Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design

• URL: http://arxiv.org/abs/2302.14732v1
• Date: Tue, 28 Feb 2023 16:36:26 GMT
• Title: Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design
• Authors: Harsh Vardhan, Peter Volgyesi, Janos Sztipanovits
• Abstract summary: This paper introduces the automatic design optimization of underwater vehicle hull design. We use the technique of Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design.
• Score: 0.6767885381740952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool.

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