Estimation of Turbofan Engine Performance Model Accuracy and Confidence Bounds

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ISABE2003-1208.pdf (359.42 KB)
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Roth, Bryce Alexander
Doel, David L.
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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http://hdl.handle.net/1853/6321
Abstract
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.
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2003-09
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