Multiobjective Aerodynamic Design Optimization of the NASA Common Research Model

TL;DR

This paper introduces a novel multiobjective Bayesian optimization method for aircraft aerodynamic design, improving sample efficiency and accuracy over existing approaches by generating Pareto optimal solutions using Gaussian process surrogates.

Contribution

The paper proposes a new batch Pareto optimal Thompson sampling method that enhances multiobjective aerodynamic design optimization in terms of efficiency and scalability.

Findings

Outperforms state-of-the-art methods in synthetic experiments

Achieves better Pareto front approximation in NASA research model case

Demonstrates improved sample efficiency and accuracy

Abstract

Aircraft aerodynamic design optimization must account for the varying operating conditions along the cruise segment as opposed to designing at one fixed operating condition, to arrive at more realistic designs. Conventional approaches address this by performing a ``multi-point'' optimization that assumes a weighted average of the objectives at a set of sub-segments along the cruise segment. We argue that since such multi-point approaches are, inevitably, biased by the specification of the weights, they can lead to sub-optimal designs. Instead, we propose to optimize the aircraft design at multiple sub-segments simultaneously -- that is, via multiobjective optimization that leads to a set of Pareto optimal solutions. However, existing work in multiobjective optimization suffers from (i) lack of sample efficiency (that is, keeping the number of function evaluations to convergence minimal), (ii) scalability {in the absence of derivative information}, and (iii) the ability to generate a batch of iterates for synchronous parallel evaluations. To overcome these limitations, we {apply} a novel multiobjective Bayesian optimization methodology {for aerodynamic design optimization} that demonstrates improved sample efficiency and accuracy compared to the state of the art. Inspired by Thompson sampling, our approach leverages Gaussian process surrogates and Bayesian decision theory to generate a sequence of iterates according to the probability that they are Pareto optimal. Our approach, named batch Pareto optimal Thompson sampling (\qpots)\footnote{Here, $q$ stands for selecting a batch of $q$ iterates at every step.}, demonstrates superior empirical performance on a variety of synthetic experiments as well as a $24$ dimensional two-objective aerodynamic design optimization of the NASA common research model. We also provide open-source software of our methodology {and experiments}.