Title

Neural network approach to compressor modelling with surge margin consideration

Journal title

Archives of Thermodynamics

Yearbook

2022

Volume

vol. 43

Issue

No 1

Affiliation

Loryś, Sergiusz Michał : Hamilton Sundstrand Poland / Pratt & Whitney AeroPower Rzeszów, Hetmanska 120, 35-078 Rzeszów, Poland ; Orkisz, Marek : Rzeszow University of Technology, Department of Aerospace Engineering, Powstanców Warszawy 8, 35-959 Rzeszów, Poland

Authors

Loryś, Sergiusz Michał ; Orkisz, Marek

Keywords

Modelling ; Compressor map ; Neural-network

Divisions of PAS

Nauki Techniczne

Coverage

89-108

Publisher

The Committee of Thermodynamics and Combustion of the Polish Academy of Sciences and The Institute of Fluid-Flow Machinery Polish Academy of Sciences

Bibliography

[1] Sieber J.: European Technology Programs for Eco-Efficient Ducted Turbofans. ISABE-2015-20029, 2015.
[2] European Commission, Directorate-General for Mobility and Transport, Directorate- General for Research and Innovation. Flightpath 2050: Europe’s vision for aviation: maintaining global leadership and serving society’s needs. Publications Office, 2011.
[3] Orkisz M., Stawarz S.: Modeling of turbine engine axial-flow compressor and turbine characteristics. J. Propul. Power 16(2000), 2, 336–339.
[4] Gholamrezaei M., Ghorbanian K.: Compressor map generation using a feedforward neural network and rig data. P.I. Mech. Eng. A: J. Power Energ. 224(2010), 1, 97–108.
[5] Tsoutsanis E., Meskin N., Benammar M., Khorasani K.: Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps. ASME J. Eng. Gas Turbines Power 137(2015), 9, 091201.
[6] Walsh P., Fletcher P.: Gas Turbine Performance. Blackwel, Bristol 2004.
[7] Sethi V., Doulgeris G., Pilidis P., Nind, A., Doussinault M., Cobas P., Rueda A.: The map fitting tool methodology: gas turbine compressor off-design performance modeling. ASME J. Turbomach. 135(2013), 6, 061010.
[8] Kurzke J.: How to get component maps for aircraft gas turbine performance calculations. Proc. ASME 1996 Int. Gas Turbine and Aeroengine Cong. Exhibit., Vol. 5, Birmingham, June 10–13, 1996, V005T16A0011996. ASME Pap. 96-GT-164.
[9] Misté G., Benini E.: Improvements in off design aeroengine performance prediction using analytic compressor map interpolation. Int. J. Turbo Jet Eng. 29(2012), 69–77.
[10] Muszynski M., Orkisz M.: Turbine Jet Engine Modelling. Ser. Scientific Library 7, Institute of Aviation, Warszawa 1997 (in Polish).
[11] Jones G., Pilidis P., Curnock B.: Extrapolation of Compressor Characteristics to the Low-Speed Region for Sub-Idle Performance Modelling. Proc. ASME Turbo Expo 2002, Power for Land, Sea, and Air, Vol, 2, Turbo Expo 2002, Pts., A, B, Amsterdam, June 3–6, 2002, 861–867. ASME Pap. GT2002–30649.
[12] De-You Y., Zhong-Fan M.: A dynamic model of turbojet in starting at high altitude. AIAA Pap. 83–7045, 1983.
[13] Jensen J., Kristensen A., Sorenson S., Houbak N. Hendricks E.: Mean value modeling of a small turbocharged diesel engine. SAE Tech. Pap. 910070, 1991.
[14] Tsoutsanis E., Meskin N., Benammar M., Khorasani K.: A component map tuning method for performance prediction and diagnostics of gas turbine compressors. Appl. Energ. 135(2014), 572–585.
[15] Tsoutsanis E., Meskin N., Benammar M., Khorasani K.: An Efficient Component Map Generation Method for Prediction of Gas Turbine Performance. Proc. ASME Turbo Expo 2014, Turbine Technical Conference and Exposition, Vol. 6, Düsseldorf, June 16–20, 2014, V006T06A006, ASME Pap. GT2014–25753.
[16] Trawinski P.: Development of flow and efficiency characteristics of an axial compressor with an analytical method including cooling air extraction and variable inlet guide vane angle. Arch. Thermodyn. 42(2021), 4, 17–46.
[17] Converse G.L., Giffen R.G.: Representation of Compressor Fans and Turbines. Vol. 1. CMGEN User’s Manual, NASA-CR-174645, 1984.
[18] Kong C., Ki J., Kang M.: A new scaling method for component maps of gas turbine using system identification. ASME J. Eng. Gas Turbines Power 125(2003), 4, 979–985.
[19] Kong C., Kho S., Ki J.: Component map generation of a gas turbine using genetic algorithms. ASME J. Eng. Gas Turbines Power 128(2006), 1, 92–96.
[20] Kong C., Ki J., Lee C.: Components map generation of gas turbine engine using genetic algorithms and engine performance deck data. Proc. ASME Turbo Expo 2006, Power for Land, Sea, and Air, Vol. 4, Barcelona, May 8–11, 2006, 377–383. ASME Pap. GT2006–90975.
[21] Zagorowska M., Thornhill N.: Compressor map approximation using Chebyshev polynominals. In: Proc. IEEE 2017, 25th Mediterranean Conf. on Control and Automation, Valletta, July 3–6, 2017, 864–869.
[22] Li X., Yang C., Wang Y., Wang H., Zu X., Sun Y., Hu S.: Compressor map regression modelling based on partial least squares. R. Soc. Open Sci. 5(2018), 8, 172454.
[23] Ghorbanian K., Gholamrezaei Mohammad.: An artificial neural network approach to compressor performance prediction. Appl. Energ. 86(2009), 1210–1221.
[24] Ghorbanian K, Gholamrezaei M.: Axial compressor performance map prediction using artificial neural network. Proc. ASME Turbo Expo 2007, Power for Land, Sea, and Air, Vol. 6, Turbo Expo 2007, Pts. A, B, Montreal, May 14-17, 2007, 1199–1208, ASME Pap. GT2007–27165.
[25] Youhong Y., Lingen Ch., Fengrui S., Chih W.: Neural-network based analysis and prediction of a compressor’s characteristic performance map. Appl. Energ. 84(2007), 1, 48–55.
[26] Hornik K., Stinchcombe M., White H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(1989), 5, 359–366.
[27] Pinkus A.: Approximation theory of the MLP model in neural networks. Acta Numerica, 8(1999), 143–195.
[28] Hagan M.T., Demuth H.B., Jesús O.D.: An introduction to the use of neural networks in control systems. Int. J. Robust Nonlin. Contr. 12(2002), 959–985.
[29] www.mathworks.com/help/deeplearning/ref/neuralnetfitting-app.html (accessed 17 Apr. 2021).
[30] Hagan M.T., Menhaj M.B.: Training feedforward networks with the Marquardt algorithm. IEEE T. Neural Netw. 5(1994), 6, 989–993.
[31] Burden F., Winkler D.: Bayesian regularization of neural networks. In: Artificial Neural Networks), ser. Methods in Molecular Biology Vol. 458 (D.J. Livingstone, Eds.). Humana Press, 2008.

Date

2022.04.13

Type

Article

Identifier

DOI: 10.24425/ather.2022.140926

Editorial Board

International Advisory Board

J. Bataille, Ecole Central de Lyon, Ecully, France

A. Bejan, Duke University, Durham, USA

W. Blasiak, Royal Institute of Technology, Stockholm, Sweden

G. P. Celata, ENEA, Rome, Italy

L.M. Cheng, Zhejiang University, Hangzhou, China

M. Colaco, Federal University of Rio de Janeiro, Brazil

J. M. Delhaye, CEA, Grenoble, France

M. Giot, Université Catholique de Louvain, Belgium

K. Hooman, University of Queensland, Australia

D. Jackson, University of Manchester, UK

D.F. Li, Kunming University of Science and Technology, Kunming, China

K. Kuwagi, Okayama University of Science, Japan

J. P. Meyer, University of Pretoria, South Africa

S. Michaelides, Texas Christian University, Fort Worth Texas, USA

M. Moran, Ohio State University, Columbus, USA

W. Muschik, Technische Universität Berlin, Germany

I. Müller, Technische Universität Berlin, Germany

H. Nakayama, Japanese Atomic Energy Agency, Japan

A. Nenarokomov, Moscow Aviation Institute, Russia

S. Nizetic, University of Split, Croatia

H. Orlande, Federal University of Rio de Janeiro, Brazil

M. Podowski, Rensselaer Polytechnic Institute, Troy, USA

A. Rusanov, Institute for Mechanical Engineering Problems NAS, Kharkiv, Ukraine

M. R. von Spakovsky, Virginia Polytechnic Institute and State University, Blacksburg, USA

A. Vallati, Sapienza University of Rome, Italy

H.R. Yang, Tsinghua University, Beijing, China