Open Access Peer-reviewed Research Article

Application of probabilistic methods to turbine engine disk life prediction and risk assessment

Main Article Content

Paul Copp
Ashley Whitney-Rawls
Jace Carter
Tarun Goswami corresponding author


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.

damage tolerant life, life extension, disk components, probability of failure, non-destructive testing, risk assessment

Article Details

How to Cite
Copp, P., Whitney-Rawls, A., Carter, J., & Goswami, T. (2022). Application of probabilistic methods to turbine engine disk life prediction and risk assessment. Research on Intelligent Manufacturing and Assembly, 1(1), 3-19.


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