The objective of this work was to develop and demonstrate a probabilistic life prediction method for the prediction of minimum fatigue lives that are typically used in the design of fracture critical rotating turbine engine components. A Monte Carlo analysis was used to predict the variability in fatigue lives based on the distribution of microstructural features that lead to early crack initiation as well as the variability in small fatigue crack growth rates. Two titanium alloys, both with bimodal microstructures, were tested and analysed in this study. The distribution of critical microstructural features was calibrated based on test results and understanding of microstructure neighbourhood effects. Testing was conducted on both alloys and included both smooth and notched specimens. The predictions are presented and compared with the data for smooth and notch geometries for the various loading conditions. A parametric study was performed to identify the importance of several model inputs and to identify areas for future improvement.

Failure of critical engine components such as compressor, fan, and turbine disks during flight can cause the loss of the engine, aircraft, or even life. To reduce the risk of this failure during flight, different methodologies and tools have been developed to determine the safe operating life of these critical disk components. The two most widely used lifing methods, safe-life and damage tolerance, are inherently conservative, retiring all components when a predetermined operating limit is reached. Both methods retire components with theoretical useful life remaining. Additional lifing methods can be used to reduce this conservatism and extend the life of these components. Retirement for cause, developed within the United States Air Force is a lifing method that can extend the life of components by retiring a component only when there is cause to do so. Military and industry standards on lifing methodologies were reviewed. Both deterministic and probabilistic approaches to disk lifing methods are discussed as well as current tools. This paper provides a comparison of the methodologies and tools currently being used today by both the government and industry.

Turbine engine disk life prediction and understanding the associated risk remains a significant challenge for today’s designer. Despite advances made in materials testing and characterization, as well as, the application of damage tolerance and linear elastic fracture mechanics modeling, there remains a void in properly assessing loading, geometry, and material design property variability. Add to this the application of advanced hybrid and composite material systems and the need to accurately deal with material variability is even greater. There still remain incidents of failure of critical components which were not properly accounted for by the existing analytical methods, testing, and inspections employed today. Application of probabilistic methods offers an effective and useful approach to modeling this variability while also providing a means by which to assess random variable sensitivity and risk assessment. Current research, as well as, applicable industry and government regulatory guidelines and publications were examined and will be presented. An assessment of the most effective tools, modeling methods, and predictive risk of failure assessments together with recommendations for future work will be discussed. The potential for probabilistic methods to provide a cost-effective way to manage fleet engine and component usage is presented, as well as, its ability to enhance the safe implementation of Retirement for Cause concepts to fleet management.