Surrogate Model Development for Optimized Blended-Wing-Body Aerodynamics

Abstract

In the conceptual design phase of conventional-configuration aircraft, calibrated low-fidelity methods provide sufficiently accurate estimates of aerodynamic coefficients. It has been observed, however, that for blended-wing–body aircraft, important flow effects are not captured adequately with low-fidelity aerodynamic tools. Consequently, high-fidelity methods become necessary to study blended-wing–body aerodynamics. Since repeated function calls are needed in an optimization loop, high-fidelity analysis is prohibitively expensive in the conceptual design phase, where several optimization scenarios are considered. In this paper, the integration of high-fidelity data for blended-wing–body aircraft for a mission calculation module is presented. A surrogate model based on Gaussian processes (GPs) with acceptably low prediction error is sought as an alternative to Reynolds-averaged Navier–Stokes computational fluid dynamics. Three adaptations are considered: sparse GPs, mixtures of GP approximators, and need-based filtering for GP. The results provide benchmark values for this case and show that the combination of subsonic and transonic behaviors in the training set is problematic and that, for the considered datasets, sparse GP models suffer from oversmoothing, while mixtures of GP models suffer from overfitting. From the error levels, it is observed that a GP with an infinitely differentiable squared exponential kernel based on reduced data pertinent to mission analysis is the most effective option.