Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

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

[+] Author and Article Information
Al Volponi

 Pratt & Whitney, 400 Main Street, East Hartford, CT 06108

Tom Brotherton, Rob Luppold

 Intelligent Automation, Inc., 13029 Danielson Street, Suite 200, Poway, CA 92064

J. Eng. Gas Turbines Power 130(2), 021604 (Feb 29, 2008) (10 pages) doi:10.1115/1.2799527 History: Received May 01, 2007; Revised June 04, 2007; Published February 29, 2008

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.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 6

GMM algorithm overview

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Figure 7

Example cruise data

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Figure 8

Example cruise data input parameters

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Figure 9

Example cruise data %Δ residual parameters

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Figure 10

GMM locations in flight trajectory

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Figure 11

Performance Δs using hybrid model trained with 38 GMMs

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Figure 12

Hybrid model effect on performance tracking

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Figure 13

Hardware system summary

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Figure 1

Typical on-board engine model architecture

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Figure 2

Example of module performance corruption due to engine model mismatch

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Figure 3

On-board hybrid engine model architecture

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Figure 4

Two-stage empirical modeling approach overview

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Figure 5

MLP training overview



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