Cumincad : CUMINCAD Papers : Paper ecaade2013

ecaade2013_090

authors

Wilkinson, Samuel; Hanna, Sean; Hesselgren, Lars and Mueller, Volker

year

2013

title

Inductive Aerodynamics

doi

https://doi.org/10.52842/conf.ecaade.2013.2.039

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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2022/06/07 07:57