Implementation of the Critical Wave Groups Method with Computational Fluid Dynamics and Neural Networks

• URL: http://arxiv.org/abs/2301.09834v1
• Date: Tue, 24 Jan 2023 06:14:58 GMT
• Title: Implementation of the Critical Wave Groups Method with Computational Fluid Dynamics and Neural Networks
• Authors: Kevin M. Silva and Kevin J. Maki
• Abstract summary: This paper introduces a new framework for accurate and efficient prediction of extreme ship responses. The new framework is able to produce predictions that are representative of a purely CFD-driven framework, with two orders of magnitude of computational cost savings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient prediction of extreme ship responses continues to be a challenging problem in ship hydrodynamics. Probabilistic frameworks in conjunction with computationally efficient numerical hydrodynamic tools have been developed that allow researchers and designers to better understand extremes. However, the ability of these hydrodynamic tools to represent the physics quantitatively during extreme events is limited. Previous research successfully implemented the critical wave groups (CWG) probabilistic method with computational fluid dynamics (CFD). Although the CWG method allows for less simulation time than a Monte Carlo approach, the large quantity of simulations required is cost prohibitive. The objective of the present paper is to reduce the computational cost of implementing CWG with CFD, through the construction of long short-term memory (LSTM) neural networks. After training the models with a limited quantity of simulations, the models can provide a larger quantity of predictions to calculate the probability. The new framework is demonstrated with a 2-D midship section of the Office of Naval Research Tumblehome (ONRT) hull in Sea State 7 and beam seas at zero speed. The new framework is able to produce predictions that are representative of a purely CFD-driven CWG framework, with two orders of magnitude of computational cost savings.

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