Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T12:52:08.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Part of: ISABE 2017

Published online by Cambridge University Press:  18 September 2017

Elias Tsoutsanis ,
Yi-Guang Li ,
Pericles Pilidis  and
Mike Newby
Elias Tsoutsanis*
Affiliation:
School of Engineering, Emirates Aviation University, Dubai, UAE Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK
Yi-Guang Li
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, UK
Pericles Pilidis
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, UK
Mike Newby
Affiliation:
Manx Utilities, Isle of Man, UK
*
elias.tsoutsanis@emirates.com, e.tsoutsanis@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

One of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.

Keywords

gas turbine performanceinverse modellingengine model tuningperformance adaptationoff-design performance
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1245 , November 2017 , pp. 1758 - 1777
Copyright
Copyright © Royal Aeronautical Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper was presented at the ISABE 2017 Conference, 3-8 September 2017, Manchester, UK.

References

REFERENCES

1. Transforming GE to Digital Industrial, Available at: https://www.ge.com/sites/default/files/ge_webcast_presentation_03012016_0.pdf (accessed 26.07.16).Google Scholar
2. Volponi, A. Gas turbine engine health management: Past, present, and future trends, J Engineering for Gas Turbines and Power, 2014, 136, (5), p 051201.CrossRefGoogle Scholar
3. Tahan, M., Tsoutsanis, E., Muhammad, M. and Karim, Z.A. Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, 2017, 198, pp 122-144.CrossRefGoogle Scholar
4. Bala, A., Sethi, V., Gatto, E.L., Pachidis, V. and Pilidis, P. PROOSIS-A collaborative venture for gas turbine performance simulation using an object oriented programming schema, ISABE 2007 Proceedings, 2007, Beijing, China, ISABE 1357.Google Scholar
5. Visser, M. and Broomhead, M. GSP, a generic object-oriented gas turbine simulation environment, ASME Turbo Expo 2000: Power for Land, Sea, and Air, 2000, American Society of Mechanical Engineers, Munich, Germany, p V001T01A002.CrossRefGoogle Scholar
6. Frederick, D.K., DeCastro, J.A. and Litt, J.S. User's guide for the commercial modular aero-propulsion system simulation (C-MAPSS), 2007.Google Scholar
7. Tsoutsanis, E., Meskin, N., Benammar, M. and Khorasani, K. Dynamic performance simulation of an aeroderivative gas turbine using the matlab/simulink environment, Proceedings of ASME IMECE, IMECE2013-64102, vol. 4, 2013, San Diego, California, US, p V04AT04A050.CrossRefGoogle Scholar
8. Visser, M., Kogenhop, O. and Oostveen, M. A generic approach for gas turbine adaptive modelling, J Engineering for Gas Turbines and Power, 2006, 128, (1) pp 13-19.CrossRefGoogle Scholar
9. Stamatis, A., Mathioudakis, K. and Papailiou, K. Adaptive simulation of gas turbine performance, J Engineering for Gas Turbines and Power, 1990, (2), 112.Google Scholar
10. Li, Y.G. Performance-analysis-based gas turbine diagnostics: A review, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2002, 216, (5), pp 363-377.Google Scholar
11. Li, Y.G. and Singh, R. An advanced gas turbine gas path diagnostic system-PYTHIA, XVII International Symposium on Air Breathing Engines, 2005, Munich, Germany, Paper No. ISABE-2005-1284.Google Scholar
12. Li, Y.G. and Nilkitsaranont, P. Gas turbine performance prognostic for condition-based maintenance, J Applied Energy, 2009, 86, (10), pp 2152-2161.CrossRefGoogle Scholar
13. Tsoutsanis, E., Meskin, N., Benammar, M. and Khorasani, K. A dynamic prognosis scheme for flexible operation of gas turbines, J Applied Energy, 2016, 164, pp 685-701.CrossRefGoogle Scholar
14. Tsoutsanis, E. and Meskin, N. Derivative-driven window-based regression method for gas turbine performance prognostics, Energy, 2017, 128, 302-311.CrossRefGoogle Scholar
15. Hayes, R., Dwight, R. and Marques, S. Reducing parametric uncertainty in limit-cycle oscillation computational models, The Aeronautical J, 2017, 121, (1241), pp 940-969.CrossRefGoogle Scholar
16. Kennedy, M.C. and O'Hagan, A. Bayesian calibration of computer models. J Royal Statistical Society: Series B (Statistical Methodology), 2001, 63, (3), pp 425-464.CrossRefGoogle Scholar
17. Kong, C., Ki, J. and Kang, M. A new scaling method for component maps of gas turbine using system identification, J Engineering for Gas Turbines and Power, 2003, 125, (4), pp 979-985.CrossRefGoogle Scholar
18. Kong, C., Kho, S. and Ki, J. Component map generation of a gas turbine using genetic algorithms, J Engineering for Gas Turbines and Power, 2006, 128, (1), pp 92-96.CrossRefGoogle Scholar
19. Lo Gatto, E., Li, Y.G. and Pilidis, P. Gas turbine off-design performance adaptation using a genetic algorithm, Proceedings of the ASME Turbo Expo, 2006, Barcelona, Spain.CrossRefGoogle Scholar
20. Wang, L., Li, Y.G., Huang, K. and Feng, X. Gas turbine off-design performance model improvement for diagnostics, 6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2009, Dublin, Ireland, Paper No. CM-MFPT-0149-2009.Google Scholar
21. Li, Y.G., Marinai, L., Lo Gatto, E., Pachidis, V. and Pilidis, P. Multiple point adaptive performance simulation tuned to aerospace test-bed data, J Propulsion Power, 2009, 25, (3), pp 635-641.CrossRefGoogle Scholar
22. Li, Y.G., Abdul Ghafir, M.F., Wang, L., Singh, R., Huang, K. and Feng, X. Nonlinear multiple points gas turbine off-design performance adaptation using a genetic Algorithm, J Engineering for Gas Turbines and Power, 2011, 133.CrossRefGoogle Scholar
23. Li, Y.G., Abdul Ghafir, M.F., Wang, L., Singh, R., Huang, K., Feng, X. and Zhang, W. Improved multiple point non-linear genetic algorithm based performance adaptation using least square method, J Engineering for Gas Turbines and Power, March 2012, 134, pp 031701.CrossRefGoogle Scholar
24. Yang, Q., Li, S. and Cao, Y. A new component map generation method for gas turbine adaptation performance simulation. J Mechanical Science and Technology, 2017, 31, (4), pp 1947-1957.CrossRefGoogle Scholar
25. Klapproth, J., Miller, M. and Parker, D. Aerodynamic development and performance of the cf6-6/lm2500 compressor, 4th, International Symposium on Air Breathing Engines, 1979, Orlando, Florida, US, pp 243-249.CrossRefGoogle Scholar
26. Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning, 1989, Addison-Wesley, New York, New York, US.Google Scholar
27. Wadia, R., Wolf, D.P. and Haaser, F.G. Aerodynamic design and testing of an axial flow compressor with pressure ratio of 23.3:1 for the lm2500+ gas turbine, J Turbomachinery, 2002, 124, (3), pp 331-340.CrossRefGoogle Scholar
28. The LM 2500+ Engine, Available at: http://www.geaviation.com/engines/docs/Marine/datasheet-lm2500plus.pdf (accessed 26.07.16).Google Scholar
29. Li, Y.G., Pilidis, P. and Newby, M.A. An adaptation approach for gas turbine design-point performance simulation, J Engineering for Gas Turbines and Power, 2006, 128, (4), pp 789-795.CrossRefGoogle Scholar