A method for developing transient, predictive models of gas turbine engine performance using system identification techniques in conjunction with test cell data has been successfully demonstrated. Test cell data were obtained for both transient and steady-state operation from two F402-RR-406A USMC AV-8B engines at the Naval Aviation Depot (NADEP), Cherry Point, North Carolina.
One engine was run to gather a single set of steady-state data consisting of 24 subsets of five seconds of data. The other engine was run to obtain two sets of transient data, recorded at three different rates of engine acceleration for each set. The acceleration rates of 3, 25, and 100 degrees of throttle per second were preset in the test cell controls. These rates correspond to the angular velocity of the fuel throttle during an acceleration. Each of these six transient test runs consisted of 25 seconds of data. Data were captured at a rate of five Hertz over the engine operating range from idle (26% Low Pressure spool speed) to full military power (105% LP spool speed) for all cases.
A BASIC code developed at the NADEP required significant modification before it could be used to reduce the data. The modified code generated engine operating points consisting of mass flow rate, total pressure ratio, spool speed, and rate of acceleration for the inner fan, outer fan, and high pressure compressor. Finally, a multivariate regression technique using the SAS language was developed in cooperation with the Virginia Tech Statistical Consulting Center. This technique was used to generate a closed-form model of each component capable of predicting operating points at spool speeds and acceleration rates intermediate to those measured. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/44491 |
| Date | 29 August 2008 |
| Creators | Grose, Michael David |
| Contributors | Mechanical Engineering, O'Brien, Walter F. Jr., Mitchell, Larry D., Ng, Fai |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | xii, 156 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 39849282, LD5655.V855_1996.G763.pdf |
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