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.
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July 2011
Research Papers
Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm
Y. G. Li,
Y. G. Li
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
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M. F. Abdul Ghafir,
M. F. Abdul Ghafir
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
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L. Wang,
L. Wang
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
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R. Singh,
R. Singh
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
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K. Huang,
K. Huang
China Aviation Powerplant Research Institute,
Aviation Industry Corporation of China
, Zhuzhou, Hunan Province, P.C. 412002, P. R. China
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X. Feng
X. Feng
China Aviation Powerplant Research Institute,
Aviation Industry Corporation of China
, Zhuzhou, Hunan Province, P.C. 412002, P. R. China
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Y. G. Li
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
M. F. Abdul Ghafir
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
L. Wang
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
R. Singh
School of Engineering,
Cranfield University
, Cranfield, Bedford MK43 0AL, UK
K. Huang
China Aviation Powerplant Research Institute,
Aviation Industry Corporation of China
, Zhuzhou, Hunan Province, P.C. 412002, P. R. China
X. Feng
China Aviation Powerplant Research Institute,
Aviation Industry Corporation of China
, Zhuzhou, Hunan Province, P.C. 412002, P. R. ChinaJ. Eng. Gas Turbines Power. Jul 2011, 133(7): 071701 (9 pages)
Published Online: March 10, 2011
Article history
Received:
April 13, 2010
Revised:
April 19, 2010
Online:
March 10, 2011
Published:
March 10, 2011
Citation
Li, Y. G., Ghafir, M. F. A., Wang, L., Singh, R., Huang, K., and Feng, X. (March 10, 2011). "Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm." ASME. J. Eng. Gas Turbines Power. July 2011; 133(7): 071701. https://doi.org/10.1115/1.4002620
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