In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.

翻译：本文提出了一种内置的深度学习物理场优化（DLPO）框架，用于对杜伊斯堡测试案例（DTC）集装箱船进行船型优化研究。我们展示了两个不同应用：（1）灵敏度分析，以识别最具潜力的通用基础船型；（2）多目标优化，以量化最优船型之间的权衡。DLPO框架能够以端到端方式自动评估设计迭代。我们通过将Extrality的深度学习物理场（DLP）模型与CAD引擎及优化器耦合实现了这些结果。所提出的DLP模型基于来自RANS模拟的全三维体积数据进行训练，能够实时提供准确且高质量的三维流场预测，使其成为评估新型集装箱船设计 hydrodynamic 效率的优良评估器。特别地，它能够通过对船体表面积分恢复作用在船体上的力，总阻力的平均相对误差为3.84% ± 2.179%。每次迭代仅需20秒，从而大幅节省时间和工程精力，同时提供包括类RANS详细流场信息在内的宝贵船舶性能洞察。我们得出结论，DLPO框架是加速船舶设计过程、实现具有更好水动力性能的更高效船舶的有前景工具。