Hostname: page-component-6898cfcb55-w5hgh Total loading time: 0 Render date: 2026-11-11T21:25:20.935Z Has data issue: false hasContentIssue false
  • English
  • Français

Neuro-fuzzy approach for performance optimisation of variable nozzle turbofan engine

Published online by Cambridge University Press:  03 February 2016

T. R. Nada and
A. A. Hashem
T. R. Nada
Affiliation:
National Authority for Remote Sensing and Space Science, Cairo, Egypt
A. A. Hashem
Affiliation:
Aerospace Engineering Department, Cairo University, Egypt
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

An algorithm employing adaptive neuro-fuzzy online identification and sequential quadratic programming optimisation techniques is developed to enhance aircraft engine performance. This algorithm is implemented and tested using digital simulation for two spool, mixed exhaust, variable geometry turbofan engine. Parametric study is conducted to select the proper measurable parameter that can represent the actual thrust during online optimisation. Subtractive clustering technique is applied to generate the minimum number of fuzzy rules that can model the engine performance at various input parameters and flight conditions. The resulting neuro-fuzzy system is then optimised through training algorithm to accurately represent the engine. This system can address engine variations by relearning the network using online measurements from the actual engine. Generating the optimum schedules and comparing them with those obtained from the complete non-linear engine model verify the algorithm. Benefits from this algorithm include fuel consumption savings, reductions in turbine inlet temperature, and preventing limit exceeding.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 109 , Issue 1093 , March 2005, pp. 139 - 146
Copyright
Copyright © Royal Aeronautical Society 2005 

References

1. Shaw, P. and Foxgrover, J.. A design approach to a performance seeking control, June 1986, AIAA/ASME/SAE/ASEE 22nd Joint Propulsion Conference, Huntsville, Alabama, AIAA paper 86-1674.Google Scholar
2. Luppold, R.H., Roman, J.R., Gallops, G.W. and Kerr, L.J.. Estimating in-flight engine performance variations using Kalman filter concepts, July 1989, AIAA/ASME/SAE/ASEE 25th Joint Propulsion Conference, AIAA paper 89-2584.Google Scholar
3. Smith, R.H., Chisholm, J.D. and Stewart, J.F.. Optimization aircraft performance with adaptive, integrated flight/propulsion control, J Engineering for Gas Turbine and Power, ASME, January 1991, 113, pp 87–94.Google Scholar
4. Lambert, H.H.. A simulation study of turbofan engine deterioration estimation using Kalman filtering techniques, June 1991, NASA TM-104233.Google Scholar
5. Lambert, H.H., Gilyard, G.B., Chisholm, J.D. and Kerr, L.J.. Preliminary flight evaluation of an engine performance optimization algorithm, October 1991, NASA Technical Memorandum 4328.Google Scholar
6. Gelyard, G.B. and Orme, J.S.. Subsonic flight test evaluation of a performance seeking control algorithm on an F-15 airplane, August 1992, NASA Technical Memorandum 4400.Google Scholar
7. Conners, T.. Thrust stand evaluation of engine performance improvement algorithm in an F-15 airplane, AIAA/ASME/SAE/ASEE 28th Joint Propulsion Conference, July 1992, AIAA paper 92-3747.Google Scholar
8. Gelyard, G.B. and Orme, J.S.. Performance seeking control: program overview and future directions, August 1993, NASA Technical Memorandum 4531.Google Scholar
9. Orme, J.S. and Conners, T.R.. Supersonic flight test results of a performance seeking control algorithm on a NASA F-15 aircraft, June 1994, AIAA/ASME/SAE/ASEE 30th Joint Propulsion Conference, AIAA paper 94-3210.Google Scholar
10. Orme, J.S. and Gerard, S.S.. Flight assessment of the onboard propulsion system model for the performance seeking control algorithm on an F-15 aircraft, July 1995, NASA Technical Memorandum 4705.Google Scholar
11. Jang, J.-S.R.. Fuzzy modeling using generalized neural networks and Kalman filter algorithm, July 1991, Proceedings of the ninth National Conference on Artificial Intelligence (AAAI-91), pp 762–767.Google Scholar
12. Jang, J.R.. ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, May 1993, 23, (3).Google Scholar
13. Jang, J.R. and Sun, C.. Neuro-fuzzy modeling and control, Proceedings of the IEEE, March 1995, 83, (3).Google Scholar
14. Nada, T.R.. Adaptive Neuro-Fuzzy Control of Variable Geometry Turbofan Engine, February 2003, PhD dissertation, Aerospace Engineering Department, Cairo University.Google Scholar
15. Chiu, S.L.. Fuzzy model identification based on cluster estimation, J Intelligent and Fuzzy Systems, 1994, 2, pp 267–278.Google Scholar
16. Yager, R.R. and Filev, D.P.. Generation of fuzzy rules by mounting clustering, J Intelligent and Fuzzy Systems, 1994, 2, pp 209–219.Google Scholar
17. General Electric, F110-GE-100 pilot awareness program, User’s manual, 1989.Google Scholar
18. Daniele, C.J. et al. Digital computer program for generating dynamic turbofan engine models (DIGTEM), September 1983, NASA TM-83446.Google Scholar
19. Schobeiri, T., Attia, M. and Lippke, C.. GETRAN: A generic, modularly structured computer code for simulation of dynamic behavior of aero- and power generation gas turbine engines, J of Eng. for Gas Turbines and Power, July 1994, 116.Google Scholar
20. Polak, E., Mayne, D.Q. and Stimler, D.M.. Control system design via semi-infinite optimization: a review, Proceedings of the IEEE, December 1984, 72, (12).Google Scholar
21. Stimler, D.M.. Scheduling turbofan engine control set points by semiinfinite optimization June 1998, IEEE Seventh American Control Conference, Atlanta, Georgia, Proceedings, 3, pp 2256–2263.Google Scholar
22. Litt, J.S., Parker, K.I. and Chatterjee, S.. Adaptive gas turbine engine control for deterioration compensation due to aging, NASA TM-2003-212607, October 2003.Google Scholar