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Diffusion characteristics of liquid hydrogen spills in a crossflow field: Prediction model and experiment

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  • Shu, Zhiyong
  • Liang, Wenqing
  • Liu, Fan
  • Lei, Gang
  • Zheng, Xiaohong
  • Qian, Hua

Abstract

Liquid hydrogen (LH2) is currently one of the best ways to store hydrogen energy. A buoyant yet model in a crossflow field was developed to predict LH2 spilling in this work, where the source of LH2 was modeled as a jet and placed near the ground. The prediction model was validated by the experiments that simulated the diffusion characteristics of LH2 with different crossflow velocity by using liquid helium (LHe) in an environment cabin. Results show that predicted centerline trajectories agreed well with concentration cloud map measured by the experiment, where the average deviation is 9.83%. Distinct differences between vapor clouds and concentration clouds were found in experimental results and theoretical predictions, i.e., the concentration cloud is higher than the infrared cloud and vapor cloud. The increasing velocity of the crossflow field inhibits the rise of the LH2 diffusion cloud, thus resulting in a lower central trajectory of the flammable cloud and increasing the danger threshold of LH2 spilling. The velocity of the cloud cluster centerline shows a trend of increasing and then decreasing with the crosswind velocity. The results of the study are expected to give guidelines on the safety threshold and standards for LH2 protection.

Suggested Citation

  • Shu, Zhiyong & Liang, Wenqing & Liu, Fan & Lei, Gang & Zheng, Xiaohong & Qian, Hua, 2022. "Diffusion characteristics of liquid hydrogen spills in a crossflow field: Prediction model and experiment," Applied Energy, Elsevier, vol. 323(C).
  • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009217
    DOI: 10.1016/j.apenergy.2022.119617
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    References listed on IDEAS

    as
    1. Shu, Zhiyong & Liang, Wenqing & Zheng, Xiaohong & Lei, Gang & Cao, Peng & Dai, Wenxiao & Qian, Hua, 2021. "Dispersion characteristics of hydrogen leakage: Comparing the prediction model with the experiment," Energy, Elsevier, vol. 236(C).
    2. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    3. Yu, Xiao & Sandhu, Navjot S. & Yang, Zhenyi & Zheng, Ming, 2020. "Suitability of energy sources for automotive application – A review," Applied Energy, Elsevier, vol. 271(C).
    4. Rousseau, Raphaël & Etcheverry, Luc & Roubaud, Emma & Basséguy, Régine & Délia, Marie-Line & Bergel, Alain, 2020. "Microbial electrolysis cell (MEC): Strengths, weaknesses and research needs from electrochemical engineering standpoint," Applied Energy, Elsevier, vol. 257(C).
    5. Hsu, A. T. & He, G. -B., 2000. "Probability-density function model of turbulent hydrogen flames," Applied Energy, Elsevier, vol. 67(1-2), pages 117-135, September.
    6. Lars H. Odsæter & Hans L. Skarsvåg & Eskil Aursand & Federico Ustolin & Gunhild A. Reigstad & Nicola Paltrinieri, 2021. "Liquid Hydrogen Spills on Water—Risk and Consequences of Rapid Phase Transition," Energies, MDPI, vol. 14(16), pages 1-15, August.
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