IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v323y2022ics0306261922009217.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922009217
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119617?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    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. 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).
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang, Tao & Zhao, Qing & Lv, Qingzhao & Sun, Hexu, 2021. "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, Elsevier, vol. 230(C).
    2. Ilias Apostolopoulos & Georgios Bampos & Amaia Soto Beobide & Stefanos Dailianis & George Voyiatzis & Symeon Bebelis & Gerasimos Lyberatos & Georgia Antonopoulou, 2021. "The Effect of Anode Material on the Performance of a Hydrogen Producing Microbial Electrolysis Cell, Operating with Synthetic and Real Wastewaters," Energies, MDPI, vol. 14(24), pages 1-20, December.
    3. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    4. Muhammad, Yasir & Khan, Nusrat & Awan, Saeed Ehsan & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Kiani, Adiqa Kausar & Ullah, Farman & Shu, Chi-Min, 2022. "Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    5. Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).
    6. Pan, Xiaoxin & Wang, Long & Wang, Zhongju & Huang, Chao, 2022. "Short-term wind speed forecasting based on spatial-temporal graph transformer networks," Energy, Elsevier, vol. 253(C).
    7. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    8. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    9. Liaw, Kim Leong & Ong, Khai Chuin & Mohd Ali Zar, Muhammad Aliff B. & Lai, Wen Kang & Muhammad, M. Fadhli B. & Firmansyah, & Kurnia, Jundika C., 2023. "Experimental and numerical investigation of an innovative non-combustion impulse gas turbine for micro-scale electricity generation," Energy, Elsevier, vol. 266(C).
    10. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    11. Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Abdullah G. Alharbi & Sulaiman Alshammari & Yahia B. Hassan, 2023. "Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    12. Tian, Zhirui & Wang, Jiyang, 2022. "Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm," Energy, Elsevier, vol. 254(PA).
    13. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    15. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
    16. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
    17. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    18. Liaw, Kim Leong & Kurnia, Jundika C. & Lai, Wen Kang & Ong, Khai Chuin & Zar, Muhammad Aliff B. Mohd Ali & Muhammad, M. Fadhli B. & Firmansyah,, 2023. "Optimization of a novel impulse gas turbine nozzle and blades design utilizing Taguchi method for micro-scale power generation," Energy, Elsevier, vol. 282(C).
    19. Gao, Yuyang & Wang, Jianzhou & Yang, Hufang, 2022. "A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting," Renewable Energy, Elsevier, vol. 188(C), pages 384-401.
    20. Korberg, A.D. & Brynolf, S. & Grahn, M. & Skov, I.R., 2021. "Techno-economic assessment of advanced fuels and propulsion systems in future fossil-free ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009217. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.