A Machine Learning Enabled MDO for Bio-Inspired Autonomous Underwater Gliders

TL;DR

This paper introduces a machine learning-enabled bi-level multidisciplinary design optimization framework for bio-inspired underwater gliders, significantly improving their hydrodynamic efficiency and reducing weight through physics-based dimensionality reduction and multi-fidelity surrogate modeling.

Contribution

It presents a novel ML-based MDO approach that effectively explores high-dimensional design spaces of bio-inspired underwater vehicles using physics-driven models and surrogate-based optimization.

Findings

14.7% improvement in hydrodynamic efficiency

12.8% reduction in empty weight

Efficient exploration of complex design spaces with reduced computational cost

Abstract

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.