Using Machine Learning to Predict Wind Flow in Urban Areas

Abstract

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.