In this work, we focus on the early design phase of cruise ship hulls, where the designers are tasked with ensuring the structural resilience of the ship against extreme waves while reducing steel usage and respecting safety and manufacturing constraints. The ship's geometry is already finalized and the designer can choose the thickness of the primary structural elements, such as decks, bulkheads, and the shell. Reduced order modeling and black-box optimization techniques reduce the use of expensive finite element analysis to only validate the most promising configurations, thanks to the efficient exploration of the domain of decision variables. However, the quality of the results heavily relies on the problem formulation, and on how the structural elements are assigned to the decision variables. A parameterization that does not capture well the stress configuration of the model prevents the optimization procedure from achieving the most efficient allocation of the steel. To address this issue, we extended an existing pipeline for the structural optimization of cruise ships developed in collaboration with Fincantieri S.p.A. with a novel data-driven reparameterization procedure, based on the optimization of a series of sub-problems. Moreover, we implemented a multi-objective optimization module to provide the designers with insights into the efficient trade-offs between competing quantities of interest and enhanced the single-objective Bayesian optimization module. The new pipeline is tested on a simplified midship section and a full ship hull, comparing the automated reparameterization to a baseline model provided by the designers. The tests show that the iterative refinement outperforms the baseline on the more complex hull, proving that the pipeline streamlines the initial design phase, and helps the designers tackle more innovative projects.

翻译：本研究聚焦于邮轮船体的早期设计阶段，在此阶段，设计人员需确保船舶在极端波浪下的结构韧性，同时减少钢材用量并满足安全与制造约束。船舶几何形状已确定，设计人员可选择甲板、舱壁及外壳等主要结构元件的厚度。通过降阶建模与黑箱优化技术，结合决策变量域的高效探索，可将昂贵的有限元分析仅用于验证最具潜力的配置方案。然而，优化结果的质量在很大程度上取决于问题表述方式以及结构元件如何分配给决策变量。若参数化方法未能充分捕捉模型的应力分布，将阻碍优化过程实现最有效的钢材配置。为解决此问题，我们在与Fincantieri S.p.A.合作开发的邮轮结构优化现有流程基础上，引入了一种基于子问题序列优化的新型数据驱动重参数化流程。此外，我们实现了多目标优化模块，为设计人员揭示竞争性关注量之间的有效权衡关系，并增强了单目标贝叶斯优化模块。新流程在简化中横剖面和完整船体模型上进行了测试，将自动化重参数化结果与设计人员提供的基准模型进行对比。测试表明，在更复杂的船体案例中，迭代细化方法优于基准模型，证明该流程能优化初始设计阶段，并助力设计人员应对更具创新性的项目。