AI/ML APPLICATIONS FOR SHIP DESIGN

Abstract

This work uses Simcenter STAR-CCM+ to generate CFD data for determining the powering characteristics of a vessel and Simcenter ROM Builder and Monolith for using that data in no-code machine learning pipelinesto allow hull designers to understand how design variants affect vessel performance instantly. Firstly, in terms of capabilities, the machine learning models were able to predict several 0D performance metrics (torque, total resistance, powering, and propulsion metrics) with respect to 12 independent geometrical parameters involved in the ship design. These predictions were then supplemented with additional machine learning predictions of the spatial flow field as well to provide designers with more detailed understanding of the hydrodynamic aspects of the vessel. Regarding the spatial fields, there are two main capabilities the machine learning models provided: predict results of spatial fields in the form of local contours (loads on the hull, free surface deformation, and in-flow into the propeller), and thereby also an optimization pipeline which can morph the geometry according to the desired cost function for any combination of these machine learning predictions.