Constrained Aerodynamic Shape Optimization Using Neural Networks and Sequential Sampling

Aerodynamic shape optimization (ASO) involves computational fluid dynamics (CFD)-based search for an optimal aerodynamic shape such as airfoils and wings. Gradient-based optimization (GBO) with adjoints can be used efficiently to solve ASO problems with many design variables, but problems with many constraints can still be challenging. The recently created efficient global optimization algorithm with neural network (NN)-based prediction and uncertainty (EGONN) partially alleviates this challenge. A unique feature of EGONN is its ability to sequentially sample the design space and continuously update the NN prediction using an uncertainty model based on NNs. This work proposes a novel extension to EGONN that enables efficient handling of nonlinear constraints and a continuous update of the prediction and prediction uncertainty data sets. The proposed algorithm is demonstrated on constrained airfoil shape optimization in transonic flow and compared against state-of-the-art GBO with adjoints. The results show that the proposed constrained EGONN algorithm yields comparable optimal designs as GBO at a similar computational cost.