Wind Pressure Distributions on Buildings Using the Coherent Structure Smagorinsky Model for LES
Abstract
:1. Introduction
2. SGS Models and Numerical Methods
2.1. SGS Models
2.2. Numerical Methods
3. Numerical Simulation for Isolated Buildings in Uniform Flow
3.1. Experiments for Validation
3.2. Computational Model and Calculation Conditions
3.3. Results and Discussion
3.3.1. Pressure Coefficients of Buildings
3.3.2. Flow Fields around Buildings
4. Numerical Simulation for a High-Rise Building in an Urban City with Turbulent Flow
4.1. Validation Experiments
4.2. Computational Models and Calculation Conditions
4.3. Results and Discussion
4.3.1. Wind Flow Characteristics
4.3.2. Flow Fields around the Target Building
4.3.3. Pressure Coefficients of the Target Building for a Specific Wind Direction
4.3.4. Pressure Coefficients of the Target Building for All Wind Directions
5. Conclusions
- For an isolated rectangular building in uniform flow, the flow field around the building is generated by the horseshoe vortex and separated flow. Both SM and CSM show good agreement with the PIV results in the flow separation region as well as in the horseshoe vortex regions. The wind pressure coefficients calculated using both SM and CSM consistently agree with the experimental results (values within 20%).
- For an isolated building with a setback in uniform flow, a complex flow field is generated by a distinctive large horseshoe vortex interfering with the separated flow from the lower roof. A large negative pressure coefficient is found at the corner of the sidewall, near the interfered-with area. Although using the SM results in under-prediction, the predictions made using the CSM show good agreement with the PIV results for the flow field. This means that the CSM with the model parameter based on the coherent structure reflects the vortex behaviors and simulates well the complex flow fields due to the strong interference of the vorticities and flow separations. The CSM results also showed much better performance than the SM results for the wind pressure distribution on this building, being within 20% of the experimental results.
- For a high-rise building in an actual urban city with a turbulent boundary layer flow, we found that a strong three-dimensional complex wind flow occurs due to the strong influence of neighboring buildings, including interference of vortices and separated flows. We also found a distinctive wind pressure distribution that had strong positive and negative pressures simultaneously occurring on the front wall of the target building. The CSM also gives more accurate results with less variation than the SM, being within 20% of experimental results. In addition, LES with the CSM was conducted for all wind directions. The calculated largest positive and negative peak pressure coefficients were consistently in good agreement with experimental results (within 20%) in the relatively high-pressure region, at least similar to or less than the COVs of the wind tunnel test results [20].
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Type | B | D | H | H1 | H2 | D1 |
---|---|---|---|---|---|---|
R01 | 0.20 | 0.10 | 0.20 | |||
SBL | 0.20 | 0.10 | 0.20 | 0.15 | 0.05 | 0.35 |
Subgrid-Scale (SGS) Model | Smagorinsky Model (SM) | Coherent Structure Smagorinsky Model (CSM) |
---|---|---|
Mesh cells (millions) | 570 | 570 |
Time increment Δt (s) | 10−4 | 10−4, 5 × 10−5, 2.5 × 10−5 |
Courant number Cr | 2.0 | 2.0, 1.0, 0.5 |
Initial run-up time (s) | 3 | 3 |
Evaluation time (s) | 8 | 8 |
Number of parallel CPUs | 6144 | 6144 |
SGS Model Use in Large Eddy Simulation (LES) | SM | CSM | CSM |
---|---|---|---|
Mesh type | A | A | B |
Mesh cells (millions) | 140 | 140 | 1100 |
Wind direction θ (degree) | 60 | 60 (0, .., 350) | 60 |
Time increment Δt (s) | 2.5 × 10−5 | 2.5 × 10−5 | 1.25 × 10−5 |
Courant number Cr | 1 | 1 | 1 |
Initial run-up time (s) | 3 | 3 | 3 |
Evaluation time (s) | 18 | 30 (6) | 6 |
Number of samples in ten-minute record in real-time conversion for evaluation | 3 | 5 (1) | 1 |
Number of parallel CPUs | 768 | 768 | 6144 |
Computational time for one sample (days) | 40 | 20 | 20 |
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Phuc, P.V.; Nozu, T.; Kikuchi, H.; Hibi, K.; Tamura, Y. Wind Pressure Distributions on Buildings Using the Coherent Structure Smagorinsky Model for LES. Computation 2018, 6, 32. https://doi.org/10.3390/computation6020032
Phuc PV, Nozu T, Kikuchi H, Hibi K, Tamura Y. Wind Pressure Distributions on Buildings Using the Coherent Structure Smagorinsky Model for LES. Computation. 2018; 6(2):32. https://doi.org/10.3390/computation6020032
Chicago/Turabian StylePhuc, Pham Van, Tsuyoshi Nozu, Hirotoshi Kikuchi, Kazuki Hibi, and Yukio Tamura. 2018. "Wind Pressure Distributions on Buildings Using the Coherent Structure Smagorinsky Model for LES" Computation 6, no. 2: 32. https://doi.org/10.3390/computation6020032