Methodology for Probabilistic Life Prediction of Multiple-Anomaly Materials

• Published in: AIAA journal 2006-04, Vol.44 (4), p.787-793
• Main Authors: Enright, Michael P, Huyse, Luc
• Format: Article
• Language: English
• Subjects: Aerospace materials; Applied sciences; Continuous cycle engines: steam and gas turbines, jet engines; Engines and turbines; Exact sciences and technology; Fracture mechanics; Fracture mechanics (crack, fatigue, damage...); Fundamental areas of phenomenology (including applications); Gas turbine engines; Materials fatigue; Mechanical engineering. Machine design; Physics; Solid mechanics; Structural and continuum mechanics; Test methods
• ISSN: 0001-1452; 1533-385X
• DOI: 10.2514/1.17142

Titanium gas-turbine engine components may contain anomalies that are not representative of nominal conditions. If undetected, they can lead to uncontained failure of the engine and associated loss of life. A probabilistic framework has been developed to predict the risk of fracture associated with titanium rotors and disks containing rare material anomalies. A recent Federal Aviation Administration Advisory Circular also provides guidance for the design of these components. However, some materials may exhibit relatively higher anomaly occurrence rates compared to those found in titanium alloys. In addition, the crack formation life for these materials may be nonnegligible and must be considered in the risk computation. When these materials are used, a single disk could contain a number of anomalies with unequal crack formation periods, and so the existing probabilistic framework is no longer valid. A methodology is presented for probabilistic life prediction of components with relatively large numbers of material anomalies. It is an extension of the probabilistic framework originally developed for titanium materials with hard alpha anomalies. The methodology is presented and illustrated for an aircraft gas-turbine engine disk. The results can be applied to fracture-mechanics-based probabilistic life prediction of alloys with large numbers of material anomalies.