In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to setup a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present twodifferent 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. DLPOframework allows for the evaluation of design iterations automatically in an end-to-end manner. Weachieved these results by coupling Extrality’s Deep Learning Physics (DLP) model to a CAD engineand an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANSsimulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makesit a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamicefficiency. In particular, it is able to recover the forces acting on the vessel by integration on thehull surface with a mean relative error of 3.84% ± 2.179% on the total resistance. Each iterationtakes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while deliveringvaluable 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 leadto more efficient ships with better hydrodynamic performance.

1/ Extrality, 75002 Paris, France

2/ Bremen University of Applied Sciences, 28199 Bremen, Germany