Hot-start Strategy Based on POD-BPNN Model and Its Application in Aerodynamic Surrogate-based Optimization

Abstract

The traditional aerodynamic optimization design requires computational fluid dynamics(CFD)analysis,and the surrogate-based optimization(SBO)method can effectively reduce the number of CFD analyse,but it cannot speed up a single CFD analysis time.A hot-start strategy using the proper orthogonal decomposition-back propagation based neural network(POD-BPNN)model is proposed,and applied in surrogate-based aerodynamic optimization.The prediction model from geometric design variables to flowfield data through the initial samples is built with SBO POD-BPNN model.During iteration of the SBO,the flowfield of a new sample is predicted by the built model,and the flowfield is used as the initial flowfield of the hot-start CFD analysis for the new sample.The new sample is used to update the POD-BPNN model until the end of the optimization.The comparison verification of proposed strategy is performed with instance.The results show that,in the aerodynamic optimization design of the transonic airfoil,the hot-start strategy based on POD-BPNN model can reduce the time of a single CFD analysis by 68%,and improve the efficiency of the SBO by 37%.

Keywords

• pod
• bpnn
• hot-start
• aerodynamic optimization
• airfoil design