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Numerical simulation of gas composition tracking in a gas transportation network

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  • Bermúdez, Alfredo
  • Shabani, Mohsen

Abstract

In previous papers of the authors, a new formulation for isothermal flow of gas mixtures with given constant composition in a transportation network including usual devices has been introduced and solved. However, in real networks, different gas qualities are introduced from different entry points. In this case, it is important to track the quality along the network over time and therefore a multi-species model has to be used. The main objective of the present paper is to introduce a model allowing us to simulate the evolution of the gas composition, at each point in the network and over time, and then to couple it with the flow model. The model for tracking the gas composition consists of a system of first order partial differential equations, one per pipe and per species, which are coupled together at the nodes by imposing the mass conservation equation for each species. It is important to notice that the coupling condition at the nodes guarantees that the numerical scheme conserves the mass of each species along the time. In order to validate the overall methodology, it is applied to a test case on a real network. Numerical results show good agreement with measurements.

Suggested Citation

  • Bermúdez, Alfredo & Shabani, Mohsen, 2022. "Numerical simulation of gas composition tracking in a gas transportation network," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003620
    DOI: 10.1016/j.energy.2022.123459
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    References listed on IDEAS

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    Cited by:

    1. Koo, Bonchan & Ha, Youngcheol & Kwon, Hweeung, 2023. "Preliminary evaluation of hydrogen blending into high-pressure natural gas pipelines through hydraulic analysis," Energy, Elsevier, vol. 268(C).
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    3. 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).

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