A novel combined model for energy consumption performance prediction in the secondary air system of gas turbine engines based on flow resistance network

Gong, Wenbin; Zhao, Libin; Nie, Songlin; Liu, Gaowen; Lin, Aqiang; Qing, Feng; Wang, Zhiwu

doi:10.1016/j.energy.2023.127951

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Despite the complexity of analytical descriptions, mathematical models such as differential equations, polynomial approximations, and those based on fuzzy inference and neural networks are crucial for understanding helicopter TE operations across various conditions. The authors of [26] focused on a universal flow resistance element to predict total energy consumption in engine secondary air systems, showing satisfactory accuracy but limited applicability with changing pressure and temperature. The authors of [27] investigated heat transfer mechanisms and energy conversion in a pre-swirl system to enhance cooling efficiency.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Vladov,

Yakovliev,

Bulakh

et al. 2024

Energies

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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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Section: Introductionmentioning

confidence: 99%