Search

In order to obtain the evaluation and calculation method of indirect evaporative free cooling potential in data center, a new method for evaluating the annual indirect evaporative free cooling potential of data center was proposed based on the division of meteorological parameters of evaporative cooling air conditioning and the calculation method of free cooling potential. Based on the hours when there is a temperature difference between the ambient temperature and the return air (return water) temperature of the data center, the judgment limit of heat exchange efficiency of indirect evaporative cooling equipment and the supply air temperature limit of the data center were added. Combined with the efficiency and air supply limit provided by the standard, a new method was adopted to count the annual operation time distribution of indirect evaporation natural system in data centers of 31 major cities in China. The results show that the potential of indirect evaporative free cooling in the data center is related to the ambient air meteorological parameters, the heat exchange efficiency of indirect evaporative cooling equipment, and the required air supply temperature limit in the data center. Air side and water side indirect evaporative free cooling air conditioning system has great applicability in China's data center. [ABSTRACT FROM AUTHOR]

Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems. [ABSTRACT FROM AUTHOR]

• The two sets of switch conditions and three working modes for the unit applied to data center was proposed. • The energy-saving ratio of the unit in the actual data center for the whole year in China was simulated by DeST. • The energy consumption characteristics and its influencing factors of the unit were investigated. To reduce the power consumption of the cooling system in the data center effectively, a prototype of the integrated cooling unit with rotary booster and compressor was developed, and the operating characteristics were tested comprehensively according to its cooling requirements throughout the year. A data center model reflecting the characteristics of energy consumption and environmental requirements was established, the energy consumption characteristics and influencing factors of the model data center when the integrated unit was used to meet its yearly cooling requirements in the typical climate zones in China were simulated by DeST software based on the measured characteristics of the integrated cooling unit. The results showed that compared with the traditional computer room air conditioner, the energy-saving ratio of the integrated system can be as high as 39.55% for the whole year, and the energy-saving effect is remarkable. The energy-saving potential of the integrated unit becomes more significant while the heat transfer coefficient of the envelope structure becomes larger, and the load rate of IT equipment and the annually-averaged ambient temperature lower. [ABSTRACT FROM AUTHOR]

It is important to cool data centers for improving IT equipment reliability through indoor temperature control. The air conditioning energy consumption is almost 38% of the total energy consumption. Free cooling in cool days is a promising technology for energy conservation. A pump-driven loop heat pipe (PLHP) system for free cooling was developed and installed in a data center project, which has a high energy efficiency ratio (EER) compared to air conditioners. The running performance of the PLHP system in the case project was tested and studied. The fitting rule of EER and outdoor temperature was obtained. The hourly energy savings and payback periods of the PLHP system for different cities of different climate regions in China were analyzed basing on the fitting formula compared to air conditioners. A national distribution map was developed to show the annual energy saving ratio in China. Results show that the PLHP system provides remarkable energy savings and the indoor temperature is remained at 18°C to 25°C. About 74.2% of Chinese cities lie in regions suitable for applications, and the annual energy saving ratio is over 30%. The average payback period for most cities in China is about 3.9 years. [ABSTRACT FROM AUTHOR]

Loop thermosyphon system is an efficient cooling system for energy saving in data center. It is necessary to investigate the performance of the loop thermosyphon system filled with alternative refrigerants to prompt its application. In this paper, a R32 loop thermosyphon system is experimentally investigated in an enthalpy difference laboratory for its performance. R22 and R134a are also used for comparison. The results show that the optimal filling ratio of R22, R134a and R32 is the same (37.3%). At the optimal filling ratio, the flow stability of R32 is obviously better than those of R22 and R134a. When cold water temperatures are 10–22 °C, the cooling capacity of R32 is 5.4–15.6% higher than those of R22 and R134a. After controlling extractor fans, the energy efficiency ratio of R32 is 4.9–206.6% higher than those of R22 and R134a. Moreover, the free cooling potentials of the three refrigerants in five typical climate cities across China are analyzed and compared. Compared with R22 and R134a, R32 has 3.6–11.0% and 18.5–26.7% higher free cooling percentages and annual energy efficiency ratio, respectively; and its annual average partial power usage effectiveness is 0.0012–0.0017 lower than that of other two refrigerants. [ABSTRACT FROM AUTHOR]