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Tytuł artykułu

Development of Turboshaft Engine Adaptive Dynamic Model: Analysis of Estimation Errors

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the most perspective directions of aircraft engine development is related to implementing adaptive automatic electronic control systems (ACS). The significant elements of these systems are algorithms of matching of mathematical models to actual performances of the engine. These adaptive models are used directly in control algorithms and are a combination of static and dynamic sub-models. This work considers the dynamic sub-models formation using the Least Square method (LSM) on a base of the engine parameters that are measured in-flight. While implementing this function in the (ACS), the problem of checking the sufficiency of the used information for ensuring the required precision of the model arises. We must do this checking a priori (to determine a set of operation modes, the shape of the engine test impact and volume of recorded information) and a posteriori. Equations of the engine models are considered. Relations are derived that determine the precision of parameters of these models’ estimation depending on the precision of measurement, the composition of the engine power ratings, and durability of observations, at a stepwise change of fuel flow. We present these relations in non-dimensional coordinates that make them universal and ready for application to any turboshaft engine.
Rocznik
Strony
59--71
Opis fizyczny
Bibliogr. 24 poz., rys., wykr., wzory
Twórcy
  • Aircraft Engine Design Department, Faculty of Aviation Engines, National Aerospace University ‘Kharkiv Aviation Institute’, 17 Chkalova Street, Kharkiv, Ukraine
  • Aircraft Engine Design Department, Faculty of Aviation Engines, National Aerospace University ‘Kharkiv Aviation Institute’, 17 Chkalova Street, Kharkiv, Ukraine
Bibliografia
  • [1] Jaw, L. and Mattingly, J. “Aircraft Engine Controls: Design, System Analysis, and Health Monitoring.” American Institute of Aeronautics and Astronautics, Inc., Reston, USA (2009): p. 378
  • [2] Wang, J., Zhang, W., and Hu, Z. “Model-Based Nonlinear Control of Aeroengines.” Springer Nature Singapore Pvt. Ltd (2022): p. 238.
  • [3] Yepifanov, S. “Aircraft Turbine Engine Automatic Control Based on Adaptive Dynamic Models.” Transactions on Aerospace Research Vol. 4, No. 261 (2020): pp. 61-70.
  • [4] Ibrahem, I.M.A., Akhrif, O., Moustapha, H., and Staniszewski, M. “Nonlinear Generalized Predictive Controller Based on Ensemble of NARX Models for Industrial Gas Turbine Engine.” Energy Vol. 230, No. 1 (2021): p. 120700, 14.
  • [5] Kim, S. “A New Performance Adaptation Method for Aero Gas Turbine Engines Based on Large Amounts of Measured Data.” Energy Vol. 221 (2021): p. 119863, 15.
  • [6] Wei, Z., Zhang, S., Jafari, S., and Nikolaidis, T. “Self-Enhancing Model-Based Control for Active Transient Protection and Thrust Response Improvement of Gas Turbine Aero-Engines.” Energy Vol. 242 (2022): p. 123030, 17.
  • [7] Lietzau, K. and Kreiner, A. “Model Based Control Concepts for Jet Engines.” ASME paper 2001-GT-0016. (2001): p. 8.
  • [8] Panov, V. “Model-Based Control and Diagnostic Techniques for Operational Improvements of Gas Turbine Engines.” 10th European Turbomachinery Conference: p. 8. Lappeenranta, Finland (2013).
  • [9] Guicherd, R. “Distributed Model-Based Control of Turbine Engines.” PhD Thesis. University of Sheffield. (2018): p. 212.
  • [10] Rutkovskii, V.Y., Ilyasov, B.G., and Kabalnov, Y.S. “Adaptive Control Systems of Aircraft Gas Turbine Engines.” Moscow Aviation Institute, Moscow (1995): p. 224.
  • [11] Wei, Z., Zhang, S., Jafari, S., and Nikolaidis, T. “Gas Turbine Aero-Engines Real Time On-Board Modelling: A review, Research Challenges, and Exploring the Future.” Progress in Aerospace Sciences Vol. 121 (2020): p. 100693, 16.
  • [12] Kim, S., Kim, K., and Son, C. “A New Transient Performance Adaptation Method for an Aero Gas Turbine Engine.” Energy Vol. 193 (2020): p. 116752, 10.
  • [13] Visser, W.P.J., Kogenhop, O., and Oostveen, M. “A Generic Approach for Gas Turbine Adaptive Modeling”. ASME Journal of Engineering for Gas Turbines and Power, vol. 128 GTP-04-1039 (2004).
  • [14] Mavris, D. and Denney, R. “Optimal Robust Matching of Engine Models to Test Data.” Technical Report No. AFRL-SR-AR-TR-09-0119. Aerospace Systems Design Lab., School of Aerospace Eng., Georgia Institute of Technology, Atlanta, GA (2009): p. 52.
  • [15] Roth, B., Doel, D.L., Mavris, D., and Beeson, D. “High-Accuracy Matching of Engine Performance Models to Test Data.” Proceedings of ASME Turbo Expo 2003: Power for Land, Sea and Air: GT2003-G38784, Atlanta, Georgia, USA, June 16-19, (2003): p. 9.
  • [16] Zwingenberg, M., Benra, F.K., Werner, K., and Dobrzynski, B. “Generation of Turbine Maps Using a Fusion of Validated Operational Data and Streamline Curvature Method.” Proceedings of the ASME Turbo Expo 2010: Power for Land, Sea, and Air. Volume 7: Turbomachinery, Parts A, B, and C: pp. 2757-2768. Glasgow, UK, June 14-18, (2010).
  • [17] Xu, M., Liu, J., Li, M., Geng, J., Wu, Y., and Song, Z. “Improved Hybrid Modeling Method with Input and Output Self-Tuning for Gas Turbine Engine.” Energy Vol. 238 (2022): pp. 121672, 19.
  • [18] Xu, M., Wang, J., Liu, J., Li, M., Geng, J., Wu, Y., and Song, Z. “An Improved Hybrid Modeling Method Based on Extreme Learning Machine for Gas Turbine Engine.” Aerospace Science and Technology Vol. 107 (2020): p. 106333, 13.
  • [19] Breikin, T., Arkov, V., and Kulikov, G. “Regularization Approach for Real-time Modelling of Aero Gas Turbines.” Control Engineering Practice Vol. 12, No. 4: pp. 401-407.
  • [20] Lyantsev, O., Kazantsev, A., and Abdulnagimov, A. “Identification Method for Nonlinear Dynamic Models of Gas Turbine Engines on Acceleration Mode.” Procedia Engineering Vol. 176 (2017): pp. 409-415.
  • [21] Lu, F., Ye, Y., and Huang, J. “Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models using Fast Kernel Extreme Learning Machine.” Energies Vol. 10 (2017): p. 1363, 17.
  • [22] Hulgren, L. “Full-Scale Turbofan-Engine Turbine-Transfer Function Determination using Three Internal Sensors.” Technical Report No. NASA/TM-2012-217252. (2011): p. 13.
  • [23] Yepifanov, S., Kuznetsov, B., Bogayenko, I. Grabovskii, G., Dyukov, V., Kuzmenko, S., Ryumshyn N., Sametskii, A. “Synthesis of Turbine Engine Control and Diagnostics Systems.” Technical Publishing, Kiev, Ukraine (1998): p. 312.
  • [24] Hansen, C., Pereyra, V., and Scherer, G. “Least Squares Data Fitting with Applications.” Johns Hopkins University Press, Baltimore, Maryland, USA (2013): p. 328.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-126f97fb-e935-420e-b495-5b5a3c83d02e
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