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Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis

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  • Li, Xia
  • Zhao, Tian
  • Sun, Qing-Han
  • Chen, Qun

Abstract

Timely diagnosing leakages is significant for the safety and reliability of gas pipeline systems, where an efficient and accurate modeling and solution method is critical to identify the leakage rate and location. Here, a frequency response function-based modeling method for dynamic gas flow in pipelines is proposed. Based on the frequency-domain flow resistance of pipelines and Fourier Transform, original dynamic flow model is transformed into the frequency response function model consisting of linear lumped transmission constraints, nonlinear frequency response function, and nonlinear pipeline resistance characteristics, which are efficiently solved according to their different mathematical properties. Dynamic simulation cases show the proposed modeling and solution method greatly improves the computational efficiency keeping the accuracy. Besides, taking the difference between measurements and simulation results under leakage condition as objective, an iterative bilayer optimization algorithm is proposed based on the mathematical categorization of system constraints to efficiently diagnose leakages. Four leakage diagnosis cases in single pipeline and pipeline network with measurement noise and transient boundary conditions are studied to validate the leakage diagnosis method. Absolute location errors in the results are all within 1%, demonstrating the accuracy and robustness of the proposed method for single leakages and multiple leakages occurring successively or simultaneously.

Suggested Citation

  • Li, Xia & Zhao, Tian & Sun, Qing-Han & Chen, Qun, 2022. "Frequency response function method for dynamic gas flow modeling and its application in pipeline system leakage diagnosis," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s030626192201011x
    DOI: 10.1016/j.apenergy.2022.119720
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Tian & Li, Hang & Li, Xia & Sun, Qing-Han & Fang, Xuan-Yi & Ma, Huan & Chen, Qun, 2024. "A frequency domain dynamic simulation method for heat exchangers and thermal systems," Energy, Elsevier, vol. 286(C).
    2. Xu, Ziqiang & Li, Cheng & Mu, Lianbo & Wang, Suilin & Lu, Junhui & Lan, Yuncheng, 2024. "Leakage detection method of underground heating pipeline based on improved wavelet threshold function," Energy, Elsevier, vol. 295(C).
    3. Gryboś, Dominik & Młynarczyk, Dorota & Leszczyński, Jacek & Wiciak, Jerzy, 2024. "Mitigation of noise pollution in compressed air installations through the use of an air collection system in the expansion process," Applied Energy, Elsevier, vol. 364(C).
    4. Zhao, Tian & Sun, Qing-Han & Li, Xia & Xin, Yong-Lin & Chen, Qun, 2023. "A novel transfer matrix-based method for steady-state modeling and analysis of thermal systems," Energy, Elsevier, vol. 281(C).
    5. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Yao, Lizhong & Zhang, Yu & He, Tiantian & Luo, Haijun, 2023. "Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction," Applied Energy, Elsevier, vol. 352(C).

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