Cooling System In Data Center Research Articles

Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review

In the age of digitalization and big data, cooling systems in data centers are vital for maintaining equipment efficiency and environmental sustainability. Although many studies have focused on the classification and optimization of data center cooling systems, systematic reviews using bibliometric methods are relatively scarce. This review uses bibliometric analysis to explore the classifications, control optimizations, and energy metrics of data center cooling systems, aiming to address research gaps. Using CiteSpace and databases like Scopus, Web of Science, and IEEE, this study maps the field’s historical development and current trends. The findings indicate that, firstly, the classification of cooling systems, optimization strategies, and energy efficiency metrics are the current focal points. Secondly, this review assesses the applicability of air-cooled and liquid-cooled systems in different operational environments, providing practical guidance for selection. Then, for air cooling systems, the review demonstrates that optimizing the design of static pressure chamber baffles has significantly improved airflow uniformity. Finally, the article advocates for expanding the use of artificial intelligence and machine learning to automate data collection and energy efficiency analysis, it also calls for the global standardization of energy efficiency metrics. This study offers new perspectives on the design, operational optimization, and performance evaluation of data center cooling systems.

Gross Error Detection and Data Correction in IIoT-Based Data Center Cooling System

A data center always require a proper cooling system. This research study a data center with water based cooling system that consists of two chillers and two in-rack coolers. To control the system, an Industrial Internet of Things (IIoT) infrastructures has been deployed. It able to monitors real-time data from various sensors such as temperature (T), pressure (P), water flow (Q). The data were supposed to be used for optimization. However, early assessment showed that there were discrepancies between the sensors. Therefore, data reconciliation method is essential to get the valid data from the sensors. This paper discusses the implementation of gross error detection and correction by using least square method and bias compensation.

Thermal Management and Energy Consumption in Air, Liquid, and Free Cooling Systems for Data Centers: A Review

The thermal management and reduction of energy consumption in cooling systems have become major trends with the continued growth of high heat dissipation data centers and the challenging energy situation. However, the existing studies have been limited to studying the influences of individual factors on energy saving and thermal management and have not been systematically summarized. Thus, this paper reviews the key factors in achieving thermal management and reducing energy consumption in each cooling system, the corresponding research, and optimization methods. To achieve these goals, in this paper, literature surveys on data center cooling systems are investigated. For data center air cooling, thermal management is mainly related to the uniform distribution of hot and cold air. Adjusting the porosity of perforated tiles can reduce energy consumption. For liquid cooling and free cooling systems, climate conditions, cooling system structural design, coolant type, and flow rate are key factors in achieving thermal management and reducing energy consumption. This paper provides the power usage effectiveness (PUE) values of the cooling systems in some cases. A summary of the key factors can provide directions for research on thermal management and energy reduction, and a summary of previous research can provide a basis for future optimization.

Experimental Investigation of Heat Transfer and Flow Characteristics of Split Natural Cooling System for Data Center Based on Micro Heat Pipe Array

This paper presents a new type of split natural cooling system that maximizes the use of natural cold energy to significantly reduce the power consumption of the air conditioning system in data centers. A split natural cooling system module, which consisted of indoor and outdoor heat exchanger based on micro heat pipe arrays connected by liquid circulation system, was selected for experimental research. The heat transfer process and flow characteristics were analyzed under different outdoor environment temperatures, air and water flow rates, and different ratios of heat transfer components (N) of indoor and outdoor heat exchangers. To improve the utilization of natural cold energy, two kinds of heat dissipation conditions, namely room and heat channel-based, were proposed. The indoor temperature of two conditions at 28 °C and 38 °C were simulated in the laboratory at constant temperature-humidity, respectively. Results indicated that the air flow rate had a greater influence on the heat transfer performance than the water flow rate. The pressure drop of the air and water sides was at a lower level, and the fitting curve of the pressure drop was obtained to provide a reference for the heat exchanger design and equipment selection. When the ratio of heat transfer components (N) of the indoor and outdoor heat exchanger was approximately 0.75, the split natural cooling system showed optimal comprehensive performance. Under heat channel-based conditions, the maximum heat transfer rate reached 12.4 kW, and the maximum energy efficiency ratio was 17.15; the maximum heat transfer rate and the maximum energy efficiency ratio increased by 42.5% and 22.64% compared with the room-based condition, respectively. The fitting curve of the energy efficiency ratio was calculated under different outdoor temperatures at two heat dissipation conditions.

Experimental Study on Reliable Operation Strategy for Multi-Split Backplane Cooling System in Data Centers

Experimental Study on Reliable Operation Strategy for Multi-Split Backplane Cooling System in Data Centers

A local knit pattern-based automated fault classification method for the cooling system of the data center

Many automated sound-based fault diagnosis or classification methods have been presented in the literature. A novel automatic fault diagnosis method is presented by using sounds for the cooling system of the data center. A novel feature generator and an iterative feature selector are used together to present an automated data center cooling system (DCCS) fault diagnosing method. A new feature generator is proposed inspired by knitting hence, it is called a local knit pattern (LKP). A multiple pooling based decomposition method is presented as a preprocessor. The LKP generates features from each signal. Iterative neighborhood component analysis (INCA) feature selector selects the most discriminative. Twelve classifiers are calculated in the classification phase. The selected classifiers were achieved greater than 90.0% classification accuracies, and the best-resulted classifier is Quadratic SVM. It reached 96.40% classification accuracy. Results show that new generation automated sound fault diagnosis applications can also be developed as novel sound-based fault detection applications.

Liquid Cooling System and Air Cooling System in Data Center: A Comparison

Liquid Cooling System and Air Cooling System in Data Center: A Comparison

Operation Optimization of Liquid Cooling Systems in Data Centers by the Heat Current Method and Artificial Neural Network

Liquid cooling systems in data centers have been attracting more attentions due to its better cooling capability and less energy consumption. In order to propose an effective optimization method for the operation of indirect liquid cooling systems, this paper first constructs an experiment platform and applies the heat current method to build the global heat transfer constraints of the whole system. Particularly, the thermal conductance of each heat exchanger under different working conditions is predicted by the Artificial Neural Networks (ANN) trained by the historical data. On this basis, combining the heat transfer and fluid flow constraints together with the Lagrange multiplier method builds the optimization model with the objective of minimum pumping power consumption (PPC), solving which by the solution strategy designed obtains the optimal frequencies of the variable frequency pumps (VFPs). Operating with the optimal and other feasible operating conditions validates the optimization model. Meanwhile, the experiments with variable heat loads and flow resistances provide some guidelines for the optimal system operation. For instance, to address heat load increase of a branch, it needs to increase the frequencies of the VFPs, not only the corresponding hot loop but also the whole cold loop.

Thermal Performance Evaluation of a Data Center Cooling System under Fault Conditions

If a data center experiences a system outage or fault conditions, it becomes difficult to provide a stable and continuous information technology (IT) service. Therefore, it is critical to design and implement a backup system so that stability can be maintained even in emergency (unforeseen) situations. In this study, an actual 20 MW data center project was analyzed to evaluate the thermal performance of an IT server room during a cooling system outage under six fault conditions. In addition, a method of organizing and systematically managing operational stability and energy efficiency verification was identified for data center construction in accordance with the commissioning process. Up to a chilled water supply temperature of 17 °C and a computer room air handling unit air supply temperature of 24 °C, the temperature of the air flowing into the IT server room fell into the allowable range specified by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers standard (18–27 °C). It was possible to perform allowable operations for approximately 320 s after cooling system outage. Starting at a chilled water supply temperature of 18 °C and an air supply temperature of 25 °C, a rapid temperature increase occurred, which is a serious cause of IT equipment failure. Due to the use of cold aisle containment and designs with relatively high chilled water and air supply temperatures, there is a high possibility that a rapid temperature increase inside an IT server room will occur during a cooling system outage. Thus, the backup system must be activated within 300 s. It is essential to understand the operational characteristics of data centers and design optimal cooling systems to ensure the reliability of high-density data centers. In particular, it is necessary to consider these physical results and to perform an integrated review of the time required for emergency cooling equipment to operate as well as the backup system availability time.

A Calculation Model for Typical Data Center Cooling System

This paper has established a typical calculation model for typical data center cooling system, including the inside room CRAC system, outside free cooling usage system and chiller. The aim of establishing such method is to calculate the power consumption of the data center and to guide the work of energy conservation. The results show that, higher inside room temperature, lower water mass flow rate and lower outside air temperature lead to lower PUE.

Failure Analysis of Direct Liquid Cooling System in Data Centers

In this paper, the impact of direct liquid cooling (DLC) system failure on the information technology (IT) equipment is studied experimentally. The main factors that are anticipated to affect the IT equipment response during failure are the central processing unit (CPU) utilization, coolant set point temperature (SPT), and the server type. These factors are varied experimentally and the IT equipment response is studied in terms of chip temperature and power, CPU utilization, and total server power. It was found that failure of this cooling system is hazardous and can lead to data center shutdown in less than a minute. Additionally, the CPU frequency throttling mechanism was found to be vital to understand the change in chip temperature, power, and utilization. Other mechanisms associated with high temperatures were also observed such as the leakage power and the fans' speed change. Finally, possible remedies are proposed to reduce the probability and the consequences of the cooling system failure.

비상시 열원중단에 따른 데이터센터의 냉각시스템 열성능 평가에 관한 사례연구

비상시 열원중단에 따른 데이터센터의 냉각시스템 열성능 평가에 관한 사례연구

Cooling Systems in Data Centers: State of Art and Emerging Technologies

The growing number, size, complexity and energy density of data centers due to increasing demand for storage, networking and computation bring a considerable energy challenge. Several measures to improve energy efficiency are being studied, not only to allow a supportable industry growth but also to reduce operational costs. Cooling energy consumption constitutes a large portion of the total consumption of data centers, which can account up to 40% in the case of inefficient cooling systems. In this paper a critical discussion on existing and emerging technologies for data center cooling systems was carried out. Fundamental aspects concerning advantages and drawbacks of each examined cooling system were discussed. Moreover a critical analysis on next future technology solutions for obtaining high energy efficiency data center is performed.

Outdoor Air Cooling Systems for Data Centers

Outdoor Air Cooling Systems for Data Centers

D-53 データセンターを対象としたフリークーリングシステムの検討 : (第2報)日本における年間エネルギー消費量比較

D-53 データセンターを対象としたフリークーリングシステムの検討 : (第2報)日本における年間エネルギー消費量比較

Feasibility Study of Heat Pipe Ice Storage System Using Natural Energy for Large-Sized Data Center Cooling System

Feasibility Study of Heat Pipe Ice Storage System Using Natural Energy for Large-Sized Data Center Cooling System