kougei.ac.jp 2) Tohoku University, Aoba 6, Aramaki, Aoba-ku, Sendai-City, Miyagi, Japan ; mochida@sabine.pln.archi.tohoku.ac.jp 3) Niigata Institute of Technology, Fujihashi 79, Kashiwazaki-City, Niigata, Japan ; tominaga@abe.niit.ac.jp 4) Technical Research Institute, Obayashi Corp., Shimokiyoto 4-64, Kiyose-City, Tokyo, Japan ; kataoka.hiroto@obayashi.co.jp 5) Technology Center, Taisei Corp., Nasecho 344-, Totsuka-ku, Yokohama, Japan ; masaru.yoshikawa@sakura.taisei.co.jp ABSTRACT CFD is being increasingly applied to the prediction of the wind environment around high-rise buildings. However, the prediction accuracy and many factors that might affect simulation results are not yet thoroughly understood. In order to clarify ambiguities and to provide 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: Flowfields around two types of single high-rise buildings. Second stage: Flowfield around a high-rise building located in a city. Last stage: Flowfields around two types of Building Complexes in actual urban areas. This paper describes the cross comparison results of the second stage. A high-rise building(25 25 m) is surrounded by low-rise city blocks(4 4 m). Wind tunnel experiments in a boundary layer using split-film probe were carried out by the present authors to obtain data for assessing the accuracy of CFD results. In the CFD simulations, the influence on prediction accuracy of calculation conditions such as boundary condition for ground surface, size of side and upper calculation region, grid system, range of surrounding city blocks, and turbulence models were investigated, and are discussed in detail.
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 calculation to be performed for 6 wind directions before and after construction of 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 influence 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 was organized by the Architectural Institute of Japan. The name of this working group was subsequently changed to Working Group for Preparation of Wind Environment Evaluation Guideline based on CFD. Since its inception, this group has been working continuously to prepare a guideline 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 [-7]. The present article introduces one of the results (flowfield around a high-rise building located in a city) and discusses the influence of calculation conditions and turbulence models on CFD calculation results. GENERAL FEATURES OF COMPARATIVE AND PARAMETRIC STUDIES Figures -6 show models for comparative and parametric studies as investigated by the working group. This paper describes the results of the study on the flowfield around a high-rise building located in a city (Figure 4). Wind b H=2b wind 4b b b 4b Figure Single high-rise building (2:: square prism) Figure 2 Single high-rise building (4:4: square prism) Figure 3 Simple city block Figure 4 High-rise building in city Figure 5 Building complexes in actual urban area (Niigata) Figure 6 Building complexes in actual urban area (Shinjuku)
In these studies, the standard k-ε model or modified k-ε models or DSM were used, but LES (Large Eddy Simulation) was not applied except for flowfields around two types of single prisms (Figures and 2). It is desirable to use LES to achieve highly accurate CFD [3]. However, it is very difficult to use because it requires a lot of calculation time in practical analysis due to the limited 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 building under planning, and measures after construction) are required, and time for evaluation is also limited in the practical design stages. For the time being, we must be content with RANS type models for practical use. Therefore, the guideline currently under preparation in the working group is also based on the assumption that the analysis is performed using the standard k-ε model or modified k-ε models with steady analysis. WIND TUNNEL EXPERIMENT AROUND HIGH RISE-BUILDING IN CITY General features of wind tunnel experiment The flowfield 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 4m square and m high as shown in Figure 7 (simulating a condition where low-rise houses are densely jammed), with a high-rise building 25m square and m high (::4) in a block at the center of this area. One urban block is assumed to be enclosed by two roads (each m wide) and roads 2m and 3m wide. The wind velocity measuring points are shown in Figurer 8. The scale of the experimental model was /4 and the measuring height was 5mm above the floor of the wind tunnel (2m above ground in real scale). Wind velocity was measured in three wind directions (, 22.5 and 45 ) using a thermister anemometer. In addition, for wind direction only, 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=25mm in the experiment and m in real scale) was 6.6 m/s. Wind direction 22.5 45 2m road 3m road m m road 45 m m road Figure 7 Outline of wind tunnel experiment 4m 2m 4m 3m 4m Experiment Scale: /4 Measuring height: 5mm Figure 8 Measuring points
CFD STUDY ON FLOWFIELD AROUND HIGH RISE-BUILDING IN CITY Standard calculation conditions The problems with CFD analysis in an urban area, as described above, are: () 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? Thus, as the first part of this study, the influence of the calculation conditions was examined by varying calculation conditions ()-(3) from standard calculation conditions shown in Table and Figure 9. Table Standard calculation conditions (Calculation was carried out in experimental scale.) 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.8m.8m.8m (The size of the test section of the wind tunnel) 32(x) 3(y) 76(z)=,34,6 mesh (Fig.9) Quick scheme for U,V,W,k,ε Logarithmic law for smooth surface wall Free slip wall condition Standard k-εmodel Interpolated values of U and k from the experimental approaching flow ε=c μ /2 k du/dz (ε=pk ), C μ =.9 Outflow boundary condition Zero gradient condition.8m High-rise building was divided into 2(x) 2(y) 27(z).8m y x (a) Whole computational domain and grid resolution (b) Macrograph of central area Figure 9 Computational domain and grid resolution for standard calculation conditions Comparison of experimental results with CFD results based on standard calculation conditions The calculation results based on the standard calculation conditions and the experimental results measured by split film probe (EXP_S) are compared in Figure (wind direction ) and in Figure (wind direction 45 ). The wind speed ratio between the scalar wind velocity at each measuring point and U H is represented on the ordinate. At measuring points 35, 38 where the wind velocity was highest for wind directions and 45, respectively, the calculation results
were about 6% lower than the experimental results, while relatively good matching was observed for the other strong wind regions. CFD Figure Comparison between CFD based on standard calculation conditions and experiment (Wind direction= ) CFD Figure Comparison between CFD based on standard calculation conditions and experiment (Wind direction=45 ) Influence of various calculation conditions on CFD analysis results ) Influence of size of horizontal computational domain To evaluate the influence of the horizontal computational domain, it was expanded from the standard domain of.8m.8m to one of 3.6m 3.6m, and contracted to one of.5m.5m, which is near the rim of the surrounding block. The results are shown in Figure 2. When the horizontal computational domain was large (3.6m 3.6m), the wind speed tended to slightly decrease with decrease of the obstruction ratio in the horizontal direction. However, when the calculation was carried out in the smaller domain (l.5m.5m), the wind speed became higher. The wind speed change for the small domain was larger than that for the large domain despite the fact that the contraction was only 2% for the small domain whereas the expansion was 2% for the large domain. Therefore, it is desirable to expand the horizontal computational domain to a certain extent outside the rim of the surrounding urban block. Small(.5m.5m) Standard(.8m.8m) Large(3.6m 3.6m) Figure 2 Influence of horizontal computational domain 2) Influence of size of vertical computational domain Figure 3 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 appeared that no substantial problem occurred even when the vertical computational domain was lowered to about 3H. Low(2H) Middle(3H) Standard(7.2H) Figure 3 Influence of vertical computational domain 3) Influence of grid resolution Figure 4 shows the calculation results when a fine grid and a coarse grid were used. For the fine grid (25 (x) 22(y) (z) = 4,386,43), the grid width was set to about /.5 of the standard grid in all three directions x, y and z. For the coarse grid (74 (x) 68 (y) 48 (z) = 24,536), it was about.5 times the standard grid. The difference between the calculation results for the standard grid and the fine grid was very small. The difference between the calculation results for the coarse grid and the other cases was also small. The standard grid would be satisfactory, i.e. with one side of the high-rise building divided into portions or more. Coarse mesh Standard mesh Fine mesh Figure 4 Influence of grid resolution 4) Influence of reproduction range of surrounding urban blocks Figure 6 shows the results of calculation with two rows and three rows each deleted from the peripheral region of the surrounding urban blocks, as shown in Figure 5. The difference from the standard case was very small except at measuring points, 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 two or more were maintained in the surroundings of the region to be evaluated. (a) deleting 2 rows (b) deleting 3rows Figure 5 Reproduction range of of surrounding urban blocks Standard Deleting 2 rows Deleting 3 rows Figure 6 Influences of reproduction range of surrounding urban blocks
Influence of different computational code and turbulence models Next, we investigated the influence of different computational codes and turbulence models. Table 2 shows the calculation cases. Three computational codes were compared for the standard k-ε model (wind directions:, 22.5 and 45 ), and modified k-ε models with wind direction (LK model [8], RNG model [9], Durbin model [], and mixed time scale (S-Ω) model []). Boundary conditions were unified to standard calculation conditions (see Table ) for all calculation cases. Code Case name A B C Wind direction Table 2 Calculation cases Turbulence model A-_ A-_22.5 22.5 Standard k-ε A-_45 45 A-2_ LK k-ε A-3_ RNG k-ε B-_ B-_22.5 22.5 Standard k-ε B-_45 45 B-2_ LK k-ε B-3_ RNG k-ε C-_ C-_22.5 22.5 Standard k-ε C-_45 45 C-2_ Durbin C-3_ Mixed time scale Grid system Structured grid Unstructured grid Structured grid Computational method and time integral scheme SIMPLE Steady solution SIMPLE Steady solution Artificial compressibility method explisit Influence of differences of computational codes Figures 7-9 show the calculation results of Case A-, Case B- and Case C- with experimental results measured by a thermistor anemometer (EXP_T). Turbulence model was the standard k-ε model in every case. The calculated scalar wind velocities were corrected using the calculated k value to compare the experimental results measured by non-directivity thermistor anemometer (NOTE ). As a whole, the differences among the calculation results for different codes are relatively small and the wind speed ratio in CFD is evaluated somewhat lower than the experimental results. This tendency is particularly remarkable at the measuring points positioned in circulating flow region leeward of the building (for example Nos. 8-, 49-5, etc.)... A- B- C- Exp_T Figure 7 Influence of differences of computational code (Wind direction= )
A- B- C- Exp_T Figure 8 Influence of differences of computational code (Wind direction=22.5 ). A- B- C- Exp_T. Figure 9 Influence of differences of computational code (Wind direction=45 ) Influence of differences of turbulence models The wind speed ratios for the turbulence models for each computational code for wind velocity are compared in Figure 2-22. The experiments under comparison () are based on the split film probe. There are some differences due to the influence of the turbulence models for different computational codes. The difference is rather high for code B, while it is relatively low for Code C. In all of these codes, if we see strong wind regions, the wind velocities of the LK and RNG models tends to be higher than those of the standard k-ε model, and are closer to the experimental results. Figure 23 shows the correlation between the results of CFD (Code A) and the experimental results. Although there are some discrepancies in the weak wind region, the results for the LK RNG models agree well with the experimental results in strong wind region. The results for the RNG model show somewhat better matching with experimental results than those of the LK model.
Standard k-ε(a-) LK(A-2) RNG(A-3) Figure 2 Influence of differences of turbulence models (Code A).... Standard k-ε(b-) LK(B-2) RNG(B-3) Figure 2 Influence of differences of turbulence models (Code B) Standard k-ε(c-) Durbin(C-2) S-Ω(C-3) Figure 22 Influence of differences of turbulence models (Code C) CFD (Standard k-ε) CFD (LK) CFD (RNG) (a) Standard k-εmodel (b) LK model (c) RNG model Figure 23 Comparison of wind speed ratio obtained from different turbulence models (Code A)
The horizontal distribution of wind speed ratio and turbulence energy k (standardized by U H 2 ) around the high-rise building based on Code A are shown in Figures 5 and 6. A remarkable difference is seen in the wind soeed distribution depending on the turbulence model. In the LK model, regions of.7 or more (not seen in other models) are found in the side region of the high-rise building. In the RNG model, wide regions of or more are seen in the side region. When we look at the circulating flow behind the high-rise building, isoplethic curves are denser in two modified models than in the standard k-ε model, indicating that stronger circulating flows are reproduced there. This is attributed to the fact that stronger separation flows occur at the side region than in the standard k-ε model, and this causes stronger collision with the leeward low-rise block. In the k distribution, the peak value of k appears on windward side of the high-rise building in the standard k-ε model, and this is why the wind speeds at the side region of the high-rise building were evaluated lower. In the LK model, the peak value of k (region of.4 or more), unlike in the other models, is observed near the region behind the windward building. (a) Standard k-εmodel (b) LK model RNG model Distribution of wind speed ratio (Wind direction, Code A) (a) Standard k-εmodel (b) LK model RNG model Distribution of turbulence energy k (Wind direction, Code A) CONCLUSION According to the results of the present study, the influence on the calculation results of the computational domain, the grid resolution, and the reproduction range of the surrounding urban block is relatively low. In the calculation for practical application, the condition setting criteria can be expressed as follows: Two or more urban blocks each of several tens of meters should be reproduced in the area surrounding the region to be evaluated The horizontal computational domain should be maintained to a certain extent outside the rim of the surrounding urban block. The vertical computational domain should be maintained at 3H or more One side of the high-rise building should be divided into portions or more.
The differences among the results for different computational codes were relatively small. The difference due to the codes tends to be higher in the circulating flow region behind the building than in the strong wind region. In the strong wind region, the wind speed ratios of modified k-εmodels agreed well with the experimental results compared with the standard k-ε model. 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 several tens of % at most for the standard k-ε model. For the LK model and the RNG model, more accurate prediction can be made in the strong wind region. ACKNOWLEDGEMENT We express our sincere gratitude to the members of the Working Group for Preparation of Wind Environment Evaluation Guideline Based on CFD for useful and valuable advice. This study was partially funded by the Ministry of Education, Culture, Sports, Science and Technology, Japan, through the 2 st Century Center of Excellence Program of Tokyo Polytechnic University. NOTE The mean scalar velocity measured in the wind tunnel using a non-directive thermistor anemometer (S exp ) is regarded as the time averaged instantaneous scalar velocity, which can be expressed as: S exp =<(u 2 +v 2 +w 2 ) /2 >. However, the mean scalar velocity given from CFD (S CFD ) is calculated from the time averaged velocity vector, namely, S CFD =(<u> 2 +<v> 2 +<w> 2 ) /2. Thus, the output of the thermistor anemometer is larger than that given by CFD. S exp =<(u 2 +v 2 +w 2 ) /2 > =<{(<u>+u ) 2 +(<v>+v ) 2 +(<w>+w ) 2 } /2 > =<(<u> 2 +<v> 2 +<w> 2 +<u 2 +v 2 +w 2 >) /2 > =(<u> 2 +<v> 2 +<w> 2 +2k) /2 =(S 2 CFD +2k) /2 Here, u,v,w: three components of instantaneous velocity vector, <f>: time-averaged value of f, f =f-<f>. The mean scalar velocity given from CFD(S exp ) was corrected as S exp =(S 2 CFD +2k) /2 when compared with the experimental results measured by a thermistor anemometer. However, this correction does not completely express the influence of velocity fluctuation included in the experimental results measured by the thermistor anemometer.
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