S. J. Hudak. Jr., M. P. Enright, Southwest Research Institute, San Antonio, TX
The fatigue crack growth life of military aircraft gas turbine engine components is strongly dependent on the magnitude and sequence of the applied stress values. The stress values associated with composite usages, typically modeled as deterministic variables, are useful for design purposes when little or no operational information is available. However, they are often inadequate for fleet management purposes, since mission usage and mission mixes can change over the life of the aircraft engine. Data from engine flight data recorders can be directly applied to predict the crack growth life history of a specific component. However, it is unclear how these data can be applied to the prediction of future crack growth, because values from previous flights do not address changes in future usage. In addition, the engine flight recording devices currently used in the field do not have the capability to identify the mission type, which is essential for prediction of current and future usage. In this presentation, a conceptual framework is presented for probabilistic treatment of aircraft engine usage that consists of the following three stages: (1) identification, (2) characterization, and (3) prediction. In the identification stage, a recently developed technique known as PMI (probabilistic mission identification) is used to predict the most likely mission type of each flight. In the characterization stage, probability densities associated with each mission type are quantified using an adaptive kernel approach. In the final stage (prediction), future usage is simulated based on values obtained from the mission probability densities, previous stress values, and future mission mix values from fleet management projections. An example is presented that illustrates the approach for a number of actual flight histories. The results can be applied to quantitative risk predictions of gas turbine engine components for enhanced life management, including potential life extension and associated cost savings.
Summary: A conceptual framework for probabilistic treatment of aircraft engine usage is presented that consists of the following three stages: (1) probabilistic mission identification (PMI), (2) stress probability density characterization using an adaptive kernel approach, and (3) future usage prediction. An example is presented that illustrates the approach for a number of actual flight histories.
