Air-water hybrid cooling for computer servers: A case study for optimum cooling energy allocation

Air-water hybrid cooling offers flexible design choices for computer systems with components of different thermal management needs. On one hand, water cooling enables the continuous growth of CPU performance and increasing packaging density. High performance cold plates such as microchannels have been successfully implemented for water cooling in previous high-end systems. When coupled with an air-water heat exchanger or radiator, the water loop becomes a closed one with no need for facility chilled water. This significantly reduces the complexity to deploy the server in the data center. On the other hand, for components with less thermal demand, traditional air-cooling technology is adequate with low cost, high availability and better serviceability. For the computer system as a whole, an air-water hybrid cooling system may be optimized. Such a hybrid system typically requires pumps to drive the water loops, air-movers to drive air through the radiator and blowers or fans to drive the air flow for component cooling. It is the focus of this paper to study the optimum allocation of energy between the pumps and air-movers for a given total cooling energy budget and overall load. The goals are to achieve better overall thermal performance and to reduce the cooling energy consumption. To this end models for each cooling block are established based on test data. These include the air-water heat exchanger, pumps, blowers, and cold plates. These models are linked together to predict the overall thermal system operating points for different application scenarios. A parametric study is then conducted to define the near optimum allocation of cooling energy for these scenarios that meets the thermal design objectives. Additionally, sub-threshold leakage for the CPU is taken into account to enhance the model since temperature provides positive feedback. It is shown through modeling that additional performance enhancement is possible with judicious allocation of cooling energy for a given overall energy budget. It is argued in this paper that overall energy efficiency can be improved significantly through intelligent data driven energy allocation.