Multi-fidelity Bayesian Optimization of SWATH Hull Forms

Abstract

This study presents a multi-fidelity framework that enables the construction of surrogate models capable of capturing complex correlations between design variables and quantities of interest. Resistance in calm water is investigated for a SWATH hull in a multidimensional design space using a new method to derive high-quality response surfaces through machine learning techniques based on a low number of high-fidelity computations and a larger number of less-expensive low-fidelity computations. First, a verification and validation study is presented with the goal of comparing and ranking numerical methods against experiments performed on a conventional SWATH geometry. Then, the hull geometry of a new family of unconventional SWATH hull forms with twin counter-canted struts is parametrically defined and sequentially refined using multi-fidelity Bayesian optimization. Ship resistance in calm water is finally predicted using observations from two different fidelity levels. We demonstrate that the multi-fidelity optimization framework is successful in obtaining an optimized design using a small number of high-fidelity computations and a larger number of low-fidelity computations. Simulation and optimization costs are reduced by orders of magnitude, providing accurate certificates of fidelity for the performance of the proposed design.