Low-Cost Design of Transonic Airfoils Using Variable-Fidelity Surrogates

Abstract

In the paper, a low-cost design technique for transonic airfoils is discussed. We exploit the surrogate-based optimization principle where the direct optimization of the high-fidelity (accurate but computationally expensive) airfoil model is replaced by iterative optimization of a properly corrected low-fidelity model. Both models are based on the same governing fluid flow equations (the Euler equations), however, the latter uses coarser discretization and relaxed convergence criteria. The shape-preserving response prediction (SPRP) technique is utilized to predict the high-fidelity model response, here, the airfoil pressure distribution. In this prediction process, SPRP employs the actual changes of the low-fidelity model response due to the design variable adjustments. The optimization algorithm is embedded in the trustregion framework to ensure good convergence properties. The airfoils are parameterized using Bezier curves with a total of eleven design variables. Examples demonstrate that the optimized airfoil design can be obtained at a low computational cost.