Adaptive virtual meter of harmful substances in the combustion chamber of a gas turbine engine using fuzzy technology


Аuthors

Nikulin V. S.*, Storozhev S. A., Abdullin D. M., Khizhnyakov Y. N.

Perm National Research Polytechnic University, PNRPU, 29, Komsomolsky Prospekt, Perm, 614990, Russia

*e-mail: kalif23@yandex.ru

Abstract

The toolkit of soft computing technologies is bas on fuzzy systems, probabilistic models, neural networks, genetic algorithms, etc., which have their own advantages and disadvantages. The application of this toolkit is considered for an aircraft gas turbine engine (GTE) of aircraft operating under conditions of uncertainty. The distribution of fuel between the manifolds is performed using a fuzzy regulator, the inputs of which are the pressure in the combustion chamber, temperature in the combustion chamber, fuel consumption in the diffusion manifold. Fuzzy controller output – calculation of the current value of the coefficient of the combustion chamber or the content of the oxidant in the combustion chamber. The calculation of the amount of harmful substances is carried out using a fuzzy regulator, the inputs of which are the temperature in the combustion chamber, the fuel consumption of the diffusion manifold, the consumption of the diffusion manifold in percent and the current value of the coefficient of the combustion chamber. Fuzzy regulator output – calculation of the current value of harmful substances in the combustion chamber for further calculation of the total emission of harmful substances for the takeoff and landing mode of the aircraft. Оn the basis of the total calculation of harmful substances, it is possible to draw a conclusion on the fulfillment of the emission requirements, which is 18 kg. The research results have confirmed the reduction of the influence of the uncertainty of the combustion process and the reduction of emissions in the airfield area.

Keywords:

gas turbine engine, the combustion chamber, meter of harmful substances, neural network, fuzzy regulator

References

  1. Feoktistova O.G., Feoktistova T.G., Ekzertseva E.V. Bezopasnost' zhiznedeyatel'nosti (Life Safety), Rostov na Donu, Feniks, 2006, 320 p.

  2. Gurevich O.S. Upravlenie aviatsionnymi gazoturbinnymi dvigatelyami (Aircraft Gas Turbine Engine Control), Moscow, Izd-vo MAI, 2001, 100 p.

  3. Inozemtsev A.A., Nikhamkin M.A., Sandratskii V.L. Osnovy konstruirovaniya aviatsionnykh dvigatelei i energeticheskikh ustanovok (Basics of Designing Aircraft Engines and Power Plants), Moscow, Mashinostroenie, 2008, vol. 2, 368 p.

  4. Baklanov A.V., Krasnov D.S., Garaev A.I. Trudy MAI, 2020, no. 112. URL: http://trudymai.ru/eng/published.php?ID=116314. DOI: 10.34759/trd-2020-112-2

  5. Benderskii B.Ya., Chernova A.A. Trudy MAI, 2020, no. 111. URL: http://trudymai.ru/eng/published.php?ID=115121. DOI: 10.34759/trd-2020-111-5

  6. Baklanov A.V., Makarova G.F., Vasil'ev A.A., Nuzhdin A.A. Trudy MAI, 2018, no. 103. URL: http://trudymai.ru/eng/published.php?ID=100700

  7. Isaev A.I., Skorobogatov S.V. Trudy MAI, 2018, no. 98. URL: http://trudymai.ru/eng/published.php?ID=87340

  8. Isaev A.I., Mairovich Yu.I., Safarbakov A.M., Khodatskii S.A. Trudy MAI, 2016, no. 91. URL: http://trudymai.ru/eng/published.php?ID=75583

  9. Andrievskaya N.V., Andrievskii O.A., Legotkina T.S., Khizhnyakov Yu.N., Storozhev A.A., Nikulin V.S., Yuzhakov A.A., Kuznetsov M.D. Mekhatronika. Avtomatizatsiya. Upravlenie, 2020, vol. 2, no. 6, pp. 348 - 355.

  10. Khizhnyakov Yu.N. Nechetkoe, neironnoe i gibridnoe upravlenie (Fuzzy, Neural and Hybrid Controls), Perm', Permskii natsional'nyi issledovatel'skii politekhnicheskii universitet, 2013, 303 p.

  11. Larsen P.M. Industrial applications of fuzzy logic control, International Journal of Man-Machine Studies, 1980, vol. 12, issue 1, pp. 3 - 10. URL: https://doi.org/10.1016/S0020-7373(80)80050-2

  12. Mamdani E.H. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant, Proceedings of the IEEE, 1974, vol. 121, no. 12, pp. 1585 – 1588.

  13. Devyatkov V.V. Sistemy iskusstvennogo intellekta silovymi ustanovkami (Artificial Intelligence Systems by Power Plants), Moscow, Mashinostroenie, 1991, 320 p.

  14. Yasnitskii L.N. Intellektual'nye sistemy (Intelligent Systems), Moscow, Laboratoriya znanii, 2016, 221 p.

  15. Terekhov V.A. Neirosetevye sistemy upravleniya (Neural Network Control Systems), Moscow, Vysshaya shkola, 2002, 183 p.

  16. Rutkovskaya D., Pilin'skii M., Rutkovskii L. Neironnye seti. Geneticheskie algoritmy i nechetkie sistemy (Neural Networks. Genetic Algorithms and Fuzzy Systems), Moscow, Goryachaya liniya-Telekom, 2006, 193 p.

  17. Smirnov V.A., Khasanova A.A. Izvestiya Chelyabinskogo nauchnogo tsentra, 2003, no. 4 (21), pp. 33 - 38.

  18. Pegat A. Nechetkoe modelirovanie i upravlenie (Fuzzy Modeling and Control), Moscow, BINOM. Laboratoriya znanii, 2007, 798 p.

  19. Shtovba S.D. Proektirovanie nechetkikh sistem sredstvami MATLAB (Design of Fuzzy Systems Using MATLAB), Moscow, Goryachaya liniya-Telekom, 2007, 288 p.

  20. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i FuzzyTech (Fuzzy Modeling in MATLAB and FuzzyTech), Moscow, Izd-vo “BKhV-Peterburg”, 2005, 736 p.


Download

mai.ru — informational site MAI

Copyright © 2000-2021 by MAI

Вход