Abstract
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.
Highlights
In aircraft structural engineering domain, the concept of damage tolerance was introduced to design key components
The paper presents a method of fatigue crack propagation by using the crack test information and in-service inspection information
The fatigue crack growth curve of 2024-T62 aluminum alloy is predicted by Bayesian theory with Markov chain Monte Carlo (MCMC) algorithm and OpenBUGS package
Summary
In aircraft structural engineering domain, the concept of damage tolerance was introduced to design key components. The prediction of fatigue crack propagation is a main work for aircraft structure service/use life management. Accurate prediction of fatigue crack growth determined the safety of service and usage. Due to the scatter of material, the fatigue crack growth behavior of some components still creates scatter, though the components made by the same material and served under the same loading and environmental conditions. The residual crack propagation life of the aircraft components is assessed based on the in-service observation and the mathematical model which describes the crack growth behavior of these components. There are uncertainties in the material performance, components dimensions, the measurements, and the degradation model [1]. Some stochastic fatigue crack growth models have been proposed [5, 6]
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