Real-time optimization of the liquid-cooled data center based on cold plates under different ambient temperatures and thermal loads

Abstract

With the advent of the information age, the scale of data centers has developed unprecedentedly, in which the cooling system consumes a lot of energy. Therefore, it is crucial to adjust the operating parameters in real-time to maximize energy savings. In this article, the mathematical models of each component are established, and the functions of system power consumption and chip temperatures are obtained for the liquid-cooled data center based on cold plates. The method proposed in this article has good accuracy through experimental verification and can be used for the optimization of operating parameters. The server chips in data centers need to be maintained within a safe range, so we take the minimum system power consumption as the optimization goal and the chip temperature as the constraint condition to calculate the optimal cooling tower wind volume, primary side flow rate, and secondary side flow rate under different environmental temperatures and heat loads, and fit all three for real-time optimization and regulation. The results show that the intelligent control proposed in this article can save 42.7% energy and reduce PUE to 1.16 under variable heat load, and save 30.6% energy and improve PUE by 4.3% under variable environmental temperature. The intelligent control method described in this article provides guidance for real-time optimization in data centers.