A new mean stress corrected strain energy model is proposed for fatigue life prediction of metals. Specifically, a mean stress sensitivity parameter is incorporated into modify the dissipated strain energy by introducing two mean stress correction factors. The prediction accuracy of the proposed model is compared with those of Walker, Smith-Watson-Topper, Morrow, and generalized damage parameter models by using 13 experimental data sets. All data points for each material are, respectively, fitted into a single mean stress corrected strain energy-life curve. More accurate predictions are achieved by the proposed model for all data sets with lower model prediction errors than others.

In the present work, a probabilistic framework for fatigue life prediction and reliability assessment of an engine high pressure turbine disc is proposed to incorporate the effects of load variations and mean stress, which provides a reference for engine structural design under a given target failure probability. Within this framework, a new probabilistic fatigue damage accumulation model under random loadings is elaborated based on a ductility exhaustion model, and probabilistic [Formula: see text] curves for the high pressure turbine disc under different flight missions are derived based on experimental data of turbine disc alloy GH4169. The influence of random load variations on fatigue reliability of the high pressure turbine disc has been investigated and quantified by combining the engine load spectrum with finite element analysis.

Ratcheting occurs easily because of the presence of mean stress during the stress‐control fatigue of engineering components. For ductility exhaustion dominated fatigue failure, a new fatigue life prediction model is developed by introducing the mean ratcheting strain rate to incorporate the effects of ratcheting and mean stress on fatigue life. The prediction accuracy of the proposed model was compared with that of the generalised damage parameter, Xia–Kujawski–Ellyin, Walker and Goswami models. Specifically, the model predictions and tested lives were compared using nine sets of experimental data from the literature. In the statistical analysis of these five models, the proposed model provides the highest accuracy and robust life predictions with the lowest model prediction errors.

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