Transonic Airfoil Shape Optimization Using Variable-Resolution Models and Pressure Distribution Alignment

A computationally efficient and robust methodology for transonic airfoil design is presented. The approach replaces the direct optimization of an accurate, but computationally expensive, high-fidelity airfoil model by an iterative re-optimization of a corrected low-fidelity model. The low-fidelity model is based on the same governing fluid flow equations as the high-fidelity one, but uses coarser discretization and relaxed convergence criteria. The shape-preserving response prediction technique is utilized to align the pressure distribution of the low-fidelity model with that of the high-fidelity model. Our method is applied to constrained airfoil lift maximization and drag minimization in twodimensional inviscid transonic flow. The optimized designs are obtained at substantially lower computational cost when compared to the direct high-fidelity model optimization. The savings are 85 to over 90 percent depending on the test case.