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受内压航空管道弯曲疲劳可靠性

Bending Fatigue Reliability of Internal Pressured Pipeline
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摘要 引入结合Kriging代理模型法和Monte Carlo抽样法的主动学习可靠性分析方法(AK-MCS),以管道尺寸、作用载荷以及管材属性为基本随机变量,对受内压悬臂管道在给定工况(即28 MPa内压,自由端5.06 mm纵向位移循环载荷)下的疲劳寿命进行可靠性分析。首先根据管道三点弯曲疲劳试验结果,通过线性回归分析建立管道的Manson-Coffin疲劳寿命模型,而后采用AK-MCS方法获得管道的疲劳寿命-失效概率/可靠度曲线,为内压管道的工程应用提供了基于概率的寿命预测结果。根据该曲线进而得到管道的疲劳寿命频数直方图,发现弯曲疲劳寿命总体上呈正态分布。此外,通过与Monte Carlo方法的比较,证实了AK-MCS方法在保证受内压航空管道弯曲疲劳可靠性分析精度的同时,还大大降低了计算量。 The active learning reliability method in combination with Kriging and Monte Carlo simulation(AK-MCS) is applied to the reliability analysis of the fatigue life of the cantilever internal pressured pipeline under the given working condition(i.e., 28 MPa internal pressure, 5.06 mm longitudinal displacement cyclic load at free end) by considering the randomness of the design sizes, working load, and material properties. First,the Manson coffin fatigue life model of the pipeline is established by linear regression analysis, according to the results of three-point bending test. Then, the fatigue life failure probability/reliability curve of the hydraulic pipeline is obtained by using AK-MCS, which provides the life prediction value based on the probability for the engineering application of the pressured pipeline.Finally, the fatigue life frequency histogram is obtained by the curve. It is found that the bending fatigue life of the pipeline is generally normal distributed. Moreover, by solving the small probability event problem, it is shown that compared with MCS, AK-MCS reduces the time of the FEM(finite element method) analysis and ensures the accuracy of reliability analysis.
作者 沈兴铿 王光强 杨婧 员婉莹 杨宏伟 张屹尚 戴瑛 贺鹏飞 SHEN Xingkeng;WANG Guangqiang;YANG Jing;YUAN Wanying;YANG Hongwei;ZHANG Yishang;DAI Ying;HE Pengfei(School of Aerospace Engineering and Applied Mechanics,Tongji University,Shanghai 200092,China;Research and Development Center,AECC Commercial Aircraft Engine Co.,Ltd.,Shanghai 200241,China;School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China)
出处 《同济大学学报(自然科学版)》 EI CAS CSCD 北大核心 2023年第1期108-116,共9页 Journal of Tongji University:Natural Science
关键词 内压管道 疲劳可靠性 Kriging代理模型法 Monte Carlo抽样法 主动学习方法 internal pressured pipeline fatigue reliability Kriging surrogate model Monte Carlo simulation active learning method
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