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MCMC-Based Fatigue Crack Growth Prediction on 2024-T6 Aluminum Alloy

机译:基于MCMC的2024-T6铝合金疲劳裂纹扩展预测

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摘要

This work aims to make the crack growth prediction on 2024-T6 aluminum alloy by using Markov chain Monte Carlo ( MCMC). The fatigue crack growth test is conducted on the 2024-T62 aluminum alloy standard specimens, and the scatter of fatigue crack growth behavior was analyzed by using experimental data based on mathematical statistics. An empirical analytical solution of Paris' crack growth model was introduced to describe the crack growth behavior of 2024-T62 aluminum alloy. The crack growth test results were set as prior information, and prior distributions of model parameters were obtained by MCMC using OpenBUGS package. In the additional crack growth test, the first test point data was regarded as experimental data and the posterior distribution of model parameters was obtained based on prior distributions combined with experimental data by using the Bayesian updating. At last, the veracity and superiority of the proposed method were verified by additional crack growth test.
机译:这项工作旨在通过使用马尔可夫链蒙特卡罗(MCMC)来预测2024-T6铝合金的裂纹扩展。在2024-T62铝合金标准试样上进行了疲劳裂纹扩展测试,并使用基于数学统计的实验数据分析了疲劳裂纹扩展行为的散布。介绍了巴黎裂纹扩展模型的经验解析解,以描述2024-T62铝合金的裂纹扩展行为。将裂纹扩展测试结果设置为先验信息,并通过MCMC使用OpenBUGS软件包获得模型参数的先验分布。在额外的裂纹扩展测试中,将第一个测试点数据视为实验数据,并使用贝叶斯更新将先验分布与实验数据相结合,获得模型参数的后验分布。最后,通过额外的裂纹扩展测试验证了该方法的准确性和优越性。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第9期|9409101.1-9409101.12|共12页
  • 作者单位

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

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