RL-Optimized Nanofluid Microchannel Cooling for High-Performance Computing

This paper proposes a hybrid cooling architecture that combines nanofluid-phase-change medium with reinforcement learning (RL) control to improve cooling efficiency for high-performance computing (HPC) systems.

Details

High-performance computing (HPC) systems generate significant waste heat, exceeding the capabilities of traditional air or liquid cooling methods. This paper explores a hybrid cooling approach that combines nanofluids, phase-change materials, microchannel design, and reinforcement learning (RL) control. Nanofluids, which are colloidal suspensions of nanoparticles in base fluids, can enhance thermal conductivity and specific heat. When combined with a phase-change polymer, the resulting medium displays higher effective latent heat while maintaining fluid-like flow characteristics. RL is used to adaptively control actuators like pumps and fans, adjusting flow rates based on observed temperature profiles and power consumption to achieve higher temperature stability and energy efficiency. The thermodynamic model accounts for heat transfer, phase change, and entropy generation in the microchannel cooling system. Experimental validation and a scalability analysis are presented for HPC cooling applications.