Bayesian Optimization of a Low-Boom Supersonic Wing Planform

Abstract

A surrogate-assisted methodology to accelerate the design space exploration process and multi-objective optimization of a notional low-drag, low-boom supersonic transport planform using Euler computational fluid dynamics with an empirical parasite drag addition and an augmented Burgers equation solver is demonstrated. The use of Kriging-based surrogate models, coupled with expected hypervolume improvement in a Kriging believer framework, allows efficient global optimization of the selected wing design parameters. The use of effective nondominated sampling is used to further improve the solutions found. The effects of parameterizing the planform of a baseline model (NASA’s 69 deg wing–body) with 6- and 11-variables are explored. Genetic and local optimizers are used to search the Kriging surrogates. The results show the tradeoff between planform shapes that efficiently use compression lift to increase the lift-to-drag ratio and a smooth undertrack pressure signature to reduce ground-level noise. Furthermore, correctly positioned wingtips may also be used to smooth the undertrack signature, reducing the sonic boom.