Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design

TL;DR

This paper introduces novel multi-fidelity Bayesian optimization strategies that incorporate both objective and constraint information, significantly reducing computational costs in aircraft design problems.

Contribution

It proposes new multi-fidelity selection methods that improve efficiency by considering constraints, validated on analytical and real aircraft design cases.

Findings

Achieved 86% to 200% more constraint compliant solutions under limited budgets.

Validated the approach on four analytical test cases and an aircraft wing design problem.

Demonstrated substantial computational savings over existing methods.

Abstract

Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Specifically, we demonstrate how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution. We validate the proposed multi-fidelity optimization strategy by applying it to four analytical test cases, showcasing its effectiveness. The proposed method is used to efficiently solve a challenging aircraft wing aero-structural design problem. The proposed setting uses a linear vortex lattice method and a finite element method for the aerodynamic and structural analysis respectively. We show that employing our proposed multi-fidelity approach leads to $86\%$ to $200\%$ more constraint compliant solutions given a limited budget compared to the state-of-the-art approach.