Two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model

基于CFD收敛提前终止和变复杂度模型的两级气动优化方法

Received: 12 May 2020

Abstract

Efficient aerodynamic design optimization method is of great value for improving the aerodynamic performance of little UAV's airfoil. Using engineering or semi-engineering estimation method to analyze aerodynamic forces in solving aerodynamic optimization problems costs little computational time, but the accuracy cannot be guaranteed. However, CFD method ensuring high accuracy needs much more computational cost, which is unfordable for optimization. Surrogate-based optimization can reduce the number of high-fidelity analyses to increase the optimization efficiency. However, the cost of CFD analyses is still huge for aerodynamic optimization due to multiple design variables, multi-optimal and strong nonlinearities. To solve this problem, a two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model is proposed. In the first optimization stage, the solutions by early termination CFD convergence and the convergenced CFD solutions are regarded as low-and high-fidelity data respectively for building variable-fidelity model. Then, the multi-island genetic algorithm is used in the global optimization based on the built variable-fidelity model. The modeling efficiency can be greatly improved due to many cheap low-fidelity data. In the second stage optimization, the global optimum from the first optimization stage is treated as the start of the Hooke-Jeeves algorithm to search locally based on convergenced CFD computations in order to acquire better-optimum. The proposed method is utilized in optimizing the aerodynamic performance of the airfoil of little UAV, and is compared with the EGO method based on single-fidelity Kriging surrogate model. The results show that the present two-level aerodynamic optimization method consumes less time.

摘要

高效的气动优化设计方法对于提升小型无人飞行器翼型的气动性能具有重要的价值。针对CFD分析成本较大的问题，提出了基于CFD收敛提前终止和变复杂度模型的两级气动优化方法。在第一级优化中，分别将CFD收敛提前终止和CFD完全收敛的数据作为低、高精度样本建立变复杂度模型。采用多岛遗传算法基于变复杂度模型进行全局优化。第二级优化中，以第一级全局最优解为初值，采用Hooke-Jeeves算法直接基于CFD完全收敛分析进行优化，从而得到局部更精确的解。采用该方法对小型无人飞行器的翼型进行了气动优化设计，并与基于单一精度Kriging代理模型的EGO方法进行了对比。结果表明，文中所提的两级气动优化方法所需的优化耗时更少。