Simulation-Driven Aerodynamic Shape Optimization with Automated Low-Fidelity Model Setup

Abstract

Accurate high-fidelity Computational Fluid Dynamics (CFD) models may be computationally too expensive for simulation-driven design optimization. Variable-fidelity optimization algorithms have been utilized to reduce high CPU-cost related to the design process solely based on accurate CFD models. The most critical components of such algorithms are the low-fidelity models. Typically, the low-fidelity models employ the same CFD solver as the high-fidelity one, but with reduced discretization density and reduced number of flow solver iterations. The performance of the optimization algorithm strongly depends on the quality of the low-fidelity models. The low-fidelity model grid setup has been based on hands-on parametric studies. In this work, an automated low-fidelity CFD model setup technique is developed. The model setup task is defined as a constrained nonlinear optimization problem and suitable grid and flow solver parameters are obtained numerically. Comparison of the standard and the proposed approach is carried out in the context of aerodynamic design of transonic airfoils. Two variable-fidelity optimization algorithms are used in the study. One algorithm is based on a single corrected low-fidelity CFD model and the other utilizes a family of such models. The results suggest that the automated model generation may lead to significant computational savings of the CFDbased aerodynamic design process.