Using Machine Learning to Predict Wind Flow in Urban Areas

被引:12
|
作者
BenMoshe, Nir [1 ]
Fattal, Eyal [1 ]
Leitl, Bernd [2 ]
Arav, Yehuda [1 ]
机构
[1] Israel Inst Biol Res, Dept Appl Math, POB 19, IL-7410001 Ness Ziona, Israel
[2] Univ Hamburg, Meteorol Inst Informat & Nat Sci, Dept Math, Bundesstr 55, D-20146 Hamburg, Germany
关键词
wind; urban; machine learning; CFD; openFOAM; AIR-QUALITY; DISPERSION; TUNNEL; MODEL; SIMULATIONS; VALIDATION; BUILDINGS; COMFORT; RANS;
D O I
10.3390/atmos14060990
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Solving the hydrodynamical equations in urban canopies often requires substantial computational resources. This is especially the case when tackling urban wind comfort issues. In this article, a novel and efficient technique for predicting wind velocity is discussed. Reynolds-averaged Navier-Stokes (RANS) simulations of the Michaelstadt wind tunnel experiment and the Tel Aviv center are used to supervise a machine learning function. Using the machine learning function it is possible to observe wind flow patterns in the form of eddies and spirals emerging from street canyons. The flow patterns observed in urban canopies tend to be predominantly localized, as the machine learning algorithms utilized for flow prediction are based on local morphological features.
引用
收藏
页数:17
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