Probabilistic methods for design assessment of reliability with inspection

Conventional gas turbine rotor life prediction methodologies are based on nominal conditions that do not adequately account for material and manufacturing anomalies that can degrade the structural integrity of high-energy rotors: To account for these anomalies, the Rotor Integrity Subcommittee of the Aerospace Industries Association recommended adoption of a probabilistic damage tolerance approach to supplement the current safe-life methodology: The recommendation led to the development of a computer program called DARWIN(TM) that computes the probability of fracture as a function of flight cycles, considering random defect occurrence and location, random inspection schedules, and several other random variables. The probabilistic fatigue analysis methodology developed for DARWIN to address hard alpha material anomalies is presented. The capability of this computer program is demonstrated using several realistic rotor models provided by aircraft engine manufacturers. It is shown that the life approximation function and importance sampling methods significantly reduce computation time (nearly two orders of magnitude) compared to the Monte Carlo method. In addition, an optimal zone sampling strategy is presented that can minimize the total number of samples required to achieve a desired sampling accuracy result for a given confidence interval. This probabilistic methodology can be used to focus design efforts on variables that have the most influence on risk reduction.