2021 Fiscal Year Annual Research Report
Stochastic Model Optimization for Improving the Accuracy and Rapidity of Urban Airflow CFD Simulations
| Project/Area Number |
20K14889
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| Research Institution | Hiroshima University |
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Principal Investigator |
シルザディ モハンマッドレザ 広島大学, 先進理工系科学研究科(工), 研究員 (40868733)
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| Project Period (FY) |
2020-04-01 – 2022-03-31
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| Keywords | Stochastic optimization / Turbulence mode / Urban airflow simulation / Accuracy improvement / CFD |
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| Outline of Annual Research Achievements |
In this research, a systematic calibration methodology is proposed for enhancing the accuracy of urban airflow simulations using computational fluid dynamics (CFD) models based on the Reynolds-averaged Navier-Stokes (RANS) equations. In the calibration process, high-quality data from different sources are used to define the validation metrics, which are then utilized as the objective function in a stochastic optimization solver to find optimal values for closure coefficients of the RANS turbulence model. For the high-quality data, wind tunnel experimental data and numerical simulation data by Large Eddy Simulation (LES) were used. The proposed calibration method is applied to different urban case studies, including an unstable atmospheric boundary layer (ABL) around a high-rise building, a sheltered cross-ventilated low-rise building, and a group of low-rise buildings located in a highly packed urban area. The calibration methodology was also implemented for a building shape optimization aiming to optimize the pedestrian-level wind environment (PLWE) around a high-rise building located in a real urban configuration. The significant advantage of using the obtained calibrated coefficients is observed over the existing coefficients embedded in CFD tools as well as the ones recommended by other calibration methods in the literature. Thus, this research proves the necessity of finding a group of customized optimum closure coefficients for RANS turbulence models suitable for a wide range of urban flow problems.
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