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.

翻译：飞行器设计高度依赖于求解具有挑战性且计算代价高昂的多学科设计优化问题。在此背景下，多保真度模型在贝叶斯优化中日益受到关注，通过结合高保真与低保真仿真模型来平衡计算成本与精度，从而以最小计算开销高效探索设计过程。现有文献中，保真度选择仅聚焦于目标函数，以决定如何整合多重保真度层级，并利用方差缩减准则平衡精度与计算成本。本研究提出新颖的多保真度选择策略，具体而言，我们论证了如何通过整合目标函数与约束条件的信息，在不妨碍解的最优性的前提下进一步降低计算成本。我们通过四个解析测试案例验证了所提多保真度优化策略的有效性，并将其成功应用于求解具有挑战性的飞行器机翼气动结构设计问题。在该问题中，气动分析采用线性涡格法，结构分析采用有限元法。研究表明，在有限预算下，与现有最先进方法相比，采用所提多保真度方法可使满足约束条件的解数量增加86%至200%。