Data Center Air Conditioning System Research Articles

Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system

The automated fault diagnosis and smart management of heating, ventilation and air conditioning (HVAC) system is essential to the reliability of data centers. The machine learning is an efficient way to develop the smart management tools for HVAC system using historical data. In real data centers, however, the quantity of fault-free data is much more than that of fault data. As a result, the imbalanced training data limits the diagnosis capacity of machine learning models. The deep learning-based generative adversarial network is proposed to integrate with an incremental learning SVM model to diagnose the commonly occurred faults of data center air conditioning system. The adversarial learning between generator and discriminator generates the data of minority class for training purpose in HVAC system. The 8 sensitive features are selected as the inputs through three-step optimal selection strategy: manual screening, correlation feature selection and redundancy feature removing. The incremental learning strategy is proposed to update the FDD model regularly. The refrigerant leakage faults with intensities of 10%, 20%, 30% and 40% are tested and validated under various operational conditions. The experimental results show that the incremental learning SVM integrated with deep learning GAN reaches the acceptable diagnosis accuracies.

Simulation study on performance of data center air-conditioning system with novel evaporative condenser

Maximizing utilization of natural cold energy has become the key to reduce the energy consumption of data center air-conditioning system. A new type of evaporative condenser with water-cooled condensation is proposed and used in the data center composite air conditioning system. The model of air conditioning system is established and verified by the experimental platform. The simulation results show that although the coefficient of performance (COP) of the evaporative cooling heat pipe mode is reduced by 0.88% on average by the proposed evaporative condenser, the upper limit of temperature operation of this mode is raised from the original 15 °C to 17 °C. The heat pipe mode has been extended throughout the year, and the advantages of energy saving have been fully utilized. For the evaporative cooling vapor compression mode. The proposed evaporative condenser can reduce the energy of the composite system, and the COP is increased by 9.82% on average, showing high operating performance.

Study on influence of operating parameters of data center air conditioning system based on the concept of on-demand cooling

Abstract To improve energy efficiency of data centers, an air conditioning system for the concept of demand-based on cooling was proposed. This system combines a heat pipe cooling cycle and a vapor compression cooling cycle. Besides, the evaporative condenser is used to further utilize the natural cooling source, and the evaporator is put into the rack to realize on-demand cooling. A mathematical model was established to perform simulations of the system. The results showed that the energy consumption of compressor and pump reduce with the rising of outdoor unit air flow and evaporator inlet temperature. The coefficient of performance (COP) was selected as a reference index. When the cooling capacity is 8000 W, 6400 W and 4800 W, the optimal outdoor unit air flow of the heat pipe mode is 2000 m3/h, 1400 m3/h and 800 m3/h, respectively, and that of the vapor compression mode is 4000 m3/h, 3600 m3/h and 2400 m3/h, respectively. With the rising of evaporator inlet temperature from 29 °C to 41 °C under rated conditions, the heat pipe mode COP increased by 30.11%, 13.52% and 6.30%, respectively, and the vapor compression mode COP increased by 74.65%, 82.12% and 90.97%, respectively.

Optimization of modular data center air conditioning system

In high-density data center, energy consumption is increasing dramatically. For reducing the energy consumption, CFD software, Fluent 15.0, is used to simulate the flow and temperature field distribution with [Formula: see text] turbulence model and fluid–solid coupling method. Fans on the back of racks are simplified as walls with a certain pressure jump. Severs are treated as solid heat sources and porous media. Simulation results reveal that the temperature distribution on the back of racks is not uniform when air conditioners are arranged face-to-face, and local high temperature points emerge near the side wall of air conditioners. Factors affecting cooling efficiency, such as location of air conditioners, speed of inlets, distance of racks, etc., need to be improved. Geometric model is optimized by using a diagonal rack arrangement and drilling holes on the side wall. Based on this, four different cases with various hot aisle distance are proposed. Single and double modular data center are both simulated. Results of new model are better than those of baseline model.

Evaluation of a solar-powered absorption cooling system to a data center

Nowadays, data processing is a fundamental operation for modern businesses such as banks, technology companies, and factories, among others. However, computers dissipate significant amounts of heat yielding to an operational temperature rise. Considering that these machines cannot operate properly in inappropriate temperatures or at extreme conditions, they can come to a stop due to overheating. Consequently, cooling and air-conditioning systems are necessary to keep the proper operating temperature as well as the room temperature itself. On the other hand, a data center air-conditioning system drains a large amount of electrical power. Based on this, this paper evaluates a solar-powered absorption cooling system to assist the traditional electric chiller system resulting in energy saving, an advantage over conventional cooling, and day availability for this system. A case study is analyzed in a conventional data center located in the city of Sao Paulo, Brazil. First, the electric power density consumed by computers is 2.0 kW/m2, which represents a typical power load of an IT room. In addition, some other power density cases are also analyzed, namely: 0.5, 1.0, 4.0, and 8.0 kW/m2; these would occur at partial or at high operational loads. Local solar irradiation indexes are based on ASHRAE temperature and solar data for that city. The results are valid for a typical year and are compared to (1) a conventional data center and (2) the event combined with the solar-powered cooling system.

설비공학 분야별 최근 연구 동향 : 2018년 설비공학논문집 발표논문에 대한 종합적 고찰

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2018. The objective of this article is to understand the current issues in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities as per the recent research. Summary of this paper is follows :BR (1) The papers published in the field of building mechanical system can be categorized into equipment for multi-residential buildings and building envelope related technology, data center and clean room related technology, renewable energy related technology, and other related technologies. The topics included the energy consumption, indoor environment analysis, energy saving measures for radiant floor heating, temperature stratification of underfloor air-conditioning system, management of electric power demand, clean room humidification, initial cost of central heating and cooling system, solar thermal storage, data center air conditioning system and its performance evaluation, and underfloor heat exchanger performance etc.BR (2) Articles in the field of building environment can be classified into indoor environment, building energy and others. The research topics included; the comfort of passenger in automobile, the roof ventilation of the arcade type market, the flush out of new apartment buildings, window energy efficiency ratings, the windows’ airtightness and opening performance, pollutant diffusion in national inpatient isolation units, shading evaluation of PV module, light-shelf with PV, hospital building energy management process, IAQ in children

Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center

A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.

データセンター空調システムの省エネルギーおよび信頼性向上技術の開発 （第1報）各種省エネルギー手法の効果

データセンター空調システムの省エネルギーおよび信頼性向上技術の開発 （第1報）各種省エネルギー手法の効果

データセンター空調システムの省エネルギーおよび信頼性向上技術の開発 （第2報）サーバ実機周囲における気流・温度分布の再現計算

データセンター空調システムの省エネルギーおよび信頼性向上技術の開発 （第2報）サーバ実機周囲における気流・温度分布の再現計算

Green data center with IoT sensing and cloud-assisted smart temperature control system

With the growing shortage of energy around the world, energy efficiency is one of the most important considerations for a data center. In this paper, we propose a green data center air conditioning system assisted by cloud techniques, which consists of two subsystems: a data center air conditioning system and a cloud management platform. The data center air conditioning system includes environment monitoring, air conditioning, ventilation and temperature control, whereas the cloud platform provides data storage and analysis to support upper-layer applications. Moreover, the detailed design and implementation are presented, including the dispatch algorithm for the temperature control, topological structure of the sensor network, and framework for the environment monitoring node. A feasibility evaluation is used to verify that the proposed system can significantly reduce the data center energy consumption without degradation in the cooling performance.