Direct liquid cooling enables the development of IC, especially processors in servers, toward higher density, which demands more efficient means of heat removal. To exploit the capability, the control system that dynamically balances between cooling power and energy consumption is critical to operational safety and cost. This article presents the design, implementation, and characteristic of a dynamic control system for server processor direct liquid cooling in a high-density data center. The controller was trained and deployed in an online-offline structure. The loss function to train this model was built based on the model predictive control (MPC) principle. To simplify the complex physical model, a hybrid model with a nonlinear steady-state term and a linearized transient term was extracted. We used both averaged overheat and ratio of pumping power consumption to evaluate the performance of controllers. Benchmark tests on open- and closed-loop controllers were conducted. The results showed the advantages of the deep explicit MPC (DEMPC) in terms of cooling effectiveness and energy efficiency. The normalized pumping energy consumption of DEMPC reached 2.3 × 10 -4with the mean overheat level of -0.96 °C and the overheat time percentage of 6.5%. Comparing with open-loop control that has similar normalized energy costs, the mean overheat level was reduced by 1.2 °C and the time percentage of overheat was reduced by eight times. Meanwhile, normalized energy consumption was reduced by 21.7% than closed-loop proportional control who has a similar average overheat level and overheat time percentage.
This project is supported by the National Research
Foundation, Prime Minister's Office, Singapore under its Green
Data Centre Research Programme (NRF2015ENCGDCRO1001-032)