Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization

Abstract

In the present study, an effective optimization framework of aerodynamic shape design is established based on the multi-fidelity deep neural network (MFDNN) model. The objective of the current work is to construct a high-accuracy multi-fidelity surrogate model correlating the configuration parameters of an aircraft and its aerodynamic performance by blending different fidelity information and adaptively learning their linear or nonlinear correlation without any prior assumption. In the optimization framework, the high-fidelity model using a CFD evaluation with fine grid and the low-fidelity model using the same CFD model with coarse grid are applied. Moreover, in each optimization iteration, the high-fidelity infilling strategy by adding the current optimal solution of surrogate model into the high-fidelity database is applied to improve the surrogate accuracy. The low-fidelity infilling strategy which can generate the solutions distributed uniformly in the whole design space is used to update the low-fidelity database for avoiding local optimum. Then, the proposed multi-fidelity optimization framework is validated by two standard synthetic benchmarks. Finally, it is applied to the high-dimensional aerodynamic shape optimization of a RAE2822 airfoil parameterized by 10 design variables and a DLR-F4 wing-body configuration parameterized by 30 design variables. The optimization results demonstrate that the proposed multi-fidelity optimization framework can remarkably improve optimization efficiency and outperform the single-fidelity method.