Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Serhii Vladov, Ruslan Yakovliev, Maryna Bulakh () and Victoria Vysotska
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Serhii Vladov: Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine
Ruslan Yakovliev: Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine
Maryna Bulakh: Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland
Victoria Vysotska: Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine

Energies, 2024, vol. 17, issue 9, 1-28

Abstract: The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines’ optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.

Keywords: neural network approximation; helicopter turboshaft engines; energy; power; efficiency; training; gas-generator rotor r.p.m.; scaled conjugate gradient algorithm (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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