Knowledge Transfer-Accelerated Intelligent Aerodynamic Design Optimization

To address the gap between the minimum number of evaluations required for refined aerodynamic shape optimization tasks and the maximum number of evaluations constrained by the mission period, a study on intelligent aerodynamic design optimization accelerated by knowledge transfer was conducted based on the concept of transfer learning in the field of machine learning. Firstly, an airfoil variational autoencoder(VAE)model was built. The model's decoder enabled the intelligent parameterization of aerodynamic shapes, while its encoder unified samples from source tasks into the parameterization space of the target task. Secondly, the Bayesian transfer optimization algorithm was established, incorporating both single and multi-fidelity surrogate models. Then, an intelligent transfer optimization framework for aerodynamic shapes was established by integrating the airfoil VAE model and Bayesian transfer optimization algorithm. Last, task correlation analysis was made to discuss the mechanism of the optimization process accelerated by knowledge transfer. The results from airfoil optimization test demonstrate that such a framework improves the performance of median of the optimal solution by 4.8% compared to using VAE optimization lack of knowledge transfer, and by more than 19.9% compared to other methods, confirming the effectiveness of knowledge transfer strategy. © 2023 Xi'an Jiaotong University. All rights reserved.