Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design
This work addresses the complex engineering problem of automated hull design for underwater vehicles, but it is incremental as it applies existing Bayesian optimization methods to a new domain-specific toolchain.
The authors tackled the problem of optimizing underwater vehicle hull designs by integrating CAD and CFD tools with Bayesian optimization, handling design constraints during optimization. They validated their approach on two real-world underwater vehicle design use cases.
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.