Compressor map generation using a feed-forward neural network and rig data

Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy

Volumn 224, Issue 1, 2010, Pages 97-108

  Gholamrezaei, M ^a   Ghorbanian, K ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Axial compressor; Feed forward neural network; Performance map

Indexed keywords

AXIAL COMPRESSORS; BAYESIAN REGULARIZATION; EXPERIMENTAL DATA; LEVENBERG-MARQUARDT ALGORITHM; MAP GENERATION; MASS FLOW RATE; MAXIMUM EFFICIENCY; PERFORMANCE MAPS; PRESSURE RATIO; SHAFT SPEED;

AXIAL FLOW TURBOMACHINERY; COMPRESSORS; MINING;

NEURAL NETWORKS;

EID: 77049107158     PISSN: 09576509     EISSN: None     Source Type: Journal    
DOI: 10.1243/09576509JPE792     Document Type: Article

References (41)

1. Walsh, P. P. and Fletcher, P. Gas turbine performance, 2004 (ASME Press, New York, co-published with Blackwell Science, UK).
2. Kurzke, J. How to get component maps for an aircraft gas-turbine's performance calculations. In Proceedings of the ASME Turbo Expo, 1996, paper no. 96-GT-164.
3. Ismail, I. H. and Bhinder, F. S. Simulation of aircraft gasturbine engine. ASME, J. Eng. Gas Turbines Power, 1991, 113, 95-99.
4. El-Gammal, M. A. An algorithm and criteria for compressor characteristics real time modeling and approximation. ASME, J. Eng. Gas Turbines Power, 1991, 113, 112-117.
5. Sieros, G., Stamatis, A., and Mathioudakis, K. Jet engine component maps for performance modeling and diagnosis. AIAA, J. Propuls. Power, 1997, 13(5), 665-674.
6. Moraal, P. and Kolmanovsky, I. Turbocharger modeling for automotive control application. SAE Trans., 1999, 108, 1324-1338.
7. Orkisz, M. and Stawarz, S. Modeling of turbine engine axial-flow compressor and turbine characteristics. AIAA, J. Propuls. Power, 2000, 16(2), 336-339.
8. Kong,C. D.,Kho, S., and Ki, J.Y. Component map generation of a gas turbine using genetic algorithms. ASME, J. Eng. Gas Turbines Power, 2006, 128(1), 92-96.
9. Kong, C. D. and Ki, J. Y. Components map generation of gas turbine engine using genetic algorithms and engine performance deck data. ASME, J. Eng. Gas Turbines Power, 2007, 129(2), 312-317.
10. Faller,W., Scott, J. S., and Luttges, M.W. Neural network prediction and control of three-dimensional unsteady separated flowfields. AIAA, J. Aircr., 1995, 32(1), 1213-1220.
11. Rai, M. M. and Madavan, N. K. Application of artificial neural networks to the design of turbomachinery airfoils. AIAA, J. Propuls. Power, 2001, 17(1), 176-183.
12. Lazzaretto, A. and Toffolo, A. Analytical and neural network models for gas turbine design and offdesign simulation. Int. J. Appl. Thermodyn., 2001, 4(4), 173-182.
13. Joly, R. B., Ogaji, O. T., Singh, R., and Probert, S. D. Gas-turbine diagnostics using an artificial neural network for a high bypass ratio military turbofan engine. J. Appl. Energy, 2004, 78(4), 397-418.
14. Verma, R., Roy, N., and Ganguli, R. Gas turbine diagnostics using a soft computing approach. Appl. Math. Comput., 2006, 172(1), 1342-1363.
15. Volponi, A.J., Depold, H., Ganguli, R., and Daguang, C. The use of kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study.ASME, J.Eng. GasTurbinesPower, 2003, 125(4), 917-924.
16. Ganguli, R. Application of fuzzy logic for fault isolation of jet engines. ASME, J. Eng. Gas Turbines Power, 2003, 125(3), 617-623.
17. Ganguli, R. A fuzzy logic intelligent system for gas turbine module and system fault isolation. AIAA, J. Propuls. Power, 2002, 18(2), 440-447.
18. Bao, C., Ouyang, M., and Yi, B. Modeling and optimization of the air system in polymer exchange membrane fuel cell system. J. Power Sources, 2006, 156(2), 232-243.
19. Yu,Y., Chen, L., Sun, F., andWu,C. Neural network based analysis and prediction of a compressor's characteristic performance map. J. Appl. Energy, 2007, 84(1), 48-55.
20. Ghorbanian, K. and Gholamrezaei, M. Neural network modeling of axial flow compressor performance map. In Proceedings of the 45th AIAA Aerospace ScienceMeeting and Exhibit, Reno, USA, 2006.
21. Ghorbanian, K. and Gholamrezaei,M. Axial compressor performance map prediction using artificial neural network. In Proceedings of the ASME Turbo Expo, paper no. GT2007-27165,Montreal, Canada, 2007, pp. 1199-1208. (Pubitemid 47436524)
22. Ghorbanian, K. and Gholamrezaei, M. An artificial neural network approach to compressor performance prediction. J. Appl. Energy, 2009, 86(8), 1210-1221.
23. Venturini,M. Development and experimental validation of a compressor dynamic model. ASME, J. Turbomach., 2005, 127(3), 599-608.
24. Venturini,M. Simulation of compressor transient behavior through recurrent neural network models. ASME, J. Turbomach., 2006, 128(4), 1-11.
25. Venturini, M. Optimization of a real-time simulator based on recurrent neural networks for compressor transient behavior prediction. ASME, J. Turbomach., 2007, 129(3), 1-11.
26. Hansen, K. E., Jorgensen, P., and Larsen, P. S. Experimental and theoretical study of surge in a small centrifugal compressor. ASME, J. Fluids Eng., 1981, 103, 391-395. (Pubitemid 12439945)
27. Moore, F. K. and Greitzer, E. M. A theory of post-stall transients in axial compression systems: part I - development of equations. ASME, J. Eng. Gas Turbines Power, 1986, 108, 68-76.
28. Pinsley, J. E., Guenette, G. R., Epstein, A. H., and Greitzer, E. M. Active stabilization of centrifugal compressor surge. ASME, J. Turbomach., 1991, 113, 723-732.
29. Badmus, O. O., Chowdhury, S., and Nett, C. N. Nonlinear control of surge in axial compression systems. Automatica, 1996, 32, 59-70.
30. Meuleman, C. H. J. Measurement and unsteady flow modeling of centrifugal compressor surge. PhD Dissertation, Technische Universiteit Eindhoven, Eindhoven, Netherlands, 2002.
31. Gravdahl, J. T. and Egeland, O. Centrifugal compressor surge and speed control. IEEE Trans., Control Syst. Technol., 1999, 7, 567-579.
32. Gravdahl,J.T.,Willems,F., Jager,B.D.E.,and Egeland,O. Modeling of surge in free-spool centrifugal compressors: Experimental validation. AIAA, J. Propuls. Power, 2004, 20, 849-857.
33. Van Helvoirt, J. Centrifugal compressor surge modeling and identification for control. PhD Dissertation, Technische Universiteit Eindhoven, Eindhoven, Netherlands, 2007.
34. Hornik, K. M., Stinchcombe, M., and White, H. Multilayer feedforward networks are universal approximators. Neural Netw., 1989, 2, 359-366.
35. Pinkus, A. Approximation theory of the MLP model in neural networks. Acta Numer., 1999, 8, 143-195.
36. Niyogi, P. and Girosi, F. Generalization bounds for function approximation from scattered noisy data. Adv. Comput.Math., 1999, 10, 51-80.
37. Hagan, M. T., Demuth, H. B., and Beale, M. Neural network design, 1997 (PWS Publishing Co., Boston, MA).
38. Hagan, M. T. and Menhaj, M. Training multilayer networks with the marquardt algorithm. IEEE Trans.,Neural Netw., 1994, 5(6), 989-993.
39. MacKay,D. J.C. Bayesian interpolation.Neural Comput., 1992, 4(3), 415-447.
40. Foresee, F. D. and Hagan, M. T. Gauss-newton approximation to bayesian learning. In Proceedings of the International Joint Conference on Neural networks, Houston, TX, USA, 9-12 June 1997, pp. 1930-1935.
41. Gravdahl, J. T. and Egeland, O. Compressor surge and rotating stall modeling and control, 1999 (Springer, London).