Modeling the Effect of Topography on Wind Flow Using a Combined Numerical–Neural Network Approach

Abstract

The impact of topography on design wind speed is addressed in current building codes and design standards by providing ratios for limited cases of terrain geometries. This paper proposes a combined numerical–neural network (NN) approach to provide speed-up ratios for a wide range of topographic features such as single and multiple hills, escarpments, and valleys. In this approach learning data required by the NN is generated via a detailed numerical approach based on computational fluid dynamics (CFD). Use of the developed model only requires simple geometrical input such as slope, height, and ground roughness while producing results of comparable accuracy to complex numerical evaluations. This combined CFD-NN approach not only produces data for new cases but also conveys the results of complex CFD simulations to the engineering profession (end user). Results compare well with an independent set of experimental data demonstrating the feasibility of the CFD-NN approach to generate data to apply wind design load provisions to buildings with upstream complex terrain.