Energy Savings Through Hot and Cold Aisle Containment Configurations for Air Cooled Servers in Data Centers

2011 ◽  
Author(s):  
Roger Schmidt ◽  
Aparna Vallury ◽  
Madhusudan Iyengar
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Energy Savings ◽  
Green Technologies ◽  
Advantages And Disadvantages ◽  
Hot Air ◽  
Type Data ◽  
Cold Aisle Containment

The increased focus on green technologies and energy efficiency coupled with the insatiable desire of IT equipment customers for more performance has driven manufacturers to deploy energy efficient technologies in the data centers. This paper describes a technique to achieve significant energy savings by preventing the cold and hot air streams within the data center from mixing. More specifically, techniques will be described that will separate the cool supply air to the server racks and exhaust hot air that returns to the air conditioning units. This separation can be achieved by three types of containment systems — cold aisle containment, hot aisle containment, and server rack exhaust chimneys. The advantages and disadvantages of each technique will be outlined. To show the potential for energy efficiency improvements a case study in deploying a cold aisle containment solution for a 8944 ft2 data center will be presented. This study will show that 59% of the energy required for the computer room air conditioning (CRAC) units used in a traditional open type data center could be saved.

Load Capacity and Thermal Efficiency Optimization of a Research Data Center Using Computational Modeling

2015 ◽  
Author(s):  
Joseph R. H. Schaadt ◽  
Kamran Fouladi ◽  
Aaron P. Wemhoff ◽  
Joseph G. Pigeon
Keyword(s):  
Flow Rate ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Load Capacity ◽  
Research Data ◽  
Optimal Operating ◽  
Cold Aisle Containment ◽  
Operating Points ◽  
The Ideal

Data centers are most commonly cooled by air delivered to electronic equipment from centralized cooling systems. The research presented here is motivated by the need for strategies to improve and optimize the load capacity and thermal efficiency of data centers by using computational fluid dynamics (CFD). Here, CFD is used to model and optimize the Villanova Steel Orca Research Center (VSORC). VSORC, presently in the design stages, will provide a testing environment as well as the capability to investigate best practices and state of the art strategies including hybrid cooling, IT load distribution, density zones, and hot aisle and cold aisle containment. The results of this study will be used in the overall design and construction of the aforementioned research data center. The objective of this study is to find the optimal operating points and design layout of a data center while still meeting certain design constraints. A focus is on finding both the ideal total supply flow rate of the air conditioning units and the ideal chilled water supply temperature (CHWST) setpoint under different data center design configurations and load capacities. The total supply flow rate of the air conditioning units and the supply temperature setpoint of the chilled water system are varied as design parameters in order to systematically determine the optimal operating points. The study also examines the influence of hot aisle and cold aisle containment strategies in full containment, half containment, and no containment configurations on the determined optimal operating conditions for the modeled research data center.

Impact of Cold Aisle Containment on Thermal Performance of Data Center

2013 ◽  
Author(s):  
Bharathkrishnan Muralidharan ◽  
Saurabh K. Shrivastava ◽  
Mahmoud Ibrahim ◽  
Sami A. Alkharabsheh ◽  
Bahgat G. Sammakia
Keyword(s):  
Data Center ◽  
Heat Load ◽  
Energy Savings ◽  
Air Conditioner ◽  
Cold Air ◽  
Hot Air ◽  
Air Temperatures ◽  
Set Up ◽  
Cold Aisle Containment

The use of air containment systems has been a growing trend in the data center industry and is an important energy saving strategy for data center optimization. Cold Aisle Containment (CAC) is one of the most effective passive cooling solutions for high density heat load applications. Cold Aisle Containment provides a physical separation between the cold air and the hot exhaust air by enclosing the cold aisle, preventing hot air recirculation and cold air bypass. This separation provides uniform inlet air temperatures to the servers, which can further contribute to overall data center efficiency. This paper includes the thermal test data for a data center lab with and without a CAC set up. The paper quantifies the thermal impact of implementing a CAC system over an open Hot Aisle/Cold Aisle (HA/CA) arrangement for three different cabinet heat load conditions at two different CRAC (Computer Room Air Conditioner) return air set point conditions. It studies the advantages of CAC over standard HA/CA arrangement. A case study has been presented showing a cooling energy savings of 22% with the use of a CAC system over a standard HA/CA arrangement.

scholarly journals Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

2021 ◽  
Vol 11 (11) ◽  
pp. 4719
Author(s):  
Romulos da S. Machado ◽  
Fabiano dos S. Pires ◽  
Giovanni R. Caldeira ◽  
Felipe T. Giuntini ◽  
Flávia de S. Santos ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Energy Resources ◽  
Cooling Power ◽  
Industrial Experience ◽  
Use Of Resources

Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.

Design for Sustainable Data Center Energy Use and Eco-Footprint

2010 ◽  
Author(s):  
Milton Meckler
Keyword(s):  
Data Center ◽  
Air Conditioning ◽  
Fossil Fuels ◽  
Energy Use ◽  
Data Centers ◽  
Ghg Emissions ◽  
High Energy ◽  
Operational Reliability ◽  
Energy Utilization ◽  
Air Conditioning Systems

What does remain a growing concern for many users of Data Centers is their continuing availability following the explosive growth of internet services in recent years, The recent maximizing of Data Center IT virtualization investments has resulted in improving the consolidation of prior (under utilized) server and cabling resources resulting in higher overall facility utilization and IT capacity. It has also resulted in excessive levels of equipment heat release, e.g. high energy (i.e. blade type) servers and telecommunication equipment, that challenge central and distributed air conditioning systems delivering air via raised floor or overhead to rack mounted servers arranged in alternate facing cold and hot isles (in some cases reaching 30 kW/rack or 300 W/ft2) and returning via end of isle or separated room CRAC units, which are often found to fight each other, contributing to excessive energy use. Under those circumstances, hybrid, indirect liquid cooling facilities are often required to augment above referenced air conditioning systems in order to prevent overheating and degradation of mission critical IT equipment to maintain rack mounted subject rack mounted server equipment to continue to operate available within ASHRAE TC 9.9 prescribed task psychometric limits and IT manufacturers specifications, beyond which their operational reliability cannot be assured. Recent interest in new web-based software and secure cloud computing is expected to further accelerate the growth of Data Centers which according to a recent study, the estimated number of U.S. Data Centers in 2006 consumed approximately 61 billion kWh of electricity. Computer servers and supporting power infrastructure for the Internet are estimated to represent 1.5% of all electricity generated which along with aggregated IT and communications, including PC’s in current use have also been estimated to emit 2% of global carbon emissions. Therefore the projected eco-footprint of Data Centers into the future has now become a matter of growing concern. Accordingly our paper will focus on how best to improve the energy utilization of fossil fuels that are used to power Data Centers, the energy efficiency of related auxiliary cooling and power infrastructures, so as to reduce their eco-footprint and GHG emissions to sustainable levels as soon as possible. To this end, we plan to demonstrate significant comparative savings in annual energy use and reduction in associated annual GHG emissions by employing a on-site cogeneration system (in lieu of current reliance on remote electric power generation systems), introducing use of energy efficient outside air (OSA) desiccant assisted pre-conditioners to maintain either Class1, Class 2 and NEBS indoor air dew-points, as needed, when operated with modified existing (sensible only cooling and distributed air conditioning and chiller systems) thereby eliminating need for CRAC integral unit humidity controls while achieving a estimated 60 to 80% (virtualized) reduction in the number servers within a existing (hypothetical post-consolidation) 3.5 MW demand Data Center located in southeastern (and/or southern) U.S., coastal Puerto Rico, or Brazil characterized by three (3) representative microclimates ranging from moderate to high seasonal outside air (OSA) coincident design humidity and temperature.

Introducing Energy Efficiency Metrics for Server and Data Centers

2011 ◽  
Author(s):  
Abdlmonem H. Beitelmal ◽  
Drazen Fabris
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Idle Power ◽  
Relevant Variables ◽  
The Cost ◽  
Evaluation Of Data

New servers and data center metrics are introduced to facilitate proper evaluation of data centers power and cooling efficiency. These metrics will be used to help reduce the cost of operation and to provision data centers cooling resources. The most relevant variables for these metrics are identified and they are: the total facility power, the servers’ idle power, the average servers’ utilization, the cooling resources power and the total IT equipment power. These metrics can be used to characterize and classify servers and data centers performance and energy efficiency regardless of their size and location.

Influence of Experimental Uncertainty on Prediction of Holistic Multi-Scale Data Center Energy Efficiency

2011 ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Niru Kumari ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Experimental Analysis ◽  
Data Center ◽  
Data Centers ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
System Level ◽  
Scale Model ◽  
Multi Scale ◽  
Scale Data

As the energy footprint of data centers continues to increase, models that allow for “what-if” simulations of different data center design and management paradigms will be important. Prior work by the authors has described a multi-scale energy efficiency model that allows for evaluating the coefficient of performance of the data center ensemble (COPGrand), and demonstrated the utility of such a model for purposes of choosing operational set-points and evaluating design trade-offs. However, experimental validation of these models poses a challenge because of the complexity involved with tailoring such a model for implementation to legacy data centers, with shared infrastructure and limited control over IT workload. Further, test facilities with dummy heat loads or artificial racks in lieu of IT equipment generally have limited utility in validating end-to-end models owing to the inability of such loads to mimic phenomena such as fan scalability, etc. In this work, we describe the experimental analysis conducted in a special test chamber and data center facility. The chamber, focusing on system level effects, is loaded with an actual IT rack, and a compressor delivers chilled air to the chamber at a preset temperature. By varying the load in the IT rack as well as the air delivery parameters — such as flow rate, supply temperature, etc. — a setup which simulates the system level of a data center is created. Experimental tests within a live data center facility are also conducted where the operating conditions of the cooling infrastructure are monitored — such as fluid temperatures, flow rates, etc. — and can be analyzed to determine effects such as air flow recirculation, heat exchanger performance, etc. Using the experimental data a multi-scale model configuration emulating the data center can be defined. We compare the results from such experimental analysis to a multi-scale energy efficiency model of the data center, and discuss the accuracies as well as inaccuracies within such a model. Difficulties encountered in the experimental work are discussed. The paper concludes by discussing areas for improvement in such modeling and experimental evaluation. Further validation of the complete multi-scale data center energy model is planned.

scholarly journals The Numerical Simulation of the Airflow Distribution and Energy Efficiency in Data Centers with Three Types of Aisle Layout

2019 ◽  
Vol 11 (18) ◽  
pp. 4937 ◽  
Author(s):  
Jing Ni ◽  
Bowen Jin ◽  
Shanglei Ning ◽  
Xiaowei Wang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Air Conditioning ◽  
Data Centers ◽  
Simulation Method ◽  
Cooling Capacity ◽  
Energy Utilization ◽  
Improve Energy Efficiency ◽  
Electricity Cost ◽  
Airflow Distribution

The energy consumption of fast-growing data centers is drawing attentions from not only energy organizations and institutions all over the world, but also charity groups, such as Greenpeace, and research shows that the power consumption of air conditioning makes up a large proportion of the electricity cost in data centers. Therefore, more detailed investigations of air conditioning power consumption are warranted. Three types of airflow distributions with different aisle layouts (the open aisle, the closed cold aisle, and the closed hot aisle) were investigated with Computational Fluid Dynamics (CFD) methods in a typical data center of four rows of racks in this study. To evaluate the results of thermal and bypass phenomenon, the temperature increase index (β) and the energy utilization index (ηr) were used. The simulations show that there is a better trend of the β index and ηr index both closed cold aisle and closed hot aisle compared with free open aisle. Especially with high air flow rate, the β index decreases and the ηr index increases considerably. Moreover, the results prove the closed aisles (both closed cold aisle and closed hot aisle) can not only significantly improve the airflow distribution, but also reduce the mixture of cold and heat flow, and therefore improve energy efficiency. In addition, it proves the design of the closed aisles can meet the increasing density of installations and our simulation method could evaluate the cooling capacity easily.

scholarly journals New Data Center Performance Index: Perfect Design Data Center—PDD

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 110
Author(s):  
Alexandre F. Santos ◽  
Pedro D. Gaspar ◽  
Heraldo J. L. de Souza
Keyword(s):  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Current Method ◽  
Energy Usage ◽  
Design Data ◽  
The Difference ◽  
Power Usage Effectiveness ◽  
Approximate Result ◽  
Classification Table

Data Centers (DC) are specific buildings that require large infrastructures to store all the information needed by companies. All data transmitted over the network is stored on CDs. By the end of 2020, Data Centers will grow 53% worldwide. There are methodologies that measure the efficiency of energy consumption. The most used metric is the Power Usage Effectiveness (PUE) index, but it does not fully reflect efficiency. Three DC’s located at the cities of Curitiba, Londrina and Iguaçu Falls (Brazil) with close PUE values, are evaluated in this article using the Energy Usage Effectiveness Design (EUED) index as an alternative to the current method. EUED uses energy as a comparative element in the design phase. Infrastructure consumption is the sum of energy with Heating, Ventilating and Air conditioning (HVAC) equipment, equipment, lighting and others. The EUED values obtained were 1.245 (kWh/yr)/(kWh/yr), 1.313 (kWh/yr)/(kWh/yr) and 1.316 (kWh/yr)/(kWh/yr) to Curitiba, Londrina and Iguaçu Falls, respectively. The difference between the EUED and the PUE Constant External Air Temperature (COA) is 16.87% for Curitiba, 13.33% for Londrina and 13.30% for Iguaçu Falls. The new Perfect Design Data center (PDD) index prioritizes efficiency in increasing order is an easy index to interpret. It is a redefinition of EUED, given by a linear equation, which provides an approximate result and uses a classification table. It is a decision support index for the location of a Data Center in the project phase.

Exergy-Based Optimization Strategies for Multi-Component Data Center Thermal Management: Part I — Analysis

2005 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Heat Dissipation ◽  
Real Data ◽  
Management Systems ◽  
Prior Work ◽  
Operating Strategy ◽  
Air Space

As heat dissipation in data centers rises by orders of magnitude, inefficiencies such as recirculation will have an increasingly significant impact on the thermal manageability and energy efficiency of the cooling infrastructure. For example, prior work has shown that for simple data centers with a single Computer Room Air-Conditioning (CRAC) unit, an operating strategy that fails to account for inefficiencies in the air space can result in suboptimal performance. To enable system-wide optimality, an exergy-based approach to CRAC control has previously been proposed. However, application of such a strategy in a real data center environment is limited by the assumptions inherent to the single-CRAC derivation. This paper addresses these assumptions by modifying the exergy-based approach to account for the additional interactions encountered in a multi-component environment. It is shown that the modified formulation provides the framework necessary to evaluate performance of multi-component data center thermal management systems under widely different operating circumstances.

Energy Aware Grid: Global Workload Placement Based on Energy Efficiency

2003 ◽  
Author(s):  
Chandrakant Patel ◽  
Ratnesh Sharma ◽  
Cullen Bash ◽  
Sven Graupner
Keyword(s):  
Energy Efficiency ◽  
Thermal Management ◽  
Pervasive Computing ◽  
Data Center ◽  
Data Centers ◽  
Global Network ◽  
New Delhi ◽  
Energy Aware ◽  
And Storage ◽  
Grid Based

Computing will be pervasive, and enablers of pervasive computing will be data centers housing computing, networking and storage hardware. The data center of tomorrow is envisaged as one containing thousands of single board computing systems deployed in racks. A data center, with 1000 racks, over 30,000 square feet, would require 10 MW of power to power the computing infrastructure. At this power dissipation, an additional 5 MW would be needed by the cooling resources to remove the dissipated heat. At $100/MWh, the cooling alone would cost $4 million per annum for such a data center. The concept of Computing Grid, based on coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations, is emerging as the new paradigm in distributed and pervasive computing for scientific as well as commercial applications. We envision a global network of data centers housing an aggregation of computing, networking and storage hardware. The increased compaction of such devices in data centers has created thermal and energy management issues that inhibit sustainability of such a global infrastructure. In this paper, we propose the framework of Energy Aware Grid that will provide a global utility infrastructure explicitly incorporating energy efficiency and thermal management among data centers. Designed around an energy-aware co-allocator, workload placement decisions will be made across the Grid, based on data center energy efficiency coefficients. The coefficient, evaluated by the data center’s resource allocation manager, is a complex function of the data center thermal management infrastructure and the seasonal and diurnal variations. A detailed procedure for implementation of a test case is provided with an estimate of energy savings to justify the economics. An example workload deployment shown in the paper aspires to seek the most energy efficient data center in the global network of data centers. The locality based energy efficiency in a data center is shown to arise from use of ground coupled loops in cold climates to lower ambient temperature for heat rejection e.g. computing and rejecting heat from a data center at nighttime ambient of 20°C. in New Delhi, India while Phoenix, USA is at 45°C. The efficiency in the cooling system in the data center in New Delhi is derived based on lower lift from evaporator to condenser. Besides the obvious advantage due to external ambient, the paper also incorporates techniques that rate the efficiency arising from internal thermo-fluids behavior of a data center in workload placement decision.

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