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• W4366439434 endingPage "107685" @default.
• W4366439434 startingPage "107685" @default.
• W4366439434 abstract "The success of wind energy, superior to the traditional energy source in sustainability and affordability, is dependent on the reliability of wind turbines. However, to assess the fatigue reliability of wind turbines for long-term operation, massive computational costs are required due to the dynamic response simulations under different environmental scenarios. To reduce the computational cost and to increase the feasibility of long-term fatigue evaluation, a novel probabilistic modeling framework is proposed for long-term fatigue reliability analysis of wind turbines. The proposed framework considers each short-term numerical simulation as a single fatigue event. Thereafter, the long-term sequence of fatigue loading events is determined by the emissions from Markov model which is constrained by environmental conditions. Finally, the statistical information for the sequence of fatigue loading events is obtained so that the counting cycles at each fatigue loading level can be considered a random variable. Therefore, fatigue reliability can be evaluated based on the subset simulation incorporated with the uncertainty of SN curve. The results show that the failure probability can significantly increase as the service lifetime increases without inspections and maintenance. The proposed model, innovatively leveraging the Markov model and subset simulation, is an effective and promising tool for long-term fatigue reliability analysis of wind turbines, as well as any other structures." @default.
• W4366439434 created "2023-04-21" @default.
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• W4366439434 date "2023-08-01" @default.
• W4366439434 modified "2023-09-27" @default.
• W4366439434 title "Probabilistic modeling for long-term fatigue reliability of wind turbines based on Markov model and subset simulation" @default.
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• W4366439434 doi "https://doi.org/10.1016/j.ijfatigue.2023.107685" @default.
• W4366439434 hasPublicationYear "2023" @default.
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