The preliminary design of AUGs is intrinsically challenging due to the strong coupling between the external hydrodynamic shape, the hydrostatic balance, the structural integrity, and internal packaging constraints. This complexity is further amplified for bio-inspired configurations, whose rich geometric parametrizations lead to high-dimensional design spaces that are difficult to explore using conventional optimization approaches. This work presents a ML-enabled bi-level multidisciplinary design optimization (MDO) framework for the performance-driven design of a manta-ray-inspired AUG. At the upper level, hydrodynamically efficient external geometries are explored in a reduced design space obtained through physics-driven parametric model embedding, which identifies a low-dimensional latent representation directly correlated with the lift, drag, and pressure distributions. At the lower level, a constrained internal sizing problem determines the minimum feasible empty weight by accounting for structural, hydrostatic, geometric, and payload constraints. To render the resulting bi-level problem computationally tractable, a multi-fidelity surrogate-based optimization strategy is adopted, combining low- and high-fidelity hydrodynamic models with stochastic radial basis function surrogates and adaptive Bayesian sampling. The framework enables efficient exploration of the coupled design space while rigorously managing model uncertainty and computational cost. The optimized configurations exhibit a 14.7\% improvement in maximum hydrodynamic efficiency and a 12.8\% reduction in empty weight relative to the baseline design, while satisfying all disciplinary constraints. These results demonstrate that the integration of physics-driven dimensionality reduction and multi-fidelity machine learning enables scalable and physically consistent MDO of complex bio-inspired underwater vehicles.

翻译：自主水下滑翔机的初步设计本质上具有挑战性，这源于外部水动力外形、静水平衡、结构完整性以及内部布局约束之间的强耦合关系。对于仿生构型，这种复杂性进一步加剧，其丰富的几何参数化导致了高维设计空间，难以使用传统优化方法进行探索。本研究提出了一种机器学习驱动的双层多学科设计优化框架，用于以性能为导向设计受蝠鲼启发的自主水下滑翔机。在上层，通过物理驱动的参数模型嵌入获得一个降维设计空间，在此空间中探索水动力高效的外部几何形状；该嵌入方法识别出一个与升力、阻力和压力分布直接相关的低维潜在表示。在下层，一个受约束的内部尺寸确定问题通过考虑结构、静水、几何和有效载荷约束，确定了最小可行空重。为使所得双层问题在计算上易于处理，采用了基于多保真度代理模型的优化策略，将低保真度与高保真度水动力模型、随机径向基函数代理模型以及自适应贝叶斯采样相结合。该框架能够在严格管理模型不确定性和计算成本的同时，高效探索耦合设计空间。优化后的构型相较于基准设计，最大水动力效率提升了14.7%，空重降低了12.8%，且满足所有学科约束。这些结果表明，物理驱动的降维与多保真度机器学习的结合，能够实现复杂仿生水下航行器的可扩展且物理一致的多学科设计优化。