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

Serhii Vladov, Maryna Bulakh (), Jan Czyżewski, Oleksii Lytvynov, Victoria Vysotska and Victor Vasylenko
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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
Victor Vasylenko: Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

Energies, 2024, vol. 17, issue 22, 1-23

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.

Keywords: helicopter turboshaft engine; engine power; free turbine rotor speed; fuel consumption; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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