Optimization of modular data center air conditioning system

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040123
Author(s):  
Yong-Liang Chen ◽  
Zi-Qiang Qin ◽  
Yao Li ◽  
Hai-Bo Wang ◽  
Sheryar Muhammad ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Geometric Model ◽  
Side Wall ◽  
Pressure Jump ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Factors Affecting ◽  
Face To Face ◽  
Modular Data

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.

scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

Data Center Room Air Conditioning Energy Consumption Analysis

2014 ◽  
Vol 672-674 ◽  
pp. 518-521
Author(s):  
Li Fei Song ◽  
Tao Li ◽  
Qi Fen Li ◽  
Lin Hui Zhao ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Field Research ◽  
Northern China ◽  
Air Conditioning System ◽  
Energy Consumption Analysis ◽  
Energy Saving Technology

In this paper, the Unicom's medium-sized IDC room in a city of northern China is the research object for the study. Based on field research of the room refrigeration conditions, data center room air conditioning system is carried out to optimize and for energy conservation research. Through the analytic methods of energy saving-technology, the best energy saving solutions is explored.

Energy Consumption Simulation of the Air Conditioning System in Large-Scale Buildings

2013 ◽  
Vol 724-725 ◽  
pp. 1506-1509
Author(s):  
Guang Ming Zhang ◽  
Xue Shen ◽  
Gui Zhong Tang
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Air Conditioning ◽  
Large Scale ◽  
Working Environment ◽  
Operation Condition ◽  
Air Conditioning System ◽  
Factors Affecting ◽  
Network Training ◽  
Consumption Data

The working environment of air conditioning system in large-scale building is very complex, and there is no significant linear relationship between factors affecting energy consumption and energy demand of air conditioning system. This study adopts a nonlinear regression model: ANN (artificial neural network) model as energy model of air conditioning system. Take outdoor temperature, categorical day-of-week variable, equipment efficiency and terminal load as input, energy demand as output. Use energy consumption data in 2011 for network training, and energy consumption data in 2012 to verify the reliability of model. Based on energy analysis, the operation condition and the characteristics of energy consumption of air conditioning system for large-scale buildings in Nanjing could be precisely represented.

System Optimization and Exergy Analysis of Air Conditioning System for Data Center

2013 ◽  
Vol 291-294 ◽  
pp. 1857-1862
Author(s):  
You Yin Jing ◽  
Qian Qian Liu ◽  
Jiang Jiang Wang
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Air Conditioning ◽  
Exergy Analysis ◽  
Operation Mode ◽  
System Optimization ◽  
Maximum Efficiency ◽  
System Operation ◽  
Air Conditioning System ◽  
Suitable Combination

As the air conditioning energy consumption of the date center has increasingly become the focus of attention, reformation for the energy consumption and exploration for the suitable combination model of the air conditioning system have significance in energy saving. This paper makes a program design for the air-conditioning system of a data center in Beijing, makes an analysis of the system operation mode and uses the concept of exergy efficiency to optimize the air conditioning system in order to achieve maximum efficiency.

scholarly journals The impact of the operation mode of CRACs on the temperature distribution of hot and cold aisles for the data center room

2021 ◽  
Vol 272 ◽  
pp. 02023
Author(s):  
Q Liu ◽  
J W Huang ◽  
L W Chong ◽  
Z Zhang ◽  
Z Qi ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Temperature Distribution ◽  
Data Center ◽  
Operation Mode ◽  
Great Influence ◽  
Air Conditioners ◽  
Air Volume ◽  
Partial Load ◽  
Calculation Results ◽  
The Impact

Redundancy is generally considered in the computer room air conditioners (CRACs) of the data center room. The position of the CRAC in the on state and the operating air volume have a great influence on the temperature distribution. The temperature distribution of the data center room will affect the failure rate of the servers and the energy consumption of the air-conditioning system. Taking a typical data center room as an example, this paper uses numerical simulation to study the influence of the on/off state and air volume of CRACs on the temperature distribution of the hot and cold aisles and energy consumption. The calculation results show that we should preferably turn off the staggered CRACs on the opposite side and close to the middle position under the partial load. When the IT load is unevenly distributed, it is irrational to use the setting method of even distribution of the air volume of each CRAC. The air volume of each CRAC should be appropriately adjusted based on the distribution characteristic of IT load to reduce the energy consumption.

Theoretical and Empirical Energy Impacts of Economization in Data Centers

2015 ◽  
Author(s):  
Dan Comperchio ◽  
Sameer Behere
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Energy Use ◽  
Data Centers ◽  
Energy Savings ◽  
Operating Conditions ◽  
Close Monitoring ◽  
Direct Expansion ◽  
System Architectures ◽  
Air Conditioners

Data center energy consumption can be divided into three broad categories: Information Technology (IT), Electrical, and Mechanical. An efficient data center uses the least amount of non-IT energy, which is typically divided between the mechanical and electrical systems. Mechanical systems generally contribute a large portion of the non-IT energy use by providing cooling from compressor-based equipment [1,2] and because of this, strategies to reduce compressor energy consumption can lead to significant mechanical system energy savings. The most efficient way to reduce compressor energy is through elimination or significant reduction in annual runtime. This is possible with the use of integrated airside or waterside economizers. This paper demonstrates the impacts of economization in data centers through data collected from four operating facilities over the course of implementing various economizer improvement projects. System architectures include water-cooled centrifugal chiller plant with waterside economization, direct expansion air handling units (AHU) with airside economization, air-cooled centrifugal chillers with integrated waterside economization, and direct expansion computer room air conditioners (CRAC) with evaporative cooling and waterside economization. A systematic and methodical comparison of the baseline and post-conditions is discussed, comparing expected to observed economizer operating conditions. The comparison of multiple real-world scenarios revealed a range of variances in expected operation of economizer sequences to actual observations, indicating a need for close monitoring of system performance by data center operators to fully realize economizer benefits within facilities.

Optimization of Enclosed Aisle Data Centers Using Bypass Recirculation

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Energy Consumption ◽  
Data Center ◽  
Data Centers ◽  
Total Power ◽  
Total Energy Consumption ◽  
Air Conditioners ◽  
Data Center Cooling ◽  
Flow Through

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model is used to evaluate parametrically the total energy consumption of the data center cooling infrastructure for data centers that utilize aisle containment. The analysis highlights the importance of reducing the total power required for moving the air within the computer room air conditioners (CRACs), the plenum, and the servers, rather than focusing primarily or exclusively on reducing the refrigeration system’s power consumption. In addition, the benefits of introducing a bypass recirculation branch in enclosed aisle configurations are shown. The analysis shows a potential for as much as a 60% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle in which all the data center exhaust is forced to flow through the CRACs. Furthermore, computational fluid dynamics is used to evaluate practical arrangements for implementing bypass recirculation in raised floor data centers. A configuration where bypass tiles, with controllable low-lift fans, are placed close to the discharge of CRACs results in increased mixing and is shown to be a suitable method for providing nearly thermally uniform conditions to the inlet of the servers in an enclosed cold aisle. Other configurations of bypass implementation are also discussed and explored.

Energy Consumption Pattern of Residential Buildings: Case Study of Residential Area in Batu Pahat, Johor

2020 ◽  
Vol 12 (9) ◽  
Author(s):  
Junaidah Jailani ◽  
◽  
Norsyalifa Mohamad ◽  
Muhammad Amirul Omar ◽  
Hauashdh Ali ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Residential Area ◽  
Consumption Pattern ◽  
Air Conditioners ◽  
Residential Sector ◽  
Waste Energy ◽  
Main Factor ◽  
Energy Consumption Pattern

According to the National Energy Balance report released by the Energy Commission of Malaysia in 2016, the residential sector uses 21.6% of the total energy in Malaysia. Residents waste energy through inefficient energy consumption and a lack of awareness. Building occupants are considered the main factor that influences energy consumption in buildings, and to change energy consumption on an overall scale, it is crucial to change individual behaviour. Therefore, this study focused on analysing the energy consumption pattern and the behaviour of consumers towards energy consumption in their homes in the residential area of Batu Pahat, Johor. A self-administrated questionnaire approach was employed in this study. The findings of this study showed that the excessive use of air conditioners was a significant factor in the increasing electricity bills of homeowners as well as the inefficient use of electrical appliances. Also, this study determined the effect of awareness on consumer behaviour. This study recommends ways to help minimise energy consumption in the residential area.

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