Neuro-Fuzzy Harmful Substances Control of Aircraft Gas Turbine Engine

One of the directions of aviation development is solving environmental problems, which excludes the emission of harmful substances into the atmosphere (nitric oxide, carbon monoxide) during the operation of an aircraft gas turbine engine (GTE) [1]. At low temperatures, oxygen and nitrogen are inert gases. At temperatures of 1100... 1600 K, oxides are formed, where nitrogen takes a valence of one to five. At temperatures above 1600 K, their atomic decomposition occurs. At temperatures in the range of 1100—1600 K, a reduction in NOx is possible with good mixing and a sufficient length of the combustion chamber, which determines the burning time of gases. If the combustion process is interrupted due to the poor operation of the automation, either vibro-combustion (atomic decomposition of NOx oxide) occurs at a temperature of 1600 K or flame failure occurs at 1100 K. Improving the process of converting the chemical energy of fuel and converting it into mechanical energy under conditions of uncertainty (variable caloric content of kerosene, changes in environmental parameters, wear of control equipment) is possible using neuro-fuzzy control of aviation gas turbine engine emissions into the environment. The control signal will be the fuel consumption in the diffusion manifold. In this case, fuel consumption in homogeneous reservoirs will vary evenly, provided that the total amount of fuel remains constant for the engine under consideration (the thrust should not change in the mode). A dynamic model of a neuro-fuzzy fuel consumption regulator by a diffusion collector has been developed. The method of obtaining training samples " % GT" = f (MNOx) for constructing the neural part of the regulator is presented. The desired " triangular" region of MNOx location (the integral of emission of nitrogen oxide emissions) is determined, on the basis of which control algorithms " with economy" and " without economy" of the MNOx integral are proposed.

aircraft engine, metering device (diffusion and homogeneous), low-emission combustion chamber, fuzzy control (fuzzification, logical inference unit, defuzzification), neural control (perceptron, error back propagation method, Levenberg-Marquardt method)

1. Avgustinovich V. G., Kuznetsova T. A., Fatykov A. I., Nugumanov A. D. The concept of management low emission combustor GTE and its expert model for training the neural network of smart regulator, Bulletin of Perm National Research Polytechnic University. Aerospace engineering, 2018, no. 53, pp. 5—19 (in Russian).

2. Vasil’ev Y. Some problems develop low-emission combustors and ways to reduce emissions of nitrogen oxides, Engine, 2016, no. 6 (108), pp. 10—13 (in Russian).

3. Lauer M., Farber J., Reith F., Masalme J. E. Model Based Prediction of Off-Design Operation Condition NOx Emission from DLE Gas Turbine Combustors, Proc. ASME Conf. Turbo Expo GT2017-63063, Charlotte, NC, USA, 2017, 11 p.

4. Marchukov E. Y., Fedorov S. A. The new concept of a low-emission combustion chambers of stationary gas turbines, developed on the basis of GTE, Vestnik of Samara State Aerospace University. Combustion processes, heat transfer and environmental heat engines, 2000, pp. 143—152 (in Russian).

5. Mosquitoes E. M. Methods reduce the emission of pollutants in combustion chambers of gas turbine engines and gas turbines, Engineering and Computer Technology, 2018, no. 05, pp. 9—29 (in Russian).

6. Avgustinovich V. G., Nazukin V. A. CFD Analysis of swirling flows in premixers, Proceedings of ASME Turbo Expo 2014 [Electronic resource]: Turbine Technical Conference and Exposition (June 16—20, 2014, Dusseldorf, Germany), American Society of Mechanical Engineers (ASME), International Gas Turbine Institute, New York: ASME, 2014, Art. No. V04AT04A0511, electronic optical disc (DVD), 10 p.

7. Markushin A. N., Baklanov A. V. Research quality fuel mixture preparation and its effect on NOx emission in low emission combustion chamber TBG, Vestnik Samara State Aerospace University. Academician S. P. Korolev (National Research University), Samara,2013, no. 3-1 (143), pp. 139—145 (in Russian).

8. Belokon A., Khirtov K., Klyachko L., Tschepin S., Zakharov V. Prediction of Combustion Efficiency and NOx Level for Diffusion Flame Combustors in HAT Cycles, Proc. ASME Conf. Turbo Expo GT2002-30609, Amsterdam, 2002, 9 p.

9. Zubilin I. A. Method for determining the boundaries of poor breakdown in the combustion chambers of gas turbines: dis. cand. tehn. Sciences: 05.07.05, Samara, Samara Publishing House nat. Inst. University Press, 2016, 169 p. (in Russian).

10. Kutsenko Y. G. Methodology of designing low-emission combustors TBG based on mathematical models of physical and chemical processes: dis.... Dr. tehn. Sciences: 05.07.05, Perm, Publishing house of Perm. state. tehn. University Press, 2010, 193 p. (in Russian).

11. Inozemtzev A. A., Sandratsky V. L. Gas turbine engines. Perm, JSC Aircraft Engine", 2006. 1204 p. (in Russian).

12. Varnatts Yu., Maas W., Dibble R. Combustion. Physical and chemical aspects, simulation experiments, the formation of pollutants, Moscow, FIZMATLIT, 2006, 352 p. (in Russian).

13. Avgustinovich V. G., Kutsenko Y. G. Creation and Application of Combined Calculation Methodology for Low Emission Combustion Chamber, Russian Aeronautics, 2011, vol. 54, no. 2, pp. 170—178.

14. Vanderhaegen E., Deneve M. Predictive Emissions Monitoring Using a Continuously Updating Neural Network, Proc. ASME Conf. Turbo Expo GT2010-22899, Glasgow, 2010, 7 p.

15. Lamont W. G., Roa M., Lucht R. Application of Artificial Neural Networks for the Prediction of Pollutant Emissions and Outlet Temperature in Fuel Staged Gas Turbine Combustion Rig, Proc. ASME Conf. Turbo Expo GT2014-25030, Dusseldorf, 2014, 10 p.

16. LamontW. G., Roa M., Lucht R. P. Application of Artificial Neural Networks for the Prediction of Pollutant Emissions and Outlet Temperature in a Fuel-Staged Gas Turbine Combustion Rig, Proceedings of ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, 2014, pp. 1—10.

17. Bulysova L. A., Vasiliev V. D., Gutnick M. M., Gutnick" M. N., Berne A. L., Pugatch K. S. Experimental studies NOx emissions during combustion of fuel in one or two sequentially arranged combustion stages, Electric stations, 2018, no.11 (1048), pp. 15—23 (in Russian).

18. Hao Zh., Kefa C., Jianren F. Modeling and optimization of the NOx emission characteristics of a tangentially firedboiler with artificial neural networks, Clean Energy and Environment Engineering Key Lab of MOE, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou, 310027, PR china Received7 February 2001.

19. Osovsky S. Neural networks for information processing, Moscvow, Finance and Statistics, 2004, 344 p. (in Russian).

20. Pegat A. Fuzzy modeling and control, Moscow, Bean. Knowledge Laboratory, 2017, 79 p. (in Russian).

21. Khizhnyakov Yu. N., Yuzhakov A. A., Titov Yu. K. Design of adaptive fuzzy control position of the metering jet engine, Electrical engineering, 2018, no. 11, pp. 6—11 (in Russian).

22. Gostev V. V. Design of fuzzy controllers for automatic control systems, SPb, BHV-Petersburg, 2011, 416 p. (in Russian).

23. Khizhnyakov Yu. N., Yuzhakov A. A., Sofin N. A. Development of an adaptive fuzzy frequency controller and an integrated model of an aircraft engine using neural technology, 14th International Conference "Aviation and Cosmonautics — 2015" , 2015, pp. 157—159 (in Russian).

24. Khizhnyakov Yu. N. Fuzzy, neural and hybrid control, Perm, Publishing house Perm. nat. researched Polytechnic University, 2013, 330 p. (in Russian).