Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Aerodynamic shape optimization has become of primary importance for the aerospace industry over the last years. Most of the method developed so far have been shown to be either computationally very expensive, or to have low dimensional search space. In this work we present how Geodesic Convolutional Neural Networks can be used as a surrogate model for a computational fluid dynamics solver. The trained model is shown to be capable of efficiently exploring a high dimensional shape space. We apply this method to the optimization of a fixed wing drone, the eBee Classic.


Advisor(s):
Fua, Pascal
Baqué, Pierre Bruno
Year:
Jun 21 2019
Keywords:
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 Record created 2019-07-29, last modified 2019-07-31

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