Hierarchical variable fidelity evolutionary optimization methods and their applications in aerodynamic shape design

Abstract

This paper proposes two hierarchical evolutionary optimization methods based on variable fidelity analysis and search space contraction for aerodynamic shape design, i.e. hierarchical evolutionary Pareto and Nash games. One of these techniques is used in the optimization process, namely the advantages of high and low fidelity flow simulation. The high-fidelity model provides solution accuracy while the low-fidelity model reduces the computational cost. Especially, the search space contraction and the population size reduction are introduced in the process of transition from the optimization on low fidelity simulation to the optimization on high fidelity simulation, so that the optimization based on high fidelity simulation can get the high-precision optimal solution with a relatively less Central Processing Unit(CPU) cost. They are applied to the single objective natural laminar wing shape design at transonic flow and the multidisciplinary shape optimization of a hypersonic air-breathing vehicle respectively. The optimization results show that regardless of a single objective or multi-objective/multidisciplinary optimization problem, the new hierarchical optimization methods proposed in this paper can improve the optimization efficiency by 5-10 times.