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Research Papers: Gas Turbines: Aircraft Engine

Turbojet Engine Performance Tuning With a New Map Adaptation Concept

[+] Author and Article Information
Gianluigi Alberto Misté

University of Padova,
Department of Industrial Engineering,
Via Venezia 1,
Padova 35131, Italy
e-mail: gianluigi.miste@dii.unipd.it

Ernesto Benini

University of Padova,
Department of Industrial Engineering,
Via Venezia 1,
Padova 35131, Italy
e-mail: ernesto.benini@unipd.it

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 10, 2014; final manuscript received January 14, 2014; published online February 18, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(7), 071202 (Feb 18, 2014) (8 pages) Paper No: GTP-14-1016; doi: 10.1115/1.4026548 History: Received January 10, 2014; Revised January 14, 2014

Gas turbine off design performance prediction is strictly dependent on the accuracy of compressor and turbine map characteristics. Experimental data regarding component maps are very difficult to find in literature, since it is undisclosed proprietary information of the engine manufacturers. To overcome this limitation, gas turbine engineers use available generic component maps and modify them to reach the maximum adherence with the experimental measures. Different scaling and adaptation techniques have been employed to this aim; these methodologies are usually based upon analytic regression models which minimize the deviation from experimental data. However, since these models are built mainly for a specific compressor or turbine map, their generalization is quite difficult: in fact, regression is highly shape-dependent and, therefore, requires a different model for each different specific component. This paper proposes a solution to the problem stated above: a new method for map adaptation is investigated to improve steady-state off design prediction accuracy of a generic gas turbine component. The methodology does not employ analytical regression models; its main principle relies in performing map modifications in an appropriate neighborhood of the multiple experimental points used for the adaptation. When using gas turbine simulation codes, component maps are usually stored in a data matrix and are ordered in a format suitable for 2D interpolation. A perturbation of the values contained in the matrix results in component map morphing. An optimization algorithm varies the perturbation intensity vector in order to minimize the deviation between experimental and predicted points. The adaptation method is integrated inside TSHAFT, the gas turbine prediction code developed at the University of Padova. The assessment of this methodology will be exposed by illustrating a case study carried out upon a turbojet engine.

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References

Li, Y. G., Abdul Ghafir, M. F., Wang, L., Singh, R., Huang, K., Feng, X., and Zhang, W., 2012, “Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method,” ASME J. Eng. Gas Turbines Power, 134(3), p. 031701. [CrossRef]
Stamatis, A., Mathioudakis, K., and Papailiou, K. D., 1990, “Adaptive Simulation of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 112(2), pp. 168–175. [CrossRef]
Lambiris, B., Mathioudakis, K., and Papailiou, K. D., 1994, “Adaptive Modeling of Jet Engine Performance With Application to Condition Monitoring,” J. Propul. Power, 10(6), pp. 890–896. [CrossRef]
Li, Y. G., Pilidis, P., and Newby, M., 2005, “An Adaptation Approach for Gas Turbine Design-Point Performance Simulation,” ASME J. Eng. Gas Turbines Power, 128(4), pp. 789–795. [CrossRef]
Li, Y. G., and Pilidis, P., 2010, “GA-Based Design-Point Performance Adaptation and Its Comparison With ICM-Based Approach,” Appl. Energy, 87(1), pp. 340–348. [CrossRef]
Li, Y. G., Marinai, L., Lo Gatto, E., Pachidis, V., and Pilidis, P., 2009, “Multiple-Point Adaptive Performance Simulation Tuned to Aeroengine Test-Bed Data,” J. Propul. Power, 25(3), pp. 635–641. [CrossRef]
Li, Y. G., Abdul Ghafir, M. F., Wang, L., Singh, R., Huang, K., and Feng, X., 2011, “Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm,” ASME J. Eng. Gas Turbines Power, 133(7), p. 071701. [CrossRef]
Kong, C., and Ki, J., 2007, “Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data,” ASME J. Eng. Gas Turbines Power, 129(2), pp. 312–317. [CrossRef]
Tsoutsanis, E., Li, Y. G., Pilidis, P., and Newby, M., 2012, “Part-Load Performance of Gas Turbines: Part I—A Novel Compressor Map Generation Approach Suitable for Adaptive Simulation,” ASME 2012 Gas Turbine India Conference, Mumbai, India, December 1, ASME Paper No. GTINDIA2012-9580. [CrossRef]
Tsoutsanis, E., Li, Y. G., Pilidis, P., and Newby, M., 2012, “Part-Load Performance of Gas Turbines: Part II—Multi-Point Adaptation With Compressor Map Generation and GA Optimization,” Proceedings of the ASME 2012 Gas Turbine India Conference, Mumbai, India, December 1, ASME Paper No. GTINDIA2012-9581. [CrossRef]
Jones, G., Pilidis, P., and Curnock, B., 2001, “Compressor Characteristics in Gas Turbine Performance Modelling,” ASME Turbo Expo 2001, New Orleans, LA, June 4–7, ASME Paper No. 2001-GT-0384.
Kurzke, J., 1996, “How to Get Component Maps for Aircraft Gas Turbine Performance Calculations,” Birmingham, UK, June 10–13, ASME Paper No. 96-GT-164.
Misté, G. A., and Benini, E., 2012, “Improvements in Off-Design Aeroengine Performance Predictions Using Analytic Compressor Map Interpolation,” Int. J. Turbo Jet Eng., 29(1), pp. 69–77. [CrossRef]
Walsh, P. P., and Fletcher, P., 2004, Gas Turbine Performance, Blackwell Publishing, Oxford, UK.
Saravanamuttoo, H. I. H., 1992, “Overview on Basis and Use of Performance Prediction Methods,” Steady and Transient Performance Prediction of Gas Turbine Engines, (AGARD Lecture Series 183), Advisory Group for Aerospace Research & Development, Neuilly sur Seine, France.
Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E., 1998, “Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions,” SIAM J. Optim., 9(1), pp. 112–147. [CrossRef]

Figures

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Fig. 1

Compressor map matrixes and utilization of the β parameter (courtesy of Walsh and Fletcher [13])

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Fig. 2

Numerical procedure used by the off design solver for the case of a turbojet engine

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Fig. 3

The turbojet engine modeled for the present case study

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Fig. 4

Initial compressor map used in preliminary simulations

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Fig. 5

Beta lines defined on the compressor map

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Fig. 6

Initial turbine map used in preliminary simulations

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Fig. 7

The four operating points calculated on the adapted compressor map

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Fig. 8

The four operating points calculated on the adapted turbine map

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Fig. 9

Compressor map: comparison between initial and adapted corrected speed lines in the pressure ratio-corrected mass flow plane

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Fig. 10

Compressor map: comparison between initial and adapted corrected speed lines in the efficiency-corrected mass flow plane

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Fig. 11

Turbine map: comparison between initial and adapted corrected speed lines in the efficiency-pressure ratio plane

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