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Machine learning simulation of one-dimensional deterministic water wave propagation

Wedler, Mathies and Stender, Merten and Klein, Marco and Hoffmann, Norbert (2023) Machine learning simulation of one-dimensional deterministic water wave propagation. Ocean Engineering (284), p. 115222. Elsevier. doi: 10.1016/j.oceaneng.2023.115222. ISSN 0029-8018.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0029801823016062


Deterministic phase-resolved prediction of the evolution of surface gravity waves in water is challenging due to their complex spatio-temporal dynamics. Physics-based methods of varying complexity are available, but the conflicting objectives of numerical efficiency and accuracy impede real-time wave prediction. Data-driven methods may be able to overcome this challenge by using training data generated by complex numerical methods. This work explores the potential of a machine learning (ML) approach based on a fully convolutional encoder–decoder architecture for the efficient and accurate prediction of water waves. The high-order spectral (HOS) method forms the foundation for the generation of the training data. The HOS method is applied for different, consecutive orders of nonlinearity starting from first order up to fourth order. The JONSWAP wave energy spectrum serves as the basis for modeling the one-dimensional irregular sea states. The overall objective of this work is to evaluate whether the complex non-linear physical processes can be identified and learned by the ML approach. The trained ML flow mapper is used to perform time integration of an initial sea state. The results indicate that the proposed ML approach is able to reproduce the distinctive physical processes of the different orders of nonlinearities. It is shown that the ML approach enables fast and accurate predictions of one-dimensional waves over a time horizon that spans multiple peak periods.

Item URL in elib:https://elib.dlr.de/195911/
Document Type:Article
Title:Machine learning simulation of one-dimensional deterministic water wave propagation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Klein, MarcoUNSPECIFIEDhttps://orcid.org/0000-0003-2867-7534138774859
Date:6 June 2023
Journal or Publication Title:Ocean Engineering
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:p. 115222
Keywords:Deterministic phase-resolved wave prediction; Machine learning; Surrogate modeling; Auto-regressive time stepping; Nonlinear wave dynamics
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Energy System Transformation
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Systems Analysis and Technology Assessment
Location: Geesthacht
Institutes and Institutions:Institute of Maritime Energy Systems
Deposited By: Klein, Marco
Deposited On:17 Jul 2023 07:26
Last Modified:17 Jul 2023 07:26

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