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Investigation on the flow and thermal properties of fibrous insulation used in thermal batteries at alternative atmosphere and pressure gradient

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  • Xin, Weiwei
  • Liu, Huan-ling
  • Zhao, Jin-feng
  • Shao, Xiao-dong
  • Zhao, Ya-xu

Abstract

The accurate prediction of behavior for insulations within thermal battery can be impeded due to the fact that the thermal properties of insulations have not been thoroughly characterized under the complex conditions in which they operate. In this study, an effective and extendible model is developed based on fractal theory and finite element method to predict the flow and thermal properties for fibrous insulations within thermal batteries. Two common fibrous insulations are tested and verified with this model under the operating gas atmosphere of thermal battery. It is found that the predictive permeability and effective thermal conductivity agree well with the existing experimental data. Verification under various gas atmosphere shows that present model can reduce the relative error of existing model by 5.21–17.2 %. The gas contributed thermal conductivity for insulations filled with high thermal conductivity gas is always significant. In addition, under the tested pressure gradients, the deviations of temperature are generally greater than 1.42 % for air, methane and carbon dioxide atmosphere, but less than 0.7 % for hydrogen atmosphere. The high porosity of material, the large or reverse pressure gradient may strongly promote the influence of the energy migration within insulations.

Suggested Citation

  • Xin, Weiwei & Liu, Huan-ling & Zhao, Jin-feng & Shao, Xiao-dong & Zhao, Ya-xu, 2024. "Investigation on the flow and thermal properties of fibrous insulation used in thermal batteries at alternative atmosphere and pressure gradient," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224018139
    DOI: 10.1016/j.energy.2024.132039
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    References listed on IDEAS

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    1. Wang, Chao & Zhang, Xu & Cui, Yixiu & He, Ke & Cao, Yong & Liu, Xiaojiang & Zeng, Chao, 2022. "A system-level thermal-electrochemical coupled model for evaluating the activation process of thermal batteries," Applied Energy, Elsevier, vol. 328(C).
    2. Yu, Haiyan & Zhang, Haochun & Buahom, Piyapong & Liu, Jing & Xia, Xinlin & Park, Chul B., 2021. "Prediction of thermal conductivity of micro/nano porous dielectric materials: Theoretical model and impact factors," Energy, Elsevier, vol. 233(C).
    3. Zhang, Wencan & Ouyang, Nan & Yin, Xiuxing & Li, Xingyao & Wu, Weixiong & Huang, Liansheng, 2022. "Data-driven early warning strategy for thermal runaway propagation in Lithium-ion battery modules with variable state of charge," Applied Energy, Elsevier, vol. 323(C).
    4. Ouyang, Nan & Zhang, Wencan & Yin, Xiuxing & Li, Xingyao & Xie, Yi & He, Hancheng & Long, Zhuoru, 2023. "A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions," Energy, Elsevier, vol. 273(C).
    5. Li, Qing & Shao, Yu-qiang & Shao, Xiao-dong & Liu, Huan-ling & Xie, Gongnan, 2021. "Activation process modeling and performance analysis of thermal batteries considering ignition time interval of heat pellets," Energy, Elsevier, vol. 219(C).
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