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http://dx.doi.org/10.21022/IJHRB.2019.8.4.291

Optimization Calculations and Machine Learning Aimed at Reduction of Wind Forces Acting on Tall Buildings and Mitigation of Wind Environment  

Tanaka, Hideyuki (Takenaka Corporation)
Matsuoka, Yasutomo (Takenaka Corporation)
Kawakami, Takuma (Takenaka Corporation)
Azegami, Yasuhiko (Takenaka Corporation)
Yamamoto, Masashi (Takenaka Corporation)
Ohtake, Kazuo (Takenaka Corporation)
Sone, Takayuki (Takenaka Corporation)
Publication Information
International Journal of High-Rise Buildings / v.8, no.4, 2019 , pp. 291-302 More about this Journal
Abstract
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.
Keywords
Optimization calculation; CFD; CNN; Wind force; Wind environment;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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