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

Abstract

In this article, a feed-forward neural network is explored to reconstruct the performance map of an axial compressor through the utilization of a limited number of experimental data. The Levenberg—Marquardt algorithm with Bayesian regularization method is used to adjust the weights and biases of the network. The proposed technique is utilized to estimate the mass flowrate, the pressure ratio, the shaft speed, and the efficiency in regions where no experimental data are available. The surge line is predicted and the line of maximum efficiencies is determined. The results are compared with experimental data.

Keywords

axial compressor performance map feed-forward neural network

Get full access to this article

View all access options for this article.

References

Walsh

P. P.

Fletcher

, Gas turbine performance (New York: ASME Press 2004) co-published with Blackwell Science, UK.

Kurzke

, ‘How to get component maps for an aircraft gas-turbine's performance calculations’ (1996) paper no. 96-GT-164.

Ismail

I. H.

Bhinder

F. S.

, ‘Simulation of aircraft gas-turbine engine’ ASME, J. Eng. Gas Turbines Power 113 (1991): 95–99.

El-Gammal

M. A.

, ‘An algorithm and criteria for compressor characteristics real time modeling and approximation’ ASME, J. Eng. Gas Turbines Power 113 (1991): 112–117.

Sieros

Stamatis

Mathioudakis

, ‘Jet engine component maps for performance modeling and diagnosis’ AIAA, J. Propuls. Power 13 5 (1997): 665–674.

Moraal

Kolmanovsky

, ‘Turbocharger modeling for automotive control application’ SAE Trans. 108 (1999): 1324–1338.

Orkisz

Stawarz

, ‘Modeling of turbine engine axial-flow compressor and turbine characteristics’ AIAA, J. Propuls. Power 16 2 (2000): 336–339.

Kong

C. D.

Kho

J. Y.

, ‘Component map generation of a gas turbine using genetic algorithms’ ASME, J. Eng. Gas Turbines Power 128 1 (2006): 92–96.

Kong

C. D.

J. Y.

, ‘Components map generation of gas turbine engine using genetic algorithms and engine performance deck data’ ASME, J. Eng. Gas Turbines Power 129 2 (2007): 312–317.

10.

Faller

Scott

J. S.

Luttges

M. W.

, ‘Neural network prediction and control of three-dimensional unsteady separated flowfields’ AIAA, J. Aircr. 32 1 (1995): 1213–1220.

11.

Rai

M. M.

Madavan

N. K.

, ‘Application of artificial neural networks to the design of turbomachinery airfoils’ AIAA, J. Propuls. Power 17 1 (2001): 176–183.

12.

Lazzaretto

Toffolo

, ‘Analytical and neural network models for gas turbine design and off-design simulation’ Int. J. Appl. Thermodyn. 4 4 (2001): 173–182.

13.

Joly

R. B.

Ogaji

O. T.

Singh

Probert

S. D.

, ‘Gas-turbine diagnostics using an artificial neural network for a high bypass ratio military turbofan engine’ J. Appl. Energy 78 4 (2004): 397–418.

14.

Verma

Roy

Ganguli

, ‘Gas turbine diagnostics using a soft computing approach’ Appl. Math. Comput. 172 1 (2006): 1342–1363.

15.

Volponi

A.J.

Depold

Ganguli

Daguang

, ‘The use of kalman filter and neural network methodologies in gas turbine performance diagnostics: A comparative study’ ASME, J. Eng. Gas Turbines Power 125 4 (2003): 917–924.

16.

Ganguli

, ‘Application of fuzzy logic for fault isolation of jet engines’ ASME, J. Eng. Gas Turbines Power 125 3 (2003): 617–623.

17.

Ganguli

, ‘A fuzzy logic intelligent system for gas turbine module and system fault isolation’ AIAA, J. Propuls. Power 18 2 (2002): 440–447.

18.

Bao

Ouyang

, ‘Modeling and optimization of the air system in polymer exchange membrane fuel cell system’ J. Power Sources 156 2 (2006): 232–243.

19.

Chen

Sun

, ‘Neural network based analysis and prediction of a compressor's characteristic performance map’ J. Appl. Energy 84 1 (2007): 48–55.

20.

Ghorbanian

Gholamrezaei

, ‘Neural network modeling of axial flow compressor performance map’ (2006).

21.

Ghorbanian

Gholamrezaei

, ‘Axial compressor performance map prediction using artificial neural network’ (2007) paper no. GT2007-27165, Montreal, Canada, 2007, pp. 1199–1208.

22.

Ghorbanian

Gholamrezaei

, ‘An artificial neural network approach to compressor performance prediction’ J. Appl. Energy 86 8 (2009): 1210–1221.

23.

Venturini

, ‘Development and experimental validation of a compressor dynamic model’ ASME, J. Turbomach. 127 3 (2005): 599–608.

24.

Venturini

, ‘Simulation of compressor transient behavior through recurrent neural network models’ ASME, J. Turbomach. 128 4 (2006): 1–11.

25.

Venturini

, ‘Optimization of a real-time simulator based on recurrent neural networks for compressor transient behavior prediction’ ASME, J. Turbomach. 129 3 (2007): 1–11.

26.

Hansen

K. E.

Jorgensen

Larsen

P. S.

, ‘Experimental and theoretical study of surge in a small centrifugal compressor’ ASME, J. Fluids Eng. 103 (1981): 391–395.

27.

Moore

F. K.

Greitzer

E. M.

, ‘A theory of post-stall transients in axial compression systems: Part I—development of equations’ ASME, J. Eng. Gas Turbines Power 108 (1986): 68–76.

28.

Pinsley

J. E.

Guenette

G. R.

Epstein

A. H.

Greitzer

E. M.

, ‘Active stabilization of centrifugal compressor surge’ ASME, J. Turbomach. 113 (1991): 723–732.

29.

Badmus

O. O.

Chowdhury

Nett

C. N.

, ‘Nonlinear control of surge in axial compression systems’ Automatica 32 (1996): 59–70.

30.

Meuleman

C. H. J.

, Measurement and unsteady flow modeling of centrifugal compressor surge (2002) PhD Dissertation Technische Universiteit Eindhoven, Eindhoven, Netherlands, 2002.

31.

Gravdahl

J. T.

Egeland

, ‘Centrifugal compressor surge and speed control. IEEE Trans’. Control Syst. Technol. 7 (1999): 567–579.

32.

Gravdahl

J. T.

Willems

Jager

B. D. E.

Egeland

, ‘Modeling of surge in free-spool centrifugal compressors: Experimental validation’ AIAA, J. Propuls. Power 20 (2004): 849–857.

33.

Van Helvoirt

, Centrifugal compressor surge modeling and identification for control (2007) PhD Dissertation.

34.

Hornik

K. M.

Stinchcombe

White

, ‘Multilayer feedforward networks are universal approximators’ Neural Netw. 2 (1989): 359–366.

35.

Pinkus

, ‘Approximation theory of the MLP model in neural networks’ Acta Numer. 8 (1999): 143–195.

36.

Niyogi

Girosi

, ‘Generalization bounds for function approximation from scattered noisy data’ Adv. Comput. Math. 10 (1999): 51–80.

37.

Hagan

M. T.

Demuth

H. B.

Beale

, Neural network design (Boston, MA: PWS Publishing Co. 1997).

38.

Hagan

M. T.

Menhaj

, ‘Training multilayer networks with the marquardt algorithm’ IEEE Trans., Neural Netw. 5 6 (1994): 989–993.

39.

MacKay

D. J. C.

, ‘Bayesian interpolation’ Neural Comput. 4 3 (1992): 415–447.

40.

Foresee

F. D.

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.

Egeland

, Compressor surge and rotating stall modeling and control (London: Springer 1999).