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    Empirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation

    Source: Journal of Engineering for Gas Turbines and Power:;2008:;volume( 130 ):;issue: 002::page 21604
    Author:
    Al Volponi
    ,
    Tom Brotherton
    ,
    Rob Luppold
    DOI: 10.1115/1.2799527
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A 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.
    keyword(s): Physics , Engines , Flight , Artificial neural networks , Gas turbines , Modeling , Hardware AND Flow (Dynamics) ,
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      Empirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation

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    https://yetl.yabesh.ir/yetl1/handle/yetl/137971
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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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