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A transient composition tracking method for natural gas pipe networks

Author

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  • Fan, Di
  • Gong, Jing
  • Zhang, Shengnan
  • Shi, Guoyun
  • Kang, Qi
  • Xiao, Yaqi
  • Wu, Changchun

Abstract

A transient composition tracking method is developed for natural gas pipe networks. It adopts the control volume method for hydraulic simulation and a one-dimensional unsteady heat transfer model for thermal simulation. In order to model the junction in the network, a special grid is proposed to connect all adjacent pipes together. Under this grid, the momentum equation could be discretized by the first-order upwind scheme while the mass equation could be discretized just like an ordinary pipe node. To track the composition, the advective concentration equation is given for each component. Therefore, the gas composition in the pipe network could be predicted in real time and its influence on the calculation of property parameters could be taken into consideration. Based on these models, a transient composition tracking algorithm is developed for natural gas pipe networks. It consists of three parts: transient flow simulation, composition tracking and parameters calculation. Case studies show that the proposed method can provide reliable results for transient flow simulation. Compared with the measured data, the composition outcomes given by this method are also very accurate. In addition, the impact of injecting LNG/H2 into the pipe network is quite complicated and should be treated with caution.

Suggested Citation

  • Fan, Di & Gong, Jing & Zhang, Shengnan & Shi, Guoyun & Kang, Qi & Xiao, Yaqi & Wu, Changchun, 2021. "A transient composition tracking method for natural gas pipe networks," Energy, Elsevier, vol. 215(PA).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220322386
    DOI: 10.1016/j.energy.2020.119131
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    5. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    6. Chen, Qian & Guan, Aocheng & Chen, Feng & Huang, Wei & Jin, Antai & Gadalla, Mamdouh & Wang, Bohong, 2024. "A transient gas pipeline network simulation model for decoupling the hydraulic-thermal process and the component tracking process," Energy, Elsevier, vol. 301(C).
    7. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
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    10. Gong, Junhua & Shi, Guoyun & Wang, Shaobo & Wang, Peng & Chen, Bin & Chen, Yujie & Wang, Bohong & Yu, Bo & Jiang, Weixin & Li, Zongze, 2024. "Efficient super-resolution of pipeline transient process modeling using the Fourier Neural Operator," Energy, Elsevier, vol. 302(C).
    11. Dancker, Jonte & Wolter, Martin, 2022. "A coupled transient gas flow calculation with a simultaneous calorific-value-gradient improved hydrogen tracking," Applied Energy, Elsevier, vol. 316(C).
    12. Vadim Fetisov & Aleksey V. Shalygin & Svetlana A. Modestova & Vladimir K. Tyan & Changjin Shao, 2022. "Development of a Numerical Method for Calculating a Gas Supply System during a Period of Change in Thermal Loads," Energies, MDPI, vol. 16(1), pages 1-16, December.
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