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Article

Numerical Simulation of the Wind Speed Field Around Suburban Residential Buildings with Different Arrangements

1
Department of Geophysics, School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
Science and Technology Department, City University of Wuhan, Wuhan 430083, China
*
Author to whom correspondence should be addressed.
Symmetry 2025, 17(10), 1699; https://doi.org/10.3390/sym17101699
Submission received: 2 September 2025 / Revised: 2 October 2025 / Accepted: 4 October 2025 / Published: 10 October 2025
(This article belongs to the Special Issue Symmetry in Finite Element Modeling and Mechanics)

Abstract

The wind environment in furnace cities has attracted considerable research attention. Investigating the impact of suburban residential building arrangements in furnace cities on inter-building wind speed fields is useful and cost-effective for scientifically optimizing layouts. This study simulated 13 wind speed fields across six symmetric and asymmetric building arrangements: linear, inclined, convex, concave, M-shaped, and V-shaped, with varying building offsets and spacing widths. We used the standard k–ε model for simulations through finite element method. Results demonstrated that larger building offsets enhanced inter-building wind speeds, with the concave arrangement most effectively enhanced the wind speed between buildings among the configurations. V-shaped arrangements slightly underperformed concave layouts in wind speed uniformity. Based on the summer wind direction data from Wuhan Tianhe Meteorological Station, we propose two corresponding layouts: concave and V-shaped arrangements, which are conductive to enhancing inter-building wind speed. In practical planning, the orientation of building clusters can be adjusted according to the local wind rose diagram.

1. Introduction

Wuhan, Nanjing, Chongqing and Nanchang are often called China’s ‘furnace cities’ due to their scorching summer temperatures. Urban planning in furnace cities should prioritize heat mitigation strategies [1]. Wind speed is a key parameter for guiding and enhancing summer ventilation, which contributes to cooling, dehumidification, and air quality improvement [2,3,4,5,6]. This is not only crucial for enhancing the quality of the living environment and ensuring health and safety, but also an essential requirement for achieving building energy efficiency, sustainable urban development, and scientific planning and design [7]. Therefore, research on wind speed field is critical.
Low wind speeds in city inter-building space are unfavorable for summer comfort [8]. Conventional methods for studying the wind environment include full-scale measurement, reduced-scale wind tunnel experiments, and numerical simulation using computational fluid dynamics (CFD) [9]. Full-scale measurement [10,11,12] and wind tunnel tests [13,14,15,16] are expensive, and their application is limited by the external environment, whereas numerical simulation can simulate the wind field near complex buildings at low cost. Thus, numerical simulations validated with wind tunnels or full-scale measurement are widely used for studying wind patterns [17,18,19,20,21,22]. Modern planning and design increasingly rely on CFD simulations to precisely evaluate and optimize the wind environment performance of different design schemes.
Many studies have focused on methods of increasing the wind speed in city center area through the adjustment of building parameters to promote ventilation [23,24,25,26,27,28,29,30]. For the city center area, the street canyon model comprising regular arrangements of cube buildings is the most widely used model [31,32]. Common geometric parameters include the building height [33,34], street width [35,36], and street aspect ratio (AR; i.e., building height/street width) [37,38]. These results show that the taller the buildings and the narrower the streets, the poorer the ventilation becomes.
In terms of turbulence model, Reynolds-Averaged Navier–Stokes (RANS) models are widely used in CFD simulations due to their accurate prediction of mean wind velocity and low computational cost [39]. In addition to RANS models, large-eddy simulations (LES) models are also commonly employed. It is generally acknowledged that LES can provide more detailed flow dynamics and turbulence-resolving capabilities than RANS, which is particularly beneficial for capturing unsteady processes and local-scale variability [40]. Despite the advantages, some research indicated that the applicability of LES models is more problematic due to its much longer computational time required than RANS approaches and some issues regarding the implementation of wall and inlet boundary conditions [41,42].
At present, residential building construction is being undertaken in several open spaces (i.e., low building density areas) on the fringes of urban areas. Strasszer and Xydis [43] used RANS models to evaluate suburban campus wind characteristics and investigated the effect of urban wind on the performance and loading of small wind turbines. Hnaien et al. [44] used the k ω two-equation turbulence model to estimate the wind flow distribution around buildings to identify areas of wind discomfort. However, these studies typically involve customized simulations for specific building complexes, which lack generalizability.
In the suburbs of Wuhan, there remain numerous residential clusters with monotonous architectural styles and minimal design variation. These building clusters differ from those in the city center area, which typically consist of just a few structures. Conducting CFD research on these standardized layouts can reveal universal patterns, enabling the development of widely applicable guidelines that could achieve substantial cost savings. How to optimizing inter-building wind conditions within constrained construction costs presents a significant research challenge for these suburban residential buildings. It is indisputable that adjusting the arrangement patterns of residential buildings is the optimal choice for optimizing the inter-building wind field under constrained costs (constrained site areas).
Therefore, this study investigates the influence of various irregular suburban residential building arrangements on inter-building space wind speed fields in furnace cities to provide suggestions to urban planners for achieving favorable wind speed field in summer. Six different building arrangements were selected. The chosen arrangements are representative of the basic suburban building layout patterns where the majority of Wuhan’s general populace resides. Considering that the standard k–ε turbulence models are more computationally efficient and provide reasonable results for mean flows and the spatial average flow properties, this paper uses the standard k–ε model for simulation. Wind speed fields corresponding to six building arrangements with various building spacing widths and offset distances were numerically simulated, and favorable suburban residential building arrangement modes for Wuhan city were selected.

2. Methods

The finite element method was used to solve governing equations of fluid dynamics. The results of the standard k–ε turbulence model were compared with those of the realizable k–ε model. The standard k–ε model was ultimately selected for the simulation. For the wind direction, only the direction perpendicular to the building’s orientation was considered. Because when the wind direction is perpendicular to the buildings, the first building blocks the majority of the wind flow, resulting in the slowest wind speeds within the interstitial spaces between structures. This incidence angle represents the worst-case condition. As long as this critical situation can be improved, the overall wind speed field will be significantly enhanced. Wind tunnel experiments [45,46] were conducted at the Architectural Institute of Japan (AIJ) to evaluate the reliability of CFD prediction for wind field. Both the numerical simulations and wind tunnel experiments were carried out at a 1:1 scale. The validity of the wind tunnel testing rests upon the establishment of key similarity criteria. In our experiment, the Reynolds number based on the building width was maintained above 104, which is well above the recognized critical value (typically 103 to 104) for most rectangular building sections to ensure that the flow regime and pressure distributions are representative of the full-scale scenario. The atmospheric boundary layer was carefully simulated in the wind tunnel to match the target full-scale wind characteristics. Through strict adherence to geometric and flow similarity, and by operating in the Reynolds-independent regime, the results obtained from the wind tunnel tests are considered a reliable representation of the expected full-scale wind effects.

2.1. Domain Dimensions and Boundary Conditions

Figure 1 displays a schematic of the CFD domain and boundary conditions. The upstream and downstream distances were 5H and 15H, respectively, while the domain height and side distance were 5H. These distances were selected on the basis of the best practice guidelines for CFD simulation of urban aerodynamics [21,46]. The area included five similar buildings with linear, incline, convex, concave, M-shape, and V-shape arrangements (Figure 2). Here, L, H, and b are building length, height, and width, respectively; L:H:b = 4:4:1; W is the inter-building spacing; D is the displacement between adjacent buildings (see Figure 2c). Here, W and D are variables. The parameter settings of the 13 simulation cases are presented in Table 1.
The symmetry condition was set at the top and side boundaries of the domain, implying that no vertical gradient occurred for any of the parameters. The no-slip condition with standard wall functions was used for the ground and surfaces of buildings. The adopted atmospheric boundary layer profile already incorporates the effects of ground roughness. Thus, the ground and building surface roughness were ignored in this simulation. Zero-normal gradients were imposed for all flow variables at the outlet boundary.
The inlet wind speed boundary conditions are expressed as follows:
U Z = U S z z s α
where U S is the velocity at the reference height, U Z is the wind velocity, z s is the reference height, and α is the power-law exponent determined by the terrain category. We obtained the fitting values z s = 1000 mm, U S = 7.84 m/s, and α = 0.25 from the wind tunnel experimental data (Figure 3c). The inlet turbulent kinetic energy boundary conditions are expressed in (Figure 3d). The following equation was applied to determine the turbulence dissipation rate profiles as the inlet airflow condition:
ε = C μ 0.5 k d U Z d z
where k is the turbulent kinetic energy and ε is the turbulent dissipation rate. Here, k was obtained from the wind tunnel experiment (Figure 3d), which was determined by measuring instantaneous velocity at high frequency and processing the data computationally, while C μ = 0.09 is a model constant.

2.2. Computational Meshes

Unstructured tetrahedral meshes were generated. Regions nearer to the buildings and ground had smaller grids, and regions farther away from the buildings and ground had larger grids to save computational time. The size of the smallest mesh elements was approximately 0.075H, whereas the size of the largest mesh elements was approximately 0.4H. A prism layer mesh comprising 5 layers was applied to resolve the near-wall region. The y+ values on the critical building surfaces consistently <1. This confirms that the mesh resolution at the walls is commensurate with the employed no-slip wall treatment, ensuring the accuracy of the near-wall flow prediction. The total number of grids was approximately 1.2 million, with slight differences between various cases. For details, see Figure A1 and Table A1.
Figure 4 displays the grid-independent test results. The data used for comparison were obtained from a straight line (coordinates: (x, y, z) = (1400 mm, 800 mm to 1500 mm, 100 mm)) in the case 3. Three types of meshes (coarse, medium, and fine) were compared. The number of mesh cells for each case is shown in Table A1. The results revealed that the difference between the medium and fine grid was small, whereas the difference between the medium and coarse grid was large. Thus, about 1.2 million grids were sufficient for simulation.

2.3. Solution Method and Model Validation

The standard k–ε model and realizable k–ε model were simulated using COMSOL Multiphysics 5.4 software. The prediction results of these two models with the wind tunnel data are presented in Figure 5. We determined that the standard k–ε model result was a little bit closer to the wind tunnel data. Therefore, we selected the standard k–ε model to perform our simulations. The solver setup is steady and the velocity–pressure coupling is based on the “SIMPLEC” algorithm. The considered convergence criterion is 10−4. The minimal Reynolds number is about 20,000, which is much larger than 4000. So, our models can ensure Reynolds number independence.
To verify the validity of our numerical simulation, we compared the results of the wind tunnel test performed at AIJ with those of our numerical simulation. The specific wind tunnel test data can be found in Supplementary Materials. Figure 3 displays the wind tunnel test model. The building width b was 50 mm in the wind tunnel test. Figure 3c,d display the inlet wind profile of the streamwise wind velocity and turbulent kinetic energy. The wind velocity and turbulent kinetic energy we used in our simulation were the same as the wind tunnel data. In Figure 5, The standard k–ε model achieved a little bit better results than the realizable model did in this case, which may be attributed to the better numerical stability of the standard k–ε model in situations where anisotropic flow is not pronounced. While the realizable model is more advanced, the standard k–ε model has its own unique advantages: robustness, good convergence, and low computational cost. Although a small difference was observed between the wind tunnel test and simulation results in the near-surface area (a maximum error of approximately 0.7 m/s was observed above pedestrian level), the results exhibited strong agreement for other areas. The trend of wind speed in the numerical simulation was consistent with that in the wind tunnel test, which indicated that the numerical model can satisfactorily predict conditions of the wind speed field.

3. Results and Discussion

The wind velocity change rate R v is a ventilation index denoting the dimensionless velocity magnitude and is calculated as follows:
R v = v U s U s
where v is the wind velocity and U s is the reference wind speed, which is 7.84 m/s. Here, R v denotes the difference between the wind speed in the inter-building space and the reference wind speed. Because the wind speed in the inter-building space is always lower than the reference speed, a bigger | R v | value corresponds to a lower wind speed in the inter-building space.
The average wind velocity (m/s) of the middle section of inter-building space is a factor for inter-building space ventilation and is defined as follows:
v a v e r a g e = v   d x d z S
where S is the sectional area of the middle of inter-building space (see Table 2). This parameter indicates the average speed of the middle section of inter-building space. We calculated every v a v e r a g e of inter-building space in all cases. The results are displayed in Table 2. Here, vst.1 vst.2 vst.3 vst.4 are the average wind speed of the first, second, third, and fourth inter-building space, respectively. The magnitude and uniformity of wind speed between buildings for each case can be evaluated from the data presented in Table 2.

3.1. Effects of Building Arrangement

We studied the effects of building arrangement in cases 3, 7, 9, and 13; cases 5, 8, and 10; and cases 2, 11, and 12. Each case set had the same inter-building space width and offset. Figure 6 presents the vertical view of the contours of the predicted wind velocity change rate R v of four cases at z = 2b. We compared the incline, convex, concave, and M-shape arrangements. Figure 6a,c show a uniform distribution for the wind speed of the inter-building space, whereas Figure 6b reveals that the wind speed in the fourth inter-building space was considerably higher than that in other spaces. Figure 6d displays that the wind speed in the first and second space was considerably higher than that in other spaces. Figure 7 shows the contours of the predicted wind velocity change rate R v of three cases at z = 2b from the vertical view. We compared the incline, convex, and concave arrangements. The wind speed was almost the same for the spaces in Figure 7c but differed considerably for those in Figure 7b. The wind speed was low for the first and second spaces. Figure 8 displays the contours of the predicted wind velocity change rate R v of three cases at z = 2b from the vertical view. We compared the incline, M-shape, and V-shape arrangements. The difference between them was small. Figure 9 displays a comparison of v a v e r a g e among all inter-building spaces of various building arrangement cases. Cases 9 and 13 exhibited a higher average wind speed (Figure 9a), but the wind speed distribution in case 9 was more even. Cases 5 and 10 exhibited a higher average wind speed (Figure 9b), but the wind speed distribution of case 10 was more even. Cases 11 and 12 exhibited a high but nearly equal average wind speed (Figure 9c). Thus, the concave arrangement was better than the V-shape arrangement, the V-shape arrangement was better than the incline arrangement, the incline arrangement was better than the convex arrangement, and the M-shape and V-shape arrangements were equally better than the incline arrangement. Therefore, the concave arrangement was the best.

3.2. Effects of the Offset

We studied three groups of cases, namely cases 1, 2, 3, and 4; cases 5 and 6; and cases 12 and 13, to investigate the effects of building offset. All cases in a group had the same inter-building space width and building arrangement. Among these cases, cases 4, 6, and 13 had a large building offset, whereas cases 1, 5, and 12 had a small building offset. Figure 10 displays the vertical view of the contours of the predicted wind velocity change rate R v of cases 1, 2, 3, and 4 at z = 2b. A larger offset corresponded to a larger high-speed region in the inter-building space. Figure 11 presents a vertical view of the contours of the predicted wind velocity change rate R v of cases 5 and 6 at z = 2b. The wind speed of inter-building space was higher in case 6 than in case 5. Figure 12 displays the vertical view of the contours of the predicted wind velocity change rate R v of cases 12 and 13 at z = 2b. Similarly, the wind speed in inter-building space was higher in case 13 than in case 12. Figure 13 presents a comparison of v a v e r a g e of all inter-building space for various building offset cases. The results indicate that larger offset results in higher average wind speed. Thus, a large offset is propitious to inter-building space ventilation.

3.3. Urban Planning Proposals: Taking Wuhan Tianhe Meteorological Station as a Case Study

We collected wind direction data from Wuhan Tianhe Meteorological Station between 1 June and 30 September 2024, and compiled it into the wind rose diagram shown in Figure 14a. The diagram reveals that the dominant summer wind directions in this area are predominantly SSW and S, while winds from the WNW and NW directions are notably scarce. Consequently, we propose the building layout recommendations illustrated in Figure 14b,c. This design can effectively channel the prevailing summer winds through the building clusters while enhancing wind speeds between structures when the wind direction is perpendicular to building orientations.

4. Conclusions

Thirteen cases were numerically simulated to investigate the effects of building arrangement on inter-building wind speed fields. Our analysis yielded the following conclusions:
  • The concave arrangement outperforms V-shaped layouts; both M-shaped and V-shaped configurations are superior to inclined layouts, which in turn surpass convex arrangements. Thus, the concave arrangement is optimal.
  • Larger offsets increase inter-building space breathability, as a result of wind flow entering the inter-building more easily.
  • For the area surrounding Wuhan Tianhe Meteorological Station, we propose concave and V-shaped arrangements oriented along the WNW-ESE axis as suitable suburban configurations.
This numerical approach can be referenced to suburban residential building design to improve inter-building wind speed. The orientation of building clusters can be adjusted according to the local wind rose diagram.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/sym17101699/s1, Supplementary Data S1: Wind tunnel test data.

Author Contributions

Conceptualization, X.Y.; methodology, X.Y.; software, X.Y.; validation, X.Y.; writing—original draft preparation, X.Y.; writing—review and editing, S.Z.; visualization, X.Y.; supervision, X.Y.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 42074176, U1939204.

Data Availability Statement

All data, models, or code generated or used during the study are available from the corresponding author by reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

The appendix illustrates the overall grid distribution and the total cell count of each model.
Figure A1. An examples of grid arrangements (case 5).
Figure A1. An examples of grid arrangements (case 5).
Symmetry 17 01699 g0a1
Table A1. The number of grid cells corresponding to each model.
Table A1. The number of grid cells corresponding to each model.
Case12345678
Grid number1,261,0161,238,4471,224,5061,203,7141,161,1371,133,8661,226,2981,164,262
Case910111213CoarseMediumFine
Grid number1,224,4751,163,5131,229,9371,223,5981,189,853207,5131,224,5064,592,660

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Figure 1. Schematic of computational domains used in CFD studies: (a) top view and (b) side view.
Figure 1. Schematic of computational domains used in CFD studies: (a) top view and (b) side view.
Symmetry 17 01699 g001
Figure 2. Schematic of various building arrangements. (a) single building side views, (b) linear, (c) incline, (d) convex, (e) concave, (f) M-shape, and (g) V-shape arrangements.
Figure 2. Schematic of various building arrangements. (a) single building side views, (b) linear, (c) incline, (d) convex, (e) concave, (f) M-shape, and (g) V-shape arrangements.
Symmetry 17 01699 g002
Figure 3. Description of the wind tunnel test model. (a) 4:4:1 building, (b) location of measuring points, (c) inlet wind profile of the streamwise wind velocity, (Points: observed data, Line: theoretical fitting curve), and (d) inlet profile of turbulent kinetic energy, (Points: observed data, Line: theoretical fitting curve).
Figure 3. Description of the wind tunnel test model. (a) 4:4:1 building, (b) location of measuring points, (c) inlet wind profile of the streamwise wind velocity, (Points: observed data, Line: theoretical fitting curve), and (d) inlet profile of turbulent kinetic energy, (Points: observed data, Line: theoretical fitting curve).
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Figure 4. Grid-independent test results.
Figure 4. Grid-independent test results.
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Figure 5. Simulation and wind tunnel test velocity for the lines (a) x = −75 mm, (b) x = 0 mm, (c) x = 50 mm, and (d) x = 200 mm.
Figure 5. Simulation and wind tunnel test velocity for the lines (a) x = −75 mm, (b) x = 0 mm, (c) x = 50 mm, and (d) x = 200 mm.
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Figure 6. Contours of the predicted wind velocity change rate R v (dimensionless) of four cases at z = 2b from the vertical view. (a) case 3, (b) case 7, (c) case 9, (d) case 13.
Figure 6. Contours of the predicted wind velocity change rate R v (dimensionless) of four cases at z = 2b from the vertical view. (a) case 3, (b) case 7, (c) case 9, (d) case 13.
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Figure 7. Contours of the predicted wind velocity change rate R v (dimensionless) of three cases at z = 2b from the vertical view. (a) case 5, (b) case 8, (c) case 10.
Figure 7. Contours of the predicted wind velocity change rate R v (dimensionless) of three cases at z = 2b from the vertical view. (a) case 5, (b) case 8, (c) case 10.
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Figure 8. Contours of the predicted wind velocity change rate R v (dimensionless) of three cases at z = 2b from the vertical view. (a) case 2, (b) case 11, (c) case 12.
Figure 8. Contours of the predicted wind velocity change rate R v (dimensionless) of three cases at z = 2b from the vertical view. (a) case 2, (b) case 11, (c) case 12.
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Figure 9. Comparison of v a v e r a g e (m/s) among all inter-building spaces for (a) cases 3, 7, 9 and 13, (b) cases 5, 8, and 10, and (c) cases 2, 11, and 12.
Figure 9. Comparison of v a v e r a g e (m/s) among all inter-building spaces for (a) cases 3, 7, 9 and 13, (b) cases 5, 8, and 10, and (c) cases 2, 11, and 12.
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Figure 10. Contours of the predicted wind velocity change rate R v (dimensionless) of four cases at z = 2b from the vertical view. (a) case 1, (b) case 2, (c) case 3, and (d) case 4.
Figure 10. Contours of the predicted wind velocity change rate R v (dimensionless) of four cases at z = 2b from the vertical view. (a) case 1, (b) case 2, (c) case 3, and (d) case 4.
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Figure 11. Contours of the predicted wind velocity change rate R v (dimensionless) of two cases at z = 2b from the vertical view. (a) Case 5, (b) case 6.
Figure 11. Contours of the predicted wind velocity change rate R v (dimensionless) of two cases at z = 2b from the vertical view. (a) Case 5, (b) case 6.
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Figure 12. Vertical view of the contours of the predicted wind velocity change rate R v   (dimensionless) of two cases at z = 2b. (a) Case 12, (b) case 13.
Figure 12. Vertical view of the contours of the predicted wind velocity change rate R v   (dimensionless) of two cases at z = 2b. (a) Case 12, (b) case 13.
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Figure 13. v a v e r a g e (m/s) of the inter-building spaces of (a) cases 1, 2, 3, and 4, (b) cases 5 and 6, and (c) cases 12 and 13.
Figure 13. v a v e r a g e (m/s) of the inter-building spaces of (a) cases 1, 2, 3, and 4, (b) cases 5 and 6, and (c) cases 12 and 13.
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Figure 14. (a) Wind rose diagram from Wuhan Tianhe Meteorological Station; design recommendations for suburban residential buildings: (b) concave arrangement, (c) V shape arrangement.
Figure 14. (a) Wind rose diagram from Wuhan Tianhe Meteorological Station; design recommendations for suburban residential buildings: (b) concave arrangement, (c) V shape arrangement.
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Table 1. Parameter settings of all cases.
Table 1. Parameter settings of all cases.
Case No.WD12D23D34D45Building Arrangements
14b0000Symmetry 17 01699 i001
24bb/2b/2b/2b/2Symmetry 17 01699 i002
34bbbbb
44b3b/23b/23b/23b/2
53bbbbb
63b2b2b2b2b
74b3b/10b/2b11b/5Symmetry 17 01699 i003
83b3b/10b/2b11b/5
94b11b/5bb/23b/10Symmetry 17 01699 i004
103b11b/5bb/23b/10
114b2b−2b2b−2bSymmetry 17 01699 i005
124bbb−b−bSymmetry 17 01699 i006
134b2b2b−2b−2b
Table 2. v a v e r a g e  (m/s) of the inter-building spaces of 13 cases.
Table 2. v a v e r a g e  (m/s) of the inter-building spaces of 13 cases.
Case No.123456
vst.1 (m/s) 1.13251.08491.06401.14880.969021.4577
vst.2 (m/s)0.980880.991371.18531.49261.26011.9383
vst.3 (m/s)0.946111.05271.39611.68171.64802.2349
vst.4 (m/s)0.983001.41131.78051.91982.10772.5241
78910111213Inter-building space location
1.09720.856801.47181.66991.54251.08521.4393Symmetry 17 01699 i007
0.927230.808211.46741.42881.13941.40271.9468Symmetry 17 01699 i008
1.14351.28011.59911.70141.49531.13391.2803Symmetry 17 01699 i009
1.83192.18941.67381.81021.24721.22221.8228Symmetry 17 01699 i010
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Yi, X.; Zhang, S. Numerical Simulation of the Wind Speed Field Around Suburban Residential Buildings with Different Arrangements. Symmetry 2025, 17, 1699. https://doi.org/10.3390/sym17101699

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Yi X, Zhang S. Numerical Simulation of the Wind Speed Field Around Suburban Residential Buildings with Different Arrangements. Symmetry. 2025; 17(10):1699. https://doi.org/10.3390/sym17101699

Chicago/Turabian Style

Yi, Xuchong, and Shuangxi Zhang. 2025. "Numerical Simulation of the Wind Speed Field Around Suburban Residential Buildings with Different Arrangements" Symmetry 17, no. 10: 1699. https://doi.org/10.3390/sym17101699

APA Style

Yi, X., & Zhang, S. (2025). Numerical Simulation of the Wind Speed Field Around Suburban Residential Buildings with Different Arrangements. Symmetry, 17(10), 1699. https://doi.org/10.3390/sym17101699

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