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2022 | OriginalPaper | Chapter

10. Inverse Aerodynamic Design Using Neural Networks

Authors : Kensley Balla, Ruben Sevilla, Oubay Hassan, Kenneth Morgan

Published in: Advances in Computational Methods and Technologies in Aeronautics and Industry

Publisher: Springer International Publishing

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Abstract

An efficient computational framework is presented and applied to the inverse aerodynamic shape design problem. The main building block is a novel neural network capable to accurately predict the pressure distribution on aerofoils and wings. The trained neural network is used to accelerate the evaluation of the objective function in an optimisation algorithm based on the gradient-free modified cuckoo search method. Two applications are presented in two and three dimensions for problems involving up to 50 geometric parameters.

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Metadata
Title
Inverse Aerodynamic Design Using Neural Networks
Authors
Kensley Balla
Ruben Sevilla
Oubay Hassan
Kenneth Morgan
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-031-12019-0_10

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