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Experimental study and prediction on the thermal management performance of SDS aqueous solution based microchannel flow boiling system

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  • Rui, Ziliang
  • Sun, Hong
  • Ma, Jie
  • Peng, Hao

Abstract

This study reports the flow boiling of SDS aqueous solution in straight and tree-shaped microchannel for thermal management. Under different volume flow rate and heat flux, the effect of SDS on the flow pattern, heat transfer and pressure drop in different channel structures is clarified. The results reflect that unique flow patterns like bubble stacking, bubble cluster and activated bubbles are observed in the flow boiling of SDS aqueous solution. 400 mg/kg and 200 mg/kg SDS aqueous solution has the best overall heat transfer enhancement performance in straight (25% improvement) and tree-shaped (22% improvement) microchannels respectively. The heat transfer enhancing mechanism of SDS can be summarized as suppressing the formation of large volume vapor slug and promote the rewetting of the bottom channel surface. Based on this mechanism, SDS can also advance the ONB and extend the stable nucleate boiling state, 100 mg/kg, and 400 mg/kg SDS aqueous solution show longest stable nucleate boiling state in straight and tree-shaped microchannel respectively. Increasing solute concentration of SDS leads to larger pressure drop and stronger pressure drop fluctuation in both microchannels, but increasing volume flow rate will suppress this extra resistance and instability caused by SDS. Targeted heat transfer and pressure drop correlations are also presented.

Suggested Citation

  • Rui, Ziliang & Sun, Hong & Ma, Jie & Peng, Hao, 2023. "Experimental study and prediction on the thermal management performance of SDS aqueous solution based microchannel flow boiling system," Energy, Elsevier, vol. 282(C).
  • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223021412
    DOI: 10.1016/j.energy.2023.128747
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    References listed on IDEAS

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    1. Zohora, Fatema-Tuj & Akter, Farzana & Haque, Md. Araful & Chowdhury, Nabil Mohammad & Haque, Mohammad Rejaul, 2024. "A novel pin finned structure-embedded microchannel heat sink: CFD-data driven MLP, MLR, and XGBR machine learning models for thermal and fluid flow prediction," Energy, Elsevier, vol. 307(C).

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