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    Long-Term Extreme Response and Reliability of a Vessel Rolling in Random Beam Seas

    Source: Journal of Offshore Mechanics and Arctic Engineering:;2018:;volume( 140 ):;issue: 001::page 11601
    Author:
    Chai, Wei
    ,
    Naess, Arvid
    ,
    Leira, Bernt J.
    DOI: 10.1115/1.4037789
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this paper, the long-term extreme response of a vessel rolling in random beam seas and the associated reliability evaluation are addressed. The long-term response analysis is based on the upcrossing rates of the roll motion under different sea states. Generally, for nonlinear roll motion in random seas, the high-level roll response is sensitive and closely related to the nonlinear effects associated with the restoring and damping terms. Therefore, assessing the corresponding statistics of the random roll motion with low probability levels is difficult and time-consuming. In this work, the Markov theory is introduced in order to tackle this problem. Specifically, for the dead ship condition, the random roll excitation moment is approximated as a filtered white noise process by applying a second-order linear filter and an efficient four-dimensional (4D) path integration (PI) technique is applied in order to calculate the response statistics. Furthermore, the reliability evaluation is based on the well-known Poisson estimate as well as on the upcrossing rate calculated by the 4D PI method. The long-term analysis and reliability evaluation of the nonlinear roll motion in random seas, which consider the variation of the sea states could be a valuable reference for ship stability research.
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      Long-Term Extreme Response and Reliability of a Vessel Rolling in Random Beam Seas

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4252653
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    • Journal of Offshore Mechanics and Arctic Engineering

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    contributor authorChai, Wei
    contributor authorNaess, Arvid
    contributor authorLeira, Bernt J.
    date accessioned2019-02-28T11:05:55Z
    date available2019-02-28T11:05:55Z
    date copyright9/14/2017 12:00:00 AM
    date issued2018
    identifier issn0892-7219
    identifier otheromae_140_01_011601.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4252653
    description abstractIn this paper, the long-term extreme response of a vessel rolling in random beam seas and the associated reliability evaluation are addressed. The long-term response analysis is based on the upcrossing rates of the roll motion under different sea states. Generally, for nonlinear roll motion in random seas, the high-level roll response is sensitive and closely related to the nonlinear effects associated with the restoring and damping terms. Therefore, assessing the corresponding statistics of the random roll motion with low probability levels is difficult and time-consuming. In this work, the Markov theory is introduced in order to tackle this problem. Specifically, for the dead ship condition, the random roll excitation moment is approximated as a filtered white noise process by applying a second-order linear filter and an efficient four-dimensional (4D) path integration (PI) technique is applied in order to calculate the response statistics. Furthermore, the reliability evaluation is based on the well-known Poisson estimate as well as on the upcrossing rate calculated by the 4D PI method. The long-term analysis and reliability evaluation of the nonlinear roll motion in random seas, which consider the variation of the sea states could be a valuable reference for ship stability research.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleLong-Term Extreme Response and Reliability of a Vessel Rolling in Random Beam Seas
    typeJournal Paper
    journal volume140
    journal issue1
    journal titleJournal of Offshore Mechanics and Arctic Engineering
    identifier doi10.1115/1.4037789
    journal fristpage11601
    journal lastpage011601-9
    treeJournal of Offshore Mechanics and Arctic Engineering:;2018:;volume( 140 ):;issue: 001
    contenttypeFulltext
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