A fully probabilistic high-cycle fatigue (HCF) risk assessment methodology for application to turbine engine blades is described. The assessment uses the Bayesian paradigm of probability theory in which probability distributions are used to encode states of knowledge. Multi-level (or hierarchical) models are employed to capture engineering knowledge of the factors important for assessing HCF risk. This structure allows us to use standard probability distributions to adequately represent uncertainties in model parameters. The model accounts for engine-to-engine, run-to-run, and blade-to-blade variability as well as uncertainty in material capability, usage (flight conditions, time at resonance), and steady and vibratory stresses. Markov chain Monte Carlo (MCMC) simulation is used to fit observed data to the engineering models, then direct Monte Carlo simulation is used to assess the HCF risk.

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