Multifidelity Sparse Polynomial Chaos Surrogate Models Applied to Flutter Databases

摘要

Multifidelity sparse polynomial chaos expansion (MFSPCE) models of critical flutter dynamic pressures as a function of Mach number, angle of attack, and thickness-to-chord ratio are constructed in lieu of solely using computationally expensive high-fidelity engineering analyses. Compressed sensing is used to determine a sparse representation, and an all-at-once approach is employed to create multifidelity polynomial chaos expansions with hybrid additive/multiplicative bridge functions. To demonstrate that accurate MFSPCE models can be obtained at lower computational cost than high-fidelity full-order polynomial chaos expansions, two analytic test functions and a more complex application example, which is the well-known Advisory Group for Aerospace Research and Development 445.6 aeroelastic model, are employed. The high- and low-fidelity levels considered are Euler and panel solutions, respectively, which are all combined with a modal structural solver.