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

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  • Gong, Wenbin
  • Lei, Zhao
  • Nie, Shunpeng
  • Liu, Gaowen
  • Lin, Aqiang
  • Feng, Qing
  • Wang, Zhiwu

Abstract

Predicting the comprehensive energy consumption is challenging for the complicated secondary air system. To solve this problem, a universal flow resistance element has been constructed to describe different components more accurately. And a novel flow resistance network is proposed to calculate the system's energy consumption. This network can be analyzed by the flow resistance characteristics of its components to estimate the energy consumption of each component and the energy loss of the system. Furthermore, a flow resistance parameter is defined as a unique factor to determine the magnitude of flow resistance for each component. Compared to experimental and numerical results, both models demonstrate sufficient accuracy in calculating mass flow rates and entropy increments, with the maximum deviation less than 1.2%. The above two models are also applied to predict flow losses of elements with changes in pressure and inlet total temperature, with the maximum deviation less than 4.5%. Based on the flow resistance network, the aerodynamic performance of each element can be easily computed by the inlet and outlet boundary of this system, regardless of whether the elements are arranged in series or parallel.

Suggested Citation

  • Gong, Wenbin & Lei, Zhao & Nie, Shunpeng & Liu, Gaowen & Lin, Aqiang & Feng, Qing & Wang, Zhiwu, 2023. "A novel combined model for energy consumption performance prediction in the secondary air system of gas turbine engines based on flow resistance network," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223013452
    DOI: 10.1016/j.energy.2023.127951
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    References listed on IDEAS

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    Cited by:

    1. Serhii Vladov & Ruslan Yakovliev & Maryna Bulakh & Victoria Vysotska, 2024. "Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency," Energies, MDPI, vol. 17(9), pages 1-28, May.
    2. Ma, Jiale & Liu, Gaowen & Yao, Guanwei & Li, Jinze & Gong, Wenbin & Lin, Aqiang, 2024. "Investigations of a turbine pre-swirl system with high temperature drop efficiency through the design of a novel vane-shaped receiver hole," Energy, Elsevier, vol. 301(C).

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