针对海上风力机基础小概率失效事件的可靠性分析,提出一种基于PC-Kriging模型和主动学习的小失效概率的结构可靠性算法。该算法利用子集模拟法SS将小失效概率事件划分为若干中间事件,并通过主动学习提高代理模型对每个中间事件的拟合精度,以此来提高对中间事件的失效概率求解精度,并通过数学算例验证该方法的可行性与高效性。最后结合导管架式海上风力机基础的有限元分析,利用该算法开展导管架式海上风力机基础的结构强度可靠性分析,计算所得失效概率为7.6416×10-8,符合规范要求,并进行全局灵敏度分析,确定桩腿壁厚为影响风力机基础可靠性的主要因素。
Abstract
A structural reliability algorithm for small failure probability based on PC-Kriging model and active learning is proposed for reliability analysis of small probability failure events of offshore wind turbine foundation. The algorithm utilizes Subset Simulation （SS） to divide the small failure probability events into several intermediate events, and improves the fitting accuracy of the agent model for each intermediate event through active learning, so as to improve the accuracy of the failure probability solution for the intermediate events, and verifies the feasibility and high efficiency of the method through mathematical examples. Finally, the algorithm is used to carry out the structural strength reliability analysis of the conduit frame offshore wind turbine foundation in combination with the finite element analysis of the conduit frame offshore wind turbine foundation, and the calculated probability of failure is 7.6416×10-8, which is in line with the specification requirements, and a global sensitivity analysis is carried out, which determines that the wall thickness of the pile leg is the main factor influencing the reliability of the wind turbine foundation.
关键词
可靠性分析 /
Kriging /
主动学习 /
导管架基础 /
小失效概率
Key words
reliability analysis /
Kriging /
active learning /
jacket type offshore wind turbine foundation /
small failure probabilit
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基金
国家重点研发计划（2022YFC2806300）; 国家自然科学基金（51109158）; 中海油能源发展股份有限公司科技重大专项课题（HFZDZX-JN2021-01-04）
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