Method of Helicopter Turboshaft Engines’ Protection During Surge in Starting Mode

Denys Baranovskyi (), Serhii Vladov, Maryna Bulakh, Victoria Vysotska, Viktor Vasylenko and Jan Czyżewski
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Denys Baranovskyi: Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland
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
Victoria Vysotska: Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine
Viktor Vasylenko: Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine
Jan Czyżewski: Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland

Energies, 2025, vol. 18, issue 1, 1-24

Abstract: This article proposes a mathematical model for protecting helicopter turboshaft engines from surges, starting with fuel metering supply and maintaining stable compressor operation. The model includes several stages: first, fuel is supplied according to a specified program; second, an unstable compressor operation signal is determined based on the gas temperature in front of the compressor turbine and the gas generator rotor speed derivatives ratio; at the third stage, when the ratios’ threshold value is exceeded, fuel supply is stopped, and the ignition system is turned on. Then, the fuel supply is restored with reduced consumption, and the rotor speed is corrected, followed by a return to regular operation. The neural network model implementing this method consists of several layers, including derivatives calculation, comparison with the threshold, and correction of fuel consumption and rotor speed. The input data for the neural network are the gas temperature in front of the compressor turbine and the rotor speed. A compressor instability signal is generated if the temperature and rotor speed derivatives ratio exceed the threshold value, which leads to fuel consumption adjustment and rotor speed regulation by 28…32%. The backpropagation algorithm with hyperparameter optimization via Bayesian optimization was used to train the network. The computational experiments result with the TV3-117 turboshaft engine on a semi-naturalistic simulation stand showed that the proposed model effectively prevents compressor surge by stabilizing pressure, vibration, and gas temperature and reduces rotor speed by 29.7% under start-up conditions. Neural network quality metrics such as accuracy (0.995), precision (0.989), recall (1.0), and F 1-score (0.995) indicate high efficiency of the proposed method.

Keywords: helicopter turboshaft engines; surge; gas temperature in front of the compressor turbine; gas generator rotor speed; compressor operation; 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: 2025
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