Micro gas turbine engine imitation model


Benches are widely used in the process of creating automatic control systems (ACS) for gas turbine engines (GTE). The engine work is simulated via mathematical models. The model is supposed to be highly accurate in conditions of quite rapid calculation of the engine main parameters in both steady-state conditions and transient processes. A simulation model of a micro gas turbine Jet Cat P-60 engine is presented in the article. The model is based on the data obtained from generalized engine performance. A simulation model of a small-sized gas turbine engine is developed by plotting the rotor acceleration against the rotor rotational speed and the combustion chamber fuel consumption at each moment of time. The results of simulating the engine operation are compared with the experimental data obtained with increasing the GTE operation mode. The average modeling error value is obtained: 1.04% (ambient temperature is 288 K), 2.58% (249 K). It is acceptable for an adequate simulation of the GTE bench performance.

About the authors

A. V. Kuznetsov

Samara National Research University

Author for correspondence.
Email: a.v.kuznetsov91@mail.ru

Russian Federation

postgraduate student

G. M. Makaryants

Samara National Research University

Email: georgy.makaryants@gmail.com

Russian Federation

Doctor of Science (Engineering), Assistant Professor
Professor of the Department of Power Plants Automatic Systems


  1. Gol'berg F.D., Batenin A.V. Matematicheskie modeli gazoturbinnykh dvigateley kak ob"ektov upravleniya [Mathematical models of gas turbine engines as objects of control]. Moscow: Moscow Aviation Institute Publ., 1999. 79 p.
  2. Tkachenko A.Yu., Rybakov V.N., Krupenich I.N., Ostapuk Ya.A., Filinov E.P. Computer-aided system of virtual gas turbine engine testing. Vestnik of the Samara State Aerospace University. 2014. No. 5(47), part 3. P. 113-119. (In Russ.)
  3. Badami M., Ferrero M.G., Portoraro A. Dynamic parsimonious model and experimental validation of a gas microturbine at part-load conditions. Applied Thermal Engineering. 2014. V. 75. P. 14-23. doi: 10.1016/j.applthermaleng.2014.10.047
  4. Arsalis A. Thermoeconomic modeling and parametric study of hybrid SOFC-gas turbine-steam turbine power plants ranging from 1.5 to 10MWe. Journal of Power Sources. 2008. V. 181. P. 313-326. doi: 10.1016/j.jpowsour.2007.11.104
  5. Hosseinalipour S.M., Razaghi E., Abdolahi M. Static and dynamic mathematical modeling of a micro gas turbine. Journal of Mechanics. 2013. V. 29, Iss. 02. P. 327-335. doi: 10.1017/jmech.2013.3
  6. Boyko L.G., Karpenko E.L., Ahtemenko Yu. Method of calculating GTE gas-thermodynamic parameters with blade row description of an axial multistage compressor. Vestnik of the Samara State Aerospace University. 2013. No. 3 (41), part 2. P. 31-39. (In Russ.)
  7. Akhmedzyanov D.A. Non-stable regimes of aviation GTE. Vestnik UGATU. 2006. V. 7, no. 1. P. 36-46. (In Russ.)
  8. Shendaleva E.V. Gas turbine engine simulation in state space: dynamic aspect. Vestnik SibADI. 2012. No. 5 (27). P. 106-111. (In Russ.)
  9. Gimadiev A.G., Shakhmatov E.V., Shorin V.P. Sistemy avtomaticheskogo regulirovaniya aviatsionnykh GTD: uchebnoe posobie [Aircraft gas turbine engine automatic control systems]. Kuybyshev: Kuybyshev Aviation Institute Publ., 1990. 120 p.
  10. Asgari H., Chen X.Q., Morini M., Pinelli M., Sainudin R., Spina P.R., Venturini M. NARX models for simulation of the start-up operation of a singleshaft gas turbine. Applied Thermal Engineering. 2015. V. 93. P. 368-376. doi: 10.1016/j.applthermaleng.2015.09.074
  11. Nikpey H., Assadi M., Breuhaus P. Development of an optimized artificial neural network model for combined heat and power micro gas turbines. Applied Energy. 2013. V. 108. P. 137-148. doi: 10.1016/j.apenergy.2013.03.016
  12. Tayarani-Bathaie S.S., Vanini Z.N.S., Khorasani K. Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing. 2014. V. 125. P. 163-165. doi: 10.1016/j.neucom.2012.06.050
  13. Kuznetsov A.V., Makaryants G.M. Development of a neural network model of a micro gas turbine engine. Vestnik of the Samara State Aerospace University. 2016. V. 15, no. 2. P. 131-144. (In Russ.). doi: 10.18287/2412-7329-2016-15-2-131-144
  14. Kulagin V.V. Teoriya, raschet i proektirovanie aviatsionnykh dvigateley i energeticheskikh ustanovok [Theory, calculation and design of aircraft engines and power plants]. Moscow: Mashinostroenie Publ., 2003. 616 p.



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