[3R4-OS-27] OS-27

[3R4-OS-27-05] A physics-informed neural networks approach to calculating the fluid dynamics of vessels using surrogate models as an alternative to computational fluid dynamics

〇Konobu Kimura¹, Akito Ueshina¹, Shin Okamoto¹, Yuto Miyake¹, Hiroyuki Hatakenaka ¹, Hitoshi Maeno¹, Daisuke Arai², Keisuke Tajima², Kenta Koike² (1. Furuno Electric, Co., Ltd, 2. Shin Kurushima Sanoyas Shipbuilding Co., Ltd.)

Keywords:Surrogate Model, Computational Fluid Dynamics, Physics-Informed Neural Network, vessel

Computational fluid dynamics (CFD) methods are subject to a trade-off between accuracy and computational complexity. Specifically, fluid dynamics calculations used for ships require high levels of accuracy, which increases the amount of computational resources needed.
In recent years, deep learning-based surrogate models have been proposed as an alternative to CFD to increase CFD speed. In general, supervised learning requires a large amount of CFD calculation results to be prepared in advance as training data in order to obtain sufficient accuracy, and it is practically impossible to collect data covering a wide variety of ship types.
In this study, the concept of Physics-Informed Neural Network (PINN) is introduced. For preparing the training dataset, mathematical virtual hulls (wigley models) are created with varying deformations, as well as spheroid and cylindrical shapes. Further, the neural network was trained in accordance with the physical boundary conditions as well as the hydrodynamic equations. Consequently, our model was constructed several hundred times faster than conventional CFD analysis while maintaining fluid behavior.
Further validation will be conducted using a model ship as well as a full-scale ship in order to develop an AI system that will allow us to achieve efficient fluid dynamics analysis even in environments with limited computing capacity.