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Assessment of Aircraft Engine Components

Assessment of Aircraft Engine Components Manual The probability of fracture of an aircraft engine component is highly dependent on the geometry and stress values at the location of anomalies that may occur during the manufacturing process. The efficiency and accuracy of the method used to construct the spatial conditional failure joint probability density function (JPDF) associated with fracture is dependent on the number of limit state evaluations at each location and on the number of locations used to describe it. Previous studies have focused on modeling this JPDF and associated risk contours as a multivariate histogram associated with regions within the domain that are referred to as “zones”. To ensure conservative estimates, the uniform probability density for each zone is set to the maximum possible value within the associated region. The primary disadvantage to this approach is that it requires human judgment for definition of zones and for identification of the maximum conditional failure probability within each zone. In this paper, an approximate approach is presented for estimating risk contours that is based on a series of crack growth life contour values associated with a range of initial anomaly sizes that are placed at the nodes of a finite element model. At each node, the PDF of initial anomaly size is applied to the deterministic life values associated with initial anomaly sizes to obtain the conditional crack growth life PDF. The conditional risk at each node is obtained by computing the area under the crack growth life PDF associated with the design life of the component. The methodology was illustrated for an aircraft gas turbine engine compressor disk, where the results were in close agreement with established reference solutions when fracture risk is based only on the variability in the initial anomaly size. The method provides a relatively fast approximate estimate of component risk for conceptual design that can also be used as a framework for traditional probabilistic computational methods.
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