Adjoint-Free Aerodynamic Shape Optimization of the Common Research Model Wing

Adjoint-free methods are required in aerodynamic shape design optimization if an adjoint solver is unavailable. However, their performance is highly criticized in high-dimensional problems like wing shape design optimization. This work proposes an adjoint-free optimization method using deep-learning techniques to address the issue. A deep-learning-based optimal sampling method is developed to generate various wing shapes subject to both geometric validity and feasibility constraints. To address the curse of dimensionality in adjoint-free optimization, a compact wing shape parameterization method is presented by deriving global wing mode shapes from the sample wings. The proposed method is compared with the adjoint-based optimization method in both single-point and multipoint design of the Common Research Model wing. The proposed adjoint-free optimization method converges within 1000 objective function evaluations. The optimized shapes are close to those obtained by the adjoint-based optimization, and the differences in are all within 0.5 counts. The results show that the proposed adjoint-free optimization method has almost the same efficiency and effectiveness as the adjoint-based optimization method in high-dimensional wing shape design.