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Research Papers: Gas Turbines: Cycle Innovations

Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

[+] Author and Article Information
Y. G. Li, M. F. Abdul Ghafir, L. Wang, R. Singh

School of Engineering, Cranfield University, Cranfield, Bedford MK43 0AL, UK

K. Huang, X. Feng

China Aviation Powerplant Research Institute, Aviation Industry Corporation of China, Zhuzhou, Hunan Province, P.C. 412002, P. R. China

J. Eng. Gas Turbines Power 133(7), 071701 (Mar 10, 2011) (9 pages) doi:10.1115/1.4002620 History: Received April 13, 2010; Revised April 19, 2010; Published March 10, 2011; Online March 10, 2011

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Gas turbine off-design performance

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Figure 2

Off-design scaling of a compressor map

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Figure 3

Off-design scaling of a turbine map

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Figure 4

Off-design scaling of a burner map

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Figure 17

Modified burner map after adaptation is performed

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Figure 18

Scaling factors for burner efficiency

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Figure 19

Modified compressor turbine maps after adaptation is performed

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Figure 20

Scaling factors for compressor turbine characteristic parameters

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Figure 15

Modified compressor maps after adaptation is performed

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Figure 14

The change in fitness values during GA adaptation process

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Figure 13

Average errors at different OD points before and after adaptations

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Figure 12

Prediction errors for T3 before and after adaptations

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Figure 11

Prediction errors for P15 before and after adaptations

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Figure 10

Prediction errors for SP before and after adaptations

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Figure 9

Prediction errors for FF before and after adaptations

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Figure 8

Prediction errors for P3 before and after adaptations

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Figure 7

Engine model configuration

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Figure 6

A flow chart of the performance adaptation process

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Figure 5

Illustration of variations of scaling factor lines

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Figure 16

Scaling factors for compressor characteristic parameters

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