Inverse Aerodynamic Design Using Neural Networks - Rubén Sevilla (Advances in Computational Methods and Technologies in Aeronautics and Industry, 2022)
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.