Multifidelity flutter prediction using local corrections to the generalized AIC

Abstract

This work adopts the manifold mapping multifidelity modeling technique for frequency-domain flutter prediction. While this type of multifidelity model is usually employed in a trust-region optimization framework, the current approach uses it, in combination with the p-k method, to find a critical point in the high-fidelity Mach-reduced frequency space (namely, the matched flutter point for a given density and sound speed). This is done by first computing a grid of low-fidelity (doublet lattice-based) aerodynamic influence coefficients in modal coordinates. After starting at the low-fidelity match point, the process iteratively approaches the high-fidelity (Euler-based) match point using a single Mach and reduced frequency evaluation per iteration. The locally accurate multifidelity model operates by applying a correction matrix to the low-fidelity response using the changes in high- and low-fidelity response vectors between iterations; the correction matrix is computed using the Moore-Penrose pseudoinverse operator, making it reminiscent of various doublet lattice correction methods. When applied to a common test case, the process is shown to converge in 4 to 8 high-fidelity evaluations, depending on the fluid density and sound speed. For comparison, standard time- and frequency domain methods are estimated to be 3 to 10 times more costly in terms of time steps or frequency evaluations.