A data-driven model for nonlinear marine dynamics. (15th September 2021)

• Record Type: Journal Article
• Authors: Xu, Wenzhe; Maki, Kevin J.; Silva, Kevin M.
• Is Part Of: Ocean engineering. Volume 236(2021)
• Publication Date: 2021-09-15
• Subjects: Machine learning -- Long short-term memory neural net -- Computational fluid dynamics -- Ship roll -- Nonlinear wave propagation; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.109469
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: The design and engineering of ships and platforms that operate in the ocean environment requires understanding of a nonlinear dynamical system that responds according to complex interaction with a wide range of sea and wind conditions. Time domain observation of nonlinear marine dynamics with either experiments or high-fidelity numerical simulation tools is costly due to the random nature of the ocean and the full range of environmental and loading conditions that are experienced in the lifetime of a ship or platform. In this paper, a data-driven method is presented to predict the complex nonlinear input–output relationship typical of marine systems. A Long Short-Term Memory neural net is used to learn nonlinear wave propagation and the nonlinear roll of a ship section in beam seas. Training data are generated with second-order wave theory or a volume-of-fluid computational fluid dynamics, although the method is directly applicable to data that is generated by other means such as nonlinear potential flow or experimental measurements. The cost and the amount of data to apply the method are estimated and measured. The data-driven results are compared with unseen data to demonstrate the accuracy and feasibility. Highlights: Nonlinear heave and roll can be predicted using a recurrent neural net. Training data cost is small for high model accuracy for two nonlinear problems. Model training cost is negligible using GPU acceleration.