Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

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 propagati...

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Veröffentlicht in:Energies (Basel) Jg. 17; H. 9; S. 2233
Hauptverfasser: Vladov, Serhii, Yakovliev, Ruslan, Bulakh, Maryna, Vysotska, Victoria
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.05.2024
Schlagworte:
Accuracy
Algorithms
Analysis
Approximation
Aviation
efficiency
energy
Energy consumption
Energy efficiency
helicopter turboshaft engines
Helicopters
Mathematical models
neural network approximation
Neural networks
Neurons
Optimization
power
Principal components analysis
Repair & maintenance
training
Turbines
ISSN:1996-1073, 1996-1073
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Zusammenfassung: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en17092233