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Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks

Author

Listed:
  • Serhii Vladov

    (Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine)

  • Maryna Bulakh

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland)

  • Jan Czyżewski

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland)

  • Oleksii Lytvynov

    (National Aerospace University “Kharkiv Aviation Institute”, 17, Chkalova Street, 61070 Kharkiv, Ukraine)

  • Victoria Vysotska

    (Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine
    Institute of Computer Science, Osnabrück University, 1, Friedrich-Janssen-Street, 49076 Osnabrück, Germany)

  • Victor Vasylenko

    (Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine)

Abstract

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating the free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links the main rotor and free turbine rotor speed parameters, based on which a relation with the engine output power was established. In this research, a differential equation was obtained that links fuel consumption, output power, and rotor speed, which makes it possible to monitor engine dynamics in various operating modes. A fuel consumption controller was developed based on a neuro-fuzzy network that processes input data, including the desired and current rotor speed, which allows real-time adjustments to improve the operational efficiency. In the research, based on the flight data analysis obtained during the Mi-8MTV helicopter with a TV3-117 turboshaft engine flight test, improved signal processing quality was obtained due to time sampling and adaptive quantisation methods (this is confirmed by assessing the homogeneity and representativeness of the training and test datasets). A comparative analysis of the developed and traditional controllers showed that the neuro-fuzzy network use reduces the transient fuel consumption process time by 8.92% while increasing the accuracy and F1 score by 18.28% and 21.32%, respectively.

Suggested Citation

  • Serhii Vladov & Maryna Bulakh & Jan Czyżewski & Oleksii Lytvynov & Victoria Vysotska & Victor Vasylenko, 2024. "Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks," Energies, MDPI, vol. 17(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5755-:d:1523302
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    References listed on IDEAS

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    1. Abdalla, Muftah S.M. & Balli, Ozgur & Adali, Osama H. & Korba, Peter & Kale, Utku, 2023. "Thermodynamic, sustainability, environmental and damage cost analyses of jet fuel starter gas turbine engine," Energy, Elsevier, vol. 267(C).
    2. Balli, Ozgur, 2023. "Exergetic, sustainability and environmental assessments of a turboshaft engine used on helicopter," Energy, Elsevier, vol. 276(C).
    3. Shuo Zhang & Aotian Ma & Teng Zhang & Ning Ge & Xing Huang, 2024. "A Performance Simulation Methodology for a Whole Turboshaft Engine Based on Throughflow Modelling," Energies, MDPI, vol. 17(2), pages 1-20, January.
    4. Huang, Yu & Turan, Ali, 2022. "Flexible power generation based on solid oxide fuel cell and twin-shaft free turbine engine: Mechanical equilibrium running and design analysis," Applied Energy, Elsevier, vol. 315(C).
    5. Feng, Hailong & Liu, Bei & Xu, Maojun & Li, Ming & Song, Zhiping, 2024. "Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient," Energy, Elsevier, vol. 292(C).
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