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Numerical investigation on the leakage and diffusion characteristics of hydrogen-blended natural gas in a domestic kitchen

Author

Listed:
  • Su, Yue
  • Li, Jingfa
  • Yu, Bo
  • Zhao, Yanlin

Abstract

Hydrogen blending has significant influences on the characteristics and evolution of safety accidents of natural gas. In this study, the numerical investigation is performed to reveal the leakage and diffusion characteristics of hydrogen-blended natural gas in a real domestic kitchen by using the ANSYS Fluent software. The physical and mathematical model of gas leak from cooking appliances is established, and the explosion risk area, alarm response time and concentration distribution of leaked gas are studied. Influences of hydrogen blending ratio (HBR), leak rate, ventilation condition and dimension size on the characteristics and evolution of leakage and diffusion are analyzed. Results show that with the increase of HBR at constant leak rates, both the alarm time and the time to reach lower explosion limit are advanced. When the volume leak rate is constant, the alarm response time for HBR of 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5% and 20% is 0.50%, 1.51%, 3.02%, 3.78%, 6.53%, 10.34%, 12.34% and 14.11% earlier than that of natural gas. The natural ventilation can effectively reduce leaked gas concentration in kitchen. At the wind speed of 1 m/s, the leaked gas concentration falls below alarm concentration within 28s, but the free diffusion takes as long as 720s.

Suggested Citation

  • Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin, 2022. "Numerical investigation on the leakage and diffusion characteristics of hydrogen-blended natural gas in a domestic kitchen," Renewable Energy, Elsevier, vol. 189(C), pages 899-916.
    • Handle: RePEc:eee:renene:v:189:y:2022:i:c:p:899-916
    DOI: 10.1016/j.renene.2022.03.038
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    References listed on IDEAS

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    1. Gerboni, R. & Salvador, E., 2009. "Hydrogen transportation systems: Elements of risk analysis," Energy, Elsevier, vol. 34(12), pages 2223-2229.
    2. Ishaq, H. & Dincer, I., 2021. "Comparative assessment of renewable energy-based hydrogen production methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
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    Cited by:

    1. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    2. Yue Su & Jingfa Li & Wangyi Guo & Yanlin Zhao & Jianli Li & Jie Zhao & Yusheng Wang, 2022. "Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model," Energies, MDPI, vol. 15(22), pages 1-19, November.
    3. Lyu, Shan & Huang, Xiaomei & Peng, Shini & Sun, Mengxiao & Qi, Qi & Aimaieraili, Dulikunjiang, 2024. "A novel method for analyzing the leakage and diffusion of hydrogen: First arrival time distribution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).

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