Multi-objective hull form optimization utilizing sequential sampling optimization method

Abstract

With advancements in computer technology, Computational fluid dynamics (CFD) theory provides a numerical tool for evaluating the comprehensive hydrodynamic performance of ships. On this basis, the Simulation-based design (SBD) technology based on the static sampling method (SSM) which is termed as SBD-SSM in this paper is recognized as a hull form optimization method with significant advantages. However, in the SBD-SSM method, the excessive pursuit of the global accuracy of the surrogate model leads to the need for more CFD evaluations. Consequently, this demands the substantiality of computing resources and diminishes optimization efficiency. We apply a novel hull form optimization method based on the Sequential Sampling-based Optimization (SBO) method to address this challenge. Furthermore, to strike an appropriate balance between prediction value and uncertainty while finding optimal solutions, we employ the adaptive sampling method known as Pseudo Expected Improvement Matrix (PEIM). By integrating SBO with PEIM, we introduce the SBO-PEIM optimization method, initially applied through mathematical optimization functions to verify its feasibility in optimization. Subsequently, we apply this method to optimize the total resistance and wake performance of the KCS ship. Our findings demonstrate that the SBO-PEIM method achieves higher prediction accuracy in near-optimal solutions than the SBD-SSM method, enhancing optimization efficiency by at least 17.5%. It validates the efficacy of the SBO-PEIM approach in addressing real-world engineering optimization challenges.