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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

  • Northwestern Polytechnical University Xian
  • Chinese Aeronautical Establishment
  • China Helicopter Research and Development Institute

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish
Article number127951
JournalEnergy
Volume280
DOIs
StatePublished - 1 Oct 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Aerodynamic performance
  • Elements system evaluation
  • Energy consumption
  • Entropy increment
  • Flow resistance characteristics
  • Flow resistance network

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