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Abstract <p>While electronic components like microprocessors, MEMS, and other power electronics are increasingly reducing in size but higher in power density, thermal management is becoming much more important not only for reliability but also for preventing overheating. Though there are many studies conducted on nanofluid-based cooling, very little research has been done so far regarding coupled thermal behavior between nanofluid and a solid wall, especially for thermal conditions in high-power density applications targeted by nanofluid cooling with applications to the next generation of AI/HPC accelerators. This study also incorporates the novel hyperspectral-enhanced dark field microscopy to provide new insights into the nature of nanofluid flow within microchannels. To address this gap, microscale cooling using nanofluids is investigated, with a focus on temperature distribution in both the flow field and solid walls and its dependence on flow conditions and nanoparticle properties. Conjugate Heat Transfer (CHT) numerical simulations, based on the finite volume method, are employed to examine the effects of Reynolds number (Re=100-1000), nanoparticle concentration (ϕ =0.5-5.0vol%), and nanoparticle material (Al₂O₃, CuO, Fe₃O₄) in a wavy-wall microchannel with three flow configurations (parallel, counter, and single-path). The results show that the parallel-flow configuration offers superior thermal performance compared to the counter and single-path designs, achieving the lowest peak wall temperature (e.g., T<sub>max</sub>= 317.44K achieved at optimal conditions), while the single-path design induces the highest thermal gradients. The wavy microchannel walls enhance heat transfer by promoting secondary fluid motion and continual disruption of the thermal boundary layer, which locally maximizes the convective heat transfer coefficient (h) and effectively reduces localized hotspots. Increasing nanoparticle concentration significantly lowers the maximum temperature, however, higher concentrations increase flow resistance, particularly with Fe₃O₄. Among the nanoparticle studied, Al₂O₃ performs most effectively at higher Reynolds numbers, likely due to its superior intrinsic thermal conductivity and stability. Finally, dark field microscopy with hyperspectral enhancement is applied to visualize and investigate the flow patterns of nanofluids, demonstrating direct physical understanding of observations like the aggregation of nanoparticles near the channel walls. Variations in scattering intensity indicates the potential difficulty of keeping particles uniformly dispersed throughout continuous flow, which may affect thermal boundary conditions. These findings provide design guidelines for optimizing nanofluid-cooled microchannels, improving thermal uniformity and reliability in next-generation compact electronic systems.</p>

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Numerical and experimental analysis of nanofluid–solid wall thermal interactions for microscale cooling applications

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Mehdi Esmaeilpour

摘要

Abstract

While electronic components like microprocessors, MEMS, and other power electronics are increasingly reducing in size but higher in power density, thermal management is becoming much more important not only for reliability but also for preventing overheating. Though there are many studies conducted on nanofluid-based cooling, very little research has been done so far regarding coupled thermal behavior between nanofluid and a solid wall, especially for thermal conditions in high-power density applications targeted by nanofluid cooling with applications to the next generation of AI/HPC accelerators. This study also incorporates the novel hyperspectral-enhanced dark field microscopy to provide new insights into the nature of nanofluid flow within microchannels. To address this gap, microscale cooling using nanofluids is investigated, with a focus on temperature distribution in both the flow field and solid walls and its dependence on flow conditions and nanoparticle properties. Conjugate Heat Transfer (CHT) numerical simulations, based on the finite volume method, are employed to examine the effects of Reynolds number (Re=100-1000), nanoparticle concentration (ϕ =0.5-5.0vol%), and nanoparticle material (Al₂O₃, CuO, Fe₃O₄) in a wavy-wall microchannel with three flow configurations (parallel, counter, and single-path). The results show that the parallel-flow configuration offers superior thermal performance compared to the counter and single-path designs, achieving the lowest peak wall temperature (e.g., Tmax= 317.44K achieved at optimal conditions), while the single-path design induces the highest thermal gradients. The wavy microchannel walls enhance heat transfer by promoting secondary fluid motion and continual disruption of the thermal boundary layer, which locally maximizes the convective heat transfer coefficient (h) and effectively reduces localized hotspots. Increasing nanoparticle concentration significantly lowers the maximum temperature, however, higher concentrations increase flow resistance, particularly with Fe₃O₄. Among the nanoparticle studied, Al₂O₃ performs most effectively at higher Reynolds numbers, likely due to its superior intrinsic thermal conductivity and stability. Finally, dark field microscopy with hyperspectral enhancement is applied to visualize and investigate the flow patterns of nanofluids, demonstrating direct physical understanding of observations like the aggregation of nanoparticles near the channel walls. Variations in scattering intensity indicates the potential difficulty of keeping particles uniformly dispersed throughout continuous flow, which may affect thermal boundary conditions. These findings provide design guidelines for optimizing nanofluid-cooled microchannels, improving thermal uniformity and reliability in next-generation compact electronic systems.