Energy efficiency optimization of an integrated heat pipe cooling system in data center based on genetic algorithm

The energy consumption of data centers has become an increasing concern in the whole world. Actually, the cooling system consumes the most energy among the auxiliary facilities. An integrated heat pipe cooling system has been proposed to reduce the energy consumption of cooling system by using natural cooling sources in our previous work. However, it still has huge energy-saving potential in the operating process. In this paper, the heat transfer model and energy consumption model of the integrated heat pipe cooling system was established to describe the relationship between operating parameters and energy efficiency. Based on genetic algorithm, energy efficiency optimization operation strategy was calculated at different load rates and outdoor ambient temperature. An experiment was carried out to verify the accuracy of the optimization algorithm. Compared with the previous work, the energy efficiency ratio of the integrated heat pipe cooling system can be improved by 2-3 times in natural cooling model and integrated cooling mode after energy efficiency optimization. The switching rules of the system's operating mode was studied, and analyzed the energy-saving effects of application in different climate cities.

Automated summary

This paper studied the optimization of the integrated heat pipe cooling system in terms of energy consumption, establishing heat transfer and energy consumption models as well as calculating energy efficiency optimization strategies using genetic algorithms. The energy efficiency of the system was significantly improved through the optimization process, demonstrating the potential for energy savings in data center cooling systems.