Probabilistic analyses of component durability have been developed for first stage buckets and nozzles in General Electric MS7001B and MS7001E industrial gas turbine engines. The analyses illustrate two different approaches to the development of probabilistic durability algorithms. The bucket algorithm is built around an existing thermal-mechanical fatigue life model which predicts average fatigue life as a function of local strains, dwell times, and constants in the fatigue model derived from laboratory testing. The fatigue model is linked to a fast probability integration method which calculates the uncertainty or variability in the response variable (life or damage) resulting from uncertainties or variabilities in the physical input variables. The nozzle cracking algorithm, in contrast, is built around a large data base of crack length sums for each vane on seven different nozzles from the field. The crack length sums on nozzles with similar numbers of fired starts were described with standard normal distributions. Extreme value distributions were then calculated analytically to describe the crack length sums on the single most heavily cracked vane per nozzle for different probability levels.

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