Aerodynamic shape optimization using a novel optimizer based on machine learning techniques

摘要

Aerodynamic shape optimization is usually a loop of an optimization model, an optimizer and an evaluation workflow. A new optimizer is proposed and tested for a typical aerodynamic shape optimization of missile control surfaces with computational fluid dynamics (CFD). The new optimizer emphasizes the use of machine learning techniques, reinforcement learning and transfer learning, to improve performance and efficiency. Reinforcement learning is applied to extract the optimization experience from the semi-empirical method DATCOM using deep neural networks. Transfer learning is implemented to reuse the experience as priori knowledge in the CFD-based optimization by sharing neural network parameters. For the considered aerodynamic shape optimization problem of missile control surfaces, a remarkable reduction in the computational time has been accomplished. The new approach significantly decreases the required CFD calls by over 62.5%. Meanwhile, the time spent in the experience extraction and parameter transfer process is negligible. (C) 2019 Elsevier Masson SAS. All rights reserved.