Inductive Aerodynamics

Conference paper (2013)
Copyright: © 2013 Wilkinson, S.; Hanna, S.; Hesselgren, L.; Mueller, V.
Machine learning; CFD; tall buildings; wind loads; procedural modelling.
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Published Date

18-09-2013

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Abstract

A novel approach is presented to predict wind pressure on tall buildings for early-stage generative design exploration and optimisation. The method provides instantaneous surface pressure data, reducing performance feedback time whilst maintaining accuracy. This is achieved through the use of a machine learning algorithm trained on procedurally generated towers and steady-state CFD simulation to evaluate the training set of models. Local shape features are then calculated for every vertex in each model, and a regression function is generated as a mapping between this shape description and wind pressure. We present a background literature review, general approach, and results for a number of cases of increasing complexity.