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首页> 外文期刊>Marine Structures >Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data
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Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data

机译:考虑应变测量的风力涡轮机支撑结构中焊接接头剩余疲劳寿命的预测及海洋数据的关节分布

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摘要

Reassessing the remaining fatigue life of the wind turbine support structures becomes more and more crucial for operation, maintenance, and life extension when they are reaching the end of their design service life. By using measured oceanographic and strain data, each year, remaining fatigue life can be updated to adapt the operation to real loading conditions. Previous works have not put attention to address the complexity of offshore loading combinations and as-constructed state of the structure in estimating structural responses for fatigue behaviour to stochastically predict the remaining fatigue life. The present paper links the oceanographic data to fatigue damage by using measured strain, and uses the Bayesian approach to update the joint distribution of the oceanographic data. Consequently, the failure probability of the support structure can be updated and so the predicted fatigue life. The year-to-year variation of the 10-min mean wind speed, the unrepresentativeness of measured strain, the measurement uncertainty, and corrosion are considered together with uncertainties in Miner's rule and S-N curves. The present research shows that the real oceanographic data can be used to adjust the predicted remaining fatigue life and eventually give decision support for the wind turbine operation.
机译:重新评估风力涡轮机支撑结构的剩余疲劳寿命对操作,维护和寿命更为至关重要,当他们达到设计使用寿命的结束时。通过使用测量的海洋和应变数据,每年可以更新剩余的疲劳寿命以使操作适应实际装载条件。以前的作品没有注意解决近海装载组合的复杂性和结构的结构,估计疲劳行为的结构反应,使得随机预测剩余的疲劳寿命。本文通过使用测量的应变将海洋数据链接到疲劳损坏,并使用贝叶斯方法更新海洋学数据的联合分布。因此,可以更新支撑结构的故障概率,因此预测的疲劳寿命。 10分钟的均值的逐年变化,测量应变,测量不确定度和腐蚀的不适用性,与矿工规则和S-N曲线的不确定性一起考虑。本研究表明,实际海洋数据可用于调整预测的剩余疲劳寿命,最终提供对风力涡轮机操作的决策支持。

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