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Verification of Speed-up Mechanism of Pedestrian-level Winds Around Square Buildings by CFD
Hideyuki Tanaka,Qiang Lin,Yasuhiko Azegami,Yukio Tamura Council on Tall Building and Urban Habitat Korea 2022 International journal of high-rise buildings Vol.11 No.4
Various studies have been conducted on pedestrian-level wind environments around buildings. With regard to the speed-up mechanism of pedestrian-level winds, there are references to downwash effect due to the vertical pressure gradient of boundary layer flow and venturi effect due to flow blocking by the building. Two factors contribute to increase or decrease of downwash effect: change in twodimensional / three-dimensional air flow pattern (Type 1) and change in downwash wind speed due to building size that does not accompany change in airflow pattern (Type 2). Previous studies have shown that downwash effect has a greater influence in increasing or decreasing the area of strong wind than venturi effect. However, these considerations are derived from the horizontal mean wind speed distribution at pedestrian level and are not the result of three-dimensional flow field around the building. Therefore, in this study, Computational Fluid Dynamics using Large Eddy Simulation were performed to verify the downwash phenomena that contributes to increase in wind speed at pedestrian level.
Tanaka, Hideyuki,Matsuoka, Yasutomo,Kawakami, Takuma,Azegami, Yasuhiko,Yamamoto, Masashi,Ohtake, Kazuo,Sone, Takayuki Council on Tall Building and Urban Habitat Korea 2019 International journal of high-rise buildings Vol.8 No.4
We performed calculations combining optimization technologies and Computational Fluid Dynamics (CFD) aimed at reducing wind forces and mitigating wind environments (local strong winds) around buildings. However, the Reynolds Averaged Navier-stokes Simulation (RANS), which seems somewhat inaccurate, needs to be used to create a realistic CFD optimization tool. Therefore, in this study we explored the possibilities of optimizing calculations using RANS. We were able to demonstrate that building configurations advantageous to wind forces could be predicted even with RANS. We also demonstrated that building layouts was more effective than building configurations in mitigating local strong winds around tall buildings. Additionally, we used the Convolutional Neural Network (CNN) as an airflow prediction method alternative to CFD in order to increase the speed of optimization calculations, and validated its prediction accuracy.