Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation
Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation
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DOI：
10.1016/j.ress.2017.03.006
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Saikumar R. Yeratapally;M. Glavicic;C. Argyrakis;M. Sangid
中科院分区：
文献类型：
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作者：
Saikumar R. Yeratapally;M. Glavicic;C. Argyrakis;M. Sangid
利用不确定性量化和扩展框架，建立了基于微观组织和变形机制的疲劳裂纹萌生和寿命预测模型，该模型将多晶材料的微观组织变异性与疲劳寿命的离散性联系起来。首先，利用全局灵敏度分析(GSA)确定疲劳寿命预测模型中影响最大的一组参数。然后，利用基于马尔可夫链蒙特卡罗(MCMC)算法的贝叶斯推理框架，计算出所有影响参数的后验分布。利用蒙特卡罗抽样技术将量化的不确定性传递到模型中，从而对疲劳寿命做出稳健的预测。通过将预测结果与实验疲劳寿命数据进行比较，验证了模型的有效性。
A microstructure and deformation mechanism based fatigue crack initiation and life prediction model, which links microstructure variability of a polycrystalline material to the scatter in fatigue life, is validated using an uncertainty quantification and propagation framework. First, global sensitivity analysis (GSA) is used to identify the set of most influential parameters in the fatigue life prediction model. Following GSA, the posterior distributions of all influential parameters are calculated using a Bayesian inference framework, which is built based on a Markov chain Monte Carlo (MCMC) algorithm. The quantified uncertainties thus obtained, are propagated through the model using Monte Carlo sampling technique to make robust predictions of fatigue life. The model is validated by comparing the predictions to experimental fatigue life data.