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PDF papers
id ecaade2013_090
authors Wilkinson, Samuel; Hanna, Sean; Hesselgren, Lars and Mueller, Volker
year 2013
title Inductive Aerodynamics
source Stouffs, Rudi and Sariyildiz, Sevil (eds.), Computation and Performance – Proceedings of the 31st eCAADe Conference – Volume 2, Faculty of Architecture, Delft University of Technology, Delft, The Netherlands, 18-20 September 2013, pp. 39-48
wos WOS:000340643600003
summary 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.
keywords Machine learning; CFD; tall buildings; wind loads; procedural modelling.
series eCAADe
full text file.pdf (1,337,508 bytes)
references Content-type: text/plain
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