Construction of Dynamic Multifidelity Locally Optimized Surrogate Models

Abstract

The conceptual design phase for the development of new and revolutionary aircraft should include both low- and high-fidelity multidisciplinary engineering analyses to enable reasonable computational cost and accuracy tradeoffs. Multifidelity locally optimized surrogate models can be employed to fuse the multifidelity information to give an accurate representation of the underlying design space. These surrogates can be further enhanced with derivative information and augmented with dynamic training point selection to reduce overall computational cost. In this paper, a framework for multifidelity locally optimized surrogate models is developed, and the superior accuracy for the same computational cost or cheaper cost for the same accuracy compared to standard single-fidelity global surrogate models is demonstrated via analytic test functions. The multifidelity locally optimized surrogate models are also applied to the creation of an aerodynamic database involving the steady turbulent flow around a NACA 00xx airfoil, where the influence of Mach number, angle of attack, and thickness-to-chord ratio variations on the lift and drag coefficient are studied.