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

2026-02-09

AI Summary

This study addresses the high-dimensional design optimization challenge arising from the strong coupling among hull geometry, buoyancy, structural configuration, and layout in bio-inspired underwater gliders. To tackle this, a bi-level multidisciplinary design optimization framework is proposed: the upper level employs a physics-informed parametric model for efficient hull shape exploration, while the lower level determines the minimum dry weight configuration satisfying multiple disciplinary constraints. By integrating multi-fidelity surrogate models, stochastic radial basis functions, and adaptive Bayesian sampling, the approach significantly enhances computational efficiency. The method achieves scalable and efficient optimization while preserving physical consistency, yielding a 14.7% improvement in maximum hydrodynamic efficiency and a 12.8% reduction in dry weight compared to the baseline design, all while fully satisfying multidisciplinary constraints.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Autonomous Underwater Glider
• Bio-inspired Design
• Multidisciplinary Design Optimization
• High-dimensional Design Space
• Hydrodynamic Efficiency

Innovation

• multidisciplinary design optimization
• machine learning
• bio-inspired underwater glider
• multi-fidelity surrogate
• physics-driven dimensionality reduction