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Inverse Aerodynamic Design Using Neural Networks

Kensley Balla, Rubén Sevilla Orcid Logo, Oubay Hassan Orcid Logo, Kenneth Morgan Orcid Logo

Advances in Computational Methods and Technologies in Aeronautics and Industry, Volume: 57, Pages: 131 - 143

Swansea University Authors: Rubén Sevilla Orcid Logo, Oubay Hassan Orcid Logo, Kenneth Morgan Orcid Logo

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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 evalua...

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Published in: Advances in Computational Methods and Technologies in Aeronautics and Industry
ISBN: 9783031120183 9783031120190
ISSN: 1871-3033 2543-0203
Published: Cham Springer International Publishing 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62238
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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.
Keywords: Aerodynamic design; Neural network; Optimisation
College: Faculty of Science and Engineering
Start Page: 131
End Page: 143