Structural Reliability Assessment of Offshore Wind Turbine Jacket Considering Corrosion Degradation

Chao Ren, Y. Aoues, D. Lemosse, E. S. Cursi
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引用次数: 2

Abstract

. In this paper, an approach is proposed to conduct reliability analysis on an offshore jacket considering corrosion degradation under extreme load cases. Corrosion degradation is considered as thickness wastage of the jacket element, which is seen as time-dependent variables. One probabilistic corrosion in literature is adopted by using different distribution models. Also, three different inspection cases (environmental conditions) of the corrosion are studied. The reliability assessment is evaluated by Crude Monte Carlo simulation based on the trained surrogate model. Deep neural networks are used to train the surrogate model, because they are not limited by the distribution and dimension of variables. The results show that using different corrosion distribution model, the probabilities of failure of the jacket are different, even though they have the same mean and standard deviation values. In addition, with same assumption of the distribution model in corrosion, the reliability of the jacket changes a lot concerning different inspection cases. Furthermore, it is noted that the inspection cases have more influences on the reliability analysis of jacket than different corrosion distribution assumptions. At the end, two recommendations are derived from this work.
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考虑腐蚀退化的海上风力机导管套结构可靠性评估
. 本文提出了一种考虑极端载荷下腐蚀退化的海上导管架可靠性分析方法。腐蚀退化被认为是夹套元件的厚度损耗,这是一个随时间变化的变量。采用文献中的一种概率腐蚀,采用不同的分布模型。此外,还研究了三种不同的腐蚀检测情况(环境条件)。基于训练好的代理模型,采用粗糙蒙特卡罗仿真方法对可靠性进行评估。由于深度神经网络不受变量分布和维数的限制,因此可以使用深度神经网络来训练代理模型。结果表明:采用不同的腐蚀分布模型,即使具有相同的均值和标准差值,导管套失效概率也不同;此外,在腐蚀分布模型假设相同的情况下,不同检测工况下夹套的可靠性变化很大。同时指出,不同的腐蚀分布假设对导管套可靠性分析的影响更大。最后,本文提出了两点建议。
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