Estimation of Turbofan Engine Performance Model Accuracy and Confidence Bounds
Roth, Bryce Alexander
Mavris, Dimitri N.
Doel, David L.
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This paper explores the application of Inference and Bayesian Updating principles as a means to efficiently incorporate probabilistic data into the turbine engine status model matching process. This approach allows efficient estimation of nominal model match parameters from test data and also enables quantification of model accuracy and confidence bounds. The basic concepts are developed in detail and formulated into a status matching approach. This method is then applied to a simple surrogate matching problem using a cantilever beam matching exercise to illustrate the methods in a clear and easy-to-understand way. Typical results are presented and are directly analogous to status matching of a gas turbine engine cycle model.