Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs

Abstract

The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in improving intelligent design levels, such as the insufficient utilization of optimization process data and optimization parameters’ adjustment that depends on the designer’s intervention and experience. To solve the above problems, a novel aerodynamic data-driven surrogate-assisted teaching-learning-based optimization (TLBO) framework is proposed for constrained aerodynamic shape optimization (ASO). The main contribution of the study is that ASO is promoted using historically aerodynamic process data generated during the gradient free optimization process. Meanwhile, nonparametric adjustment of the TLBO algorithm can help relieve manual design experience for actual engineering applications. Based on the structure of the TLBO algorithm, a model optimal prediction method is proposed as the new surrogate-assisted support strategy to accelerate the ASO process. The proposed method is applied to airfoil and wing shape designs to verify the optimization effect and efficiency. A benchmark aerodynamic design optimization is employed for the drag minimization of the RAE2822 airfoil. The optimized results indicate that the proposed method has advantages of high efficiency, strong optimization ability, and nonparametric characteristics for ASO. Moreover, the results of the wing shape optimization verify the advantages of the proposed methods over the surrogate-based optimization and direct optimization frameworks.