Enhancement of effective thermal conductivity of rGO/Mg nanocomposite packed beds

•Effective thermal conductivity of rGO/Mg nanocomposites packed beds were measured.•Non-monotonic behavior of effective thermal conductivity with varying rGO content.•Mesoscale modeling was performed with digital-twin framework of nanocomposites.•Interplay between rGO layers and Mg nanoparticles in...

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Veröffentlicht in:International journal of heat and mass transfer 2022-08, Vol.192, p.122891, Article 122891
Hauptverfasser: Kim, Dong-min, Han, Dong Ju, Heo, Tae Wook, Kang, ShinYoung, Wood, Brandon C., Lee, Jungchul, Cho, Eun Seon, Lee, Bong Jae
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Sprache:eng
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Zusammenfassung:•Effective thermal conductivity of rGO/Mg nanocomposites packed beds were measured.•Non-monotonic behavior of effective thermal conductivity with varying rGO content.•Mesoscale modeling was performed with digital-twin framework of nanocomposites.•Interplay between rGO layers and Mg nanoparticles in nanocomposite were reproduced. Engineering thermophysical properties of metal hydrides nanocomposites is crucial for effective thermal management during hydrogen storage reactions; however, the effect of microstructure on thermal transport mechanisms is still unclear. Here, we employed an integrated experiment-modeling approach to investigate microstructural factors that determine the effective thermal conductivity of individual reduced graphene oxide-magnesium (rGO/Mg) nanocomposites and their packed bed. The effective thermal conductivity of the rGO/Mg nanocomposite packed bed was measured by using guarded hot-plate method under various atmospheric conditions (i.e., vacuum, Ar and He). A microstructure-aware mesoscopic modeling revealed that anisotropy of the effective thermal conductivity of individual rGO/Mg nanocomposites plays an important role in determining the effective thermal conductivity of their packed bed. The validated mesoscopic model also disclosed a nontrivial interplay between the intrinsic rGO properties and the extrinsic composite structural features. Finally, quantitative sensitivity analysis based on the modeling framework is used to provide practical engineering guidance for controlling the thermal transport within nanocomposite packed beds.
ISSN:0017-9310
1879-2189
DOI:10.1016/j.ijheatmasstransfer.2022.122891