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    Weakly Nonlinear Surface Wave Prediction Using a Data-Driven Method With the Help of Physical Understanding

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2023:;volume( 146 ):;issue: 004::page 41201-1
    Author:
    Chen, Jialun
    ,
    Milne, Ian A.
    ,
    Gunawan, David
    ,
    Taylor, Paul H.
    ,
    Zhao, Wenhua
    DOI: 10.1115/1.4064109
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Accurate surface wave prediction can potentially improve the safety and efficiency of various offshore operations, such as heavy lifts and active control of wave energy converters and floating wind turbines. Prediction of surface waves, even if only for a few periods in advance, is of value for decision-making. This study aims to predict weakly nonlinear surface waves (up to the second-order) in real-time using a data-driven model based on artificial neural networks (ANNs), where the application of physics is investigated to aid the development of a data-driven model. Based on numerically synthesized nonlinear wave records calculated using exact second-order theory, ANN models were trained to separate the nonlinear bound components at an up-wave location, propagate the linear waves, and reintroduce the nonlinear components as a correction to the prediction at a down-wave location. Our findings indicate that the optimal approach is to predict each stage separately following the basic physical structure of weakly nonlinear water waves using a series of ANN rather than direct prediction in a single step using ANN. Furthermore, we examined the generalization of the models across different sea states and investigated the impact of the second-order bound waves on prediction accuracy.
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      Weakly Nonlinear Surface Wave Prediction Using a Data-Driven Method With the Help of Physical Understanding

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303625
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    • Journal of Offshore Mechanics and Arctic Engineering

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    contributor authorChen, Jialun
    contributor authorMilne, Ian A.
    contributor authorGunawan, David
    contributor authorTaylor, Paul H.
    contributor authorZhao, Wenhua
    date accessioned2024-12-24T19:16:14Z
    date available2024-12-24T19:16:14Z
    date copyright12/11/2023 12:00:00 AM
    date issued2023
    identifier issn0892-7219
    identifier otheromae_146_4_041201.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303625
    description abstractAccurate surface wave prediction can potentially improve the safety and efficiency of various offshore operations, such as heavy lifts and active control of wave energy converters and floating wind turbines. Prediction of surface waves, even if only for a few periods in advance, is of value for decision-making. This study aims to predict weakly nonlinear surface waves (up to the second-order) in real-time using a data-driven model based on artificial neural networks (ANNs), where the application of physics is investigated to aid the development of a data-driven model. Based on numerically synthesized nonlinear wave records calculated using exact second-order theory, ANN models were trained to separate the nonlinear bound components at an up-wave location, propagate the linear waves, and reintroduce the nonlinear components as a correction to the prediction at a down-wave location. Our findings indicate that the optimal approach is to predict each stage separately following the basic physical structure of weakly nonlinear water waves using a series of ANN rather than direct prediction in a single step using ANN. Furthermore, we examined the generalization of the models across different sea states and investigated the impact of the second-order bound waves on prediction accuracy.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleWeakly Nonlinear Surface Wave Prediction Using a Data-Driven Method With the Help of Physical Understanding
    typeJournal Paper
    journal volume146
    journal issue4
    journal titleJournal of Offshore Mechanics and Arctic Engineering
    identifier doi10.1115/1.4064109
    journal fristpage41201-1
    journal lastpage41201-8
    page8
    treeJournal of Offshore Mechanics and Arctic Engineering:;2023:;volume( 146 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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