Uncertainty propagation in fatigue crack growth analysis using dimension reduction technique

Abstract

This paper proposes a general probabilistic methodology for uncertainty propagation in fatigue crack growth analysis under both constant amplitude and variable amplitude loadings. A recently developed small time scale model is used to predict the deterministic fatigue crack growth curve (a-N curve). The dimension reduction technique is used for uncertainty propagation in fatigue crack growth analysis. The basic idea is to avoid direct simulations and focuses on the statistical moment behaviour of output random variables. A modified Chebyshev algorithm is presented to improve the approximation accuracy when calculating the integral points and associated weights for an arbitrary probabilistic distribution. Uncertainties of some material properties are considered as input random variables and propagated through the mechanical model. Prediction result of the proposed methodology is compared with the direct Monte Carlo Simulation (MCS) and is verified using experimental data of aluminium alloys under constant amplitude loading and block loading.