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contributor authorAl Volponi
contributor authorTom Brotherton
contributor authorRob Luppold
date accessioned2017-05-09T00:27:58Z
date available2017-05-09T00:27:58Z
date copyrightMarch, 2008
date issued2008
identifier issn1528-8919
identifier otherJETPEZ-27001#021604_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/137971
description abstractA practical consideration for implementing a real-time on-board engine component performance tracking system is the development of high fidelity engine models capable of providing a reference level from which performance changes can be trended. Real-time engine models made their advent as state variable models in the mid-1980s, which utilized a piecewise linear model that granted a reasonable representation of the engine during steady state operation and mild transients. Increased processor speeds over the next decade allowed more complex models to be considered, that were a combination of linear and nonlinear physics-based elements. While the latter provided greater fidelity over both transient operation and the engine operational flight envelope, these models could be further improved to provide the high level of accuracy required for long-term performance tracking, as well as address the issue of engine-to-engine variation. Over time, these models may deviate enough from the actual engine being monitored, as a result of improvements made during an engine’s life cycle such as hardware modifications, bleed and stator vane schedule alterations, cooling flow adjustments, and the like, that the module performance estimations are inaccurate and often misleading. The process described in this paper will address these shortcomings while maintaining the execution speed required for real-time implementation.
publisherThe American Society of Mechanical Engineers (ASME)
titleEmpirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation
typeJournal Paper
journal volume130
journal issue2
journal titleJournal of Engineering for Gas Turbines and Power
identifier doi10.1115/1.2799527
journal fristpage21604
identifier eissn0742-4795
keywordsPhysics
keywordsEngines
keywordsFlight
keywordsArtificial neural networks
keywordsGas turbines
keywordsModeling
keywordsHardware AND Flow (Dynamics)
treeJournal of Engineering for Gas Turbines and Power:;2008:;volume( 130 ):;issue: 002
contenttypeFulltext


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