Experimental and mesoscopic investigation of nanofluid heat transfer in CPU heat pipe

Nanofluids are increasingly replacing conventional cooling media, such as deionized water, in CPU heat pipe cooling systems due to their superior thermophysical properties. However, screening and optimizing nanofluids for heat pipes remain highly complex and critical, and experimental and macroscopic simulations alone cannot fully reveal the underlying nanofluid heat transfer mechanisms. This study experimentally optimized nanofluid formulations and innovatively constructed a mesoscopic model of the porous wick structures in heat pipes based on Nano-CT reconstruction, providing in-depth insights into nanofluid heat transfer within porous wicks. Experiments evaluated the dispersion stability, thermal performance, and long-term stability of single-component Al₂O₃ and MgO nanofluids, Al₂O₃-MgO hybrid nanofluids, and surfactant-enhanced hybrid nanofluids, revealing the effects of nanoparticle concentration as well as surfactant type and concentration on heat pipe performance. The optimized SDS-enhanced Al₂O₃-MgO hybrid nanofluid exhibited a 36.55% reduction in thermal resistance compared with deionized water and a 33.51% reduction compared with the Al₂O₃ nanofluid, while maintaining stable performance over 100 days. Mesoscopic simulations further indicated that the optimal nanofluid achieved a 35.81% increase in heat transfer coefficient and a 16.25% enhancement in inlet-to-outlet temperature difference, effectively balancing low flow resistance with an adequate heat transfer coefficient. This study establishes design principles for heat pipe working fluids, providing theoretical guidance for the optimized matching of nanofluid formulations and porous wick structures in heat pipes and other nanofluid-enabled electronic cooling systems.