Learning Predictive Models from Large Distributed Autonomous Data Sources

Summary form only given. Recent investigations were carried of saturation and subcooled nucleate boiling of FC-72 and HFE-7100 dielectric liquids on porous graphite (PG) for potential applications to immersion cooling of high power computer chips. This talk will present the results of these investigations and those of the effect of surface inclination, from upward-facing to downward-facing, on the nucleate boiling heat transfer coefficient, and the critical heat flux. Natural convection results will also be presented of dielectric liquids on PG, important for cooling the chips in the standby mode when the surface heat flux is < 20 kW/m2. In addition, the results of CFD calculations comparing the performance of copper (Cu), PG and composite spreaders for removing the dissipated thermal power by computer chips will presented removing up to 1MW/m2 of dissipated heat by a 10 × 10 mm underlying chip.