Fatigue life prediction of laminated composites using a multi-scale M-LaF and Bayesian inference

This paper presents a probabilistic model for fatigue life estimation of composite laminates using a high fidelity multi-scale approach called M-LaF (Micromechanics based approach for Fatigue Life Failure). To this end, square and hexagonal representative unit cells are introduced to calculate constituent stresses using a bridging matrix between macro and micro stresses referred to as the stress amplification factor matrix. The M-LaF is based on the constituent level input data that makes it possible to predict fatigue life of a variety of laminates with any possible fiber volume fraction. The M-LaF model parameters are calibrated as posterior distribution using the Bayesian inference methodology. A reference test data from literature was used for parameter calibration. The calculated posterior statistics were then used to calculate probabilistic fatigue life estimates of sample laminates. The predicted S-N curves are in good agreement with the test data for a range of composite laminas as well as laminates with different fiber volume fractions and under diverse stress ratios. As an illustration, the above approach was applied to a wind turbine blade to show the effect of multi-axial loading on the fatigue life of composite laminates. (C) 2016 Elsevier Ltd. All rights reserved.