An Effective Bayesian Method for Probability Fatigue Crack Propagation Modeling through Test Data. (3rd March 2019)

• Record Type: Journal Article
• Authors: Du, Xu; He, Yuting; Zhang, Tianyu; Gao, Chao; Zhang, Teng; Zhang, Sheng
• Other Names: Branco Ricardo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-03-03
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/7843627
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Fatigue crack growth test for 2A12-T4 aluminum alloy was conducted under constant amplitude loading, and the scatter of fatigue crack growth was analyzed by using experimental data based on mathematical statistics. A probabilistic modeling method was introduced to describe the crack growth behavior of 2A12-T4 aluminum alloy. The posterior distribution of model parameter is obtained based on diffuse prior distribution and fatigue crack test data, which is through Bayesian updating. Based on posterior samples of model parameter, the simulation steps and approach give us the crack length exceedance probability, the cumulative distribution function of loading cycle number, and scatter of crack length and loading cycle number, of which simulation results were used to verify the veracity and superiority of the proposed model versus the experimental results. In the present study, it can be used for the reliability assessment of aircraft cracked structures.