Thermal Environment Of Data Center Research Articles

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

Dynamic thermal environment management technologies for data center: A review

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the “requirement, prediction, control, and evaluation” of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs.

Optimization on jet‐induced ventilation to enhance the uniformity of airflow distribution in data center

AbstractImproving the utilization efficiency of cold airflow in data center (DC) has already attracted widespread concern. A broad consensus has been reached that cold/hot‐aisle containment technologies can reduce the mixture of cold and hot air to weaken the overheating phenomenon of the rack in a certain extent. However, local hot spots still occur in the front racks due to lower tile flow rate at the entrance of the cold aisle, which means the thermal environment of front racks can be further improved to achieve the more uniform airflow distribution horizontally and vertically in DC. In this paper, an innovative method of airflow optimization applying jet fans in the cold aisles is proposed to make up the lower tile flow rate, adjust the flow path of cold air from the perforated tiles to racks, and balance temperature heterogeneity. In addition, inductive velocity, nozzle height, horizontal position, and attachment distance of jet fans are optimized to explore the optimal parameters. Results show that the incorporation of jet‐induced ventilation can effectively improve the cooling performance and overall thermal environment in DC, while the amount of IT equipment that exceeded the ASHRAE recommended supply air temperature (SAT) is reduced by about 38%. The jet fan with optimal parameters has a broad application space in the raised floor DC.

데이터센터 열환경 예측 및 적응형 제어알고리즘 개발

Purpose: As cooling energy accounts for 50.5% of the data center total energy use, it is necessary to reduce cooling energy with optimal control method. Thus, this study aimed at developing an adaptive control algorithm for data center thermal environment based on Artificial Neural Network (ANN). Method: A data center thermal environment model was built to obtain variable data which is used to train and evaluate performance of the prediction model and algorithm. The thermal environment prediction model was developed using ANN and optimization process was conducted by the Bayesian Optimization Algorithm. The adaptive control algorithm, which embedded the prediction model, adopted the Sliding Windows method and was optimized to maximize the control performance. Result: The performance evaluation of the developed algorithm was conducted compared with non-adaptive algorithm. As a result, the adaptive algorithm presented better performance than the non-adaptive with 0.45 of RMSE. Therefore, the developed algorithm secured the stability and accuracy and will be applied to supervisory control platforms.

데이터센터 최적 열환경 제공을 위한 적응형 인공신경망 냉매 유량 예측 모델 개발

데이터센터 최적 열환경 제공을 위한 적응형 인공신경망 냉매 유량 예측 모델 개발

Experimental and numerical investigation of an airflow management system in data center with lower-side terminal baffles for servers

A recent research showed that data centers consumed about 1.3% of total worldwide electricity. Thus, energy conservation in data centers should be paid enough attention to. The impacts of server lower-side terminal baffles (SLTBs) on the thermal environment in data center are optimized by experiments and simulations. In this paper, three different sizes (4 cm × 46 cm, 8 cm × 46 cm and 12 cm × 46 cm) of baffles with six different baffle angles (0°, 15°, 30°, 45°, 60° and 75°) are applied to investigate the optimum airflow distribution. The simulation model was validated by on-site experiments, while the experimental results are in good agreement with numerical results. The measurements show that the use of 45° angle and 8 cm × 46 cm SLTBs can effectively eliminate the heat accumulation and improve the airflow distribution and cooling efficiency in data center. In addition, it can decrease the temperature of the rack hotspot 1.9–2.5K, that reduces significantly the risk of local hotspot and avoid catastrophic damages to the servers. Furthermore, the exhaust air temperature distribution of the upper and lower parts of rack was quite uniform and by using the optimal baffles, the maximum temperature difference reduced from 2.2K to 0.5K.

Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply.

Rack level transient CFD modeling of data center

PurposeThe paper aims to capture the rack-level thermal dynamics in data center. It proposes the rack-level response experiments as well as transient Computational Fluid Dynamics (CFD) analysis to characterize the local thermal environment of the system.Design/methodology/approachA single sever simulator rack and its two neighboring racks with its cold and hot aisle containment have been modeled with known cold air supply temperature and flow rate for transient CFD analysis. The heat load was kept constant initially and varied case-to-case basis, which includes capturing the rack-level response with respect to changes in input. However, the response experiments on simulator rack were performed for 14 h by variation of server heat loads as step and ramp input.FindingsThe paper provides the detailed transient CFD analysis of data center racks. The local cold air flow rates and temperature at the vicinity of the racks showed significant effect due to changes in input. It was concluded that the rack-level dynamics impacts the thermal environment of data center and hence cannot be ignored.Research limitations/implicationsThe high computing devices and faster internet demands have led to major thermal management concerns for data center operators. To tackle this issue, capturing the system thermal dynamics is imperative. However, the system-level CFD analysis is computationally expensive. Therefore, this paper deals with the rack-level transient CFD study using commercial tool STAR CCM+.Practical implicationsThis paper includes the modeling of the servers as a porous media as well as the multigrid method to enhance the computational speed. The successful implementation of this approach validated through experiments. This would help to establish a base for research in any type of data center.Originality/valueThis paper provides the porous media approach to model servers and multigrid method to enhance the computational speed. At the same time, the thought of characterizing the local dynamics at the vicinity of data center racks is unique.

Optimal Design of the Data Center Environmental Temperature Monitoring

Traditional datacenter temperature monitoring method is to install some temperature sensors which are unable to cover all areas completely, so there are some problems about local hot spot. The method is aiming to find out the position of potential hazard which may exist in the data center on the basis of data center thermal environment evaluation method and characteristics of air distribution. By applying this method into the wireless sensor network monitoring system, it brings a meaningful improvement of the monitoring system.