Design optimisation offers the potential to develop lightweight aircraft structures with reduced environmental impact. Due to the high number of design variables and constraints, these challenges are typically addressed using gradient-based optimisation methods to maintain efficiency. However, this approach often results in a local solution, overlooking the global design space. Moreover, gradients are frequently unavailable. Bayesian Optimisation presents a promising alternative, enabling sample-efficient global optimisation through probabilistic surrogate models that do not depend on gradients. Although Bayesian Optimisation has shown its effectiveness for problems with a small number of design variables, it struggles to scale to high-dimensional problems, particularly when incorporating large-scale constraints. This challenge is especially pronounced in aeroelastic tailoring, where directional stiffness properties are integrated into the structural design to manage aeroelastic deformations and enhance both aerodynamic and structural performance. Ensuring the safe operation of the system requires simultaneously addressing constraints from various analysis disciplines, making global design space exploration even more complex. This study seeks to address this issue by employing high-dimensional Bayesian Optimisation combined with a dimensionality reduction technique to tackle the optimisation challenges in aeroelastic tailoring. The proposed approach is validated through experiments on a well-known benchmark case with black-box constraints, as well as its application to the aeroelastic tailoring problem, demonstrating the feasibility of Bayesian Optimisation for high-dimensional problems with large-scale constraints.

翻译：设计优化为开发轻量化飞机结构并降低环境影响提供了潜力。由于设计变量和约束数量庞大，这类挑战通常采用基于梯度的优化方法以保证效率。然而，该方法往往仅获得局部解，而忽略了全局设计空间。此外，梯度信息常常无法获取。贝叶斯优化提供了一种有前景的替代方案，它通过不依赖梯度的概率代理模型实现了样本高效的全局优化。尽管贝叶斯优化在少量设计变量问题上已展现其有效性，但难以扩展至高维问题，尤其是在纳入大规模约束时。这一挑战在气动弹性剪裁中尤为突出，该领域通过将方向刚度特性融入结构设计来管理气动弹性变形并提升气动与结构性能。确保系统安全运行需要同时满足来自不同分析学科的约束条件，这使得全局设计空间探索更为复杂。本研究旨在通过采用结合降维技术的高维贝叶斯优化来应对气动弹性剪裁中的优化挑战。所提方法通过在具有黑盒约束的经典基准案例上的实验及其在气动弹性剪裁问题中的应用得到验证，证明了贝叶斯优化在处理具有大规模约束的高维问题上的可行性。