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.

Numerical Modeling of Perforated Tile Flow Distribution in a Raised-Floor Data Center

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Emad Samadiani ◽  
Jeffrey Rambo ◽  
Yogendra Joshi
Keyword(s):  
Numerical Modeling ◽  
Flow Rate ◽  
Data Center ◽  
Air Conditioning ◽  
Flow Distribution ◽  
Air Flow Rate ◽  
Flow Rates ◽  
Average Change ◽  
Tile Flow ◽  
Operating Points

This paper is centered on quantifying the effect of computer room and computer room air conditioning (CRAC) unit modeling on the perforated tile flow distribution in a representative raised-floor data center. Also, this study quantifies the effect of plenum pipes and perforated tile porosity on the operating points of the CRAC blowers, total CRAC air flow rate, and its distribution. It is concluded that modeling the computer room, the CRAC units, and/or the plenum pipes could make an average change of up to 17% in the tile flow rates with a maximum of up to 135% for the facility with 56% open tiles while the average and maximum changes for the facility with 25% open tiles are 6% and 60%, respectively.

Numerical Modeling of Perforated Tile Flow Distribution in a Raised-Floor Data Center

2007 ◽  
Author(s):  
Emad Samadiani ◽  
Jeffrey Rambo ◽  
Yogendra Joshi
Keyword(s):  
Numerical Modeling ◽  
Flow Rate ◽  
Data Center ◽  
Air Conditioning ◽  
Air Flow ◽  
Flow Distribution ◽  
Air Flow Rate ◽  
Tile Flow ◽  
Operating Points

This paper is centered on quantifying the effect of computer room and computer room air conditioning (CRAC) unit modeling on the perforated tile flow distribution in a representative raised-floor data center. Also, this study quantifies the effect of plenum pipes and perforated tile porosity on the operating points of the CRAC blowers, total CRAC air flow rate, and its distribution. It is concluded that modeling the computer room, CRAC units, and/or the plenum pipes could change the tile flow distribution by up to 60% for the facility with 25% open perforated tiles and up to 135% for the facility with 56% open perforated tiles.

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.

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.

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.

Research on Intelligent Air Conditioning System of Data Center

2014 ◽  
Vol 602-605 ◽  
pp. 928-932
Author(s):  
Min Li ◽  
Yun Wang ◽  
Zheng Qian Feng ◽  
Wang Li
Keyword(s):  
Heat Exchange ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Exchange System ◽  
Air Conditioning System ◽  
Running Time ◽  
Heat Exchange System

By studying the energy-saving technologies of air-conditioning system in data centers, we designed a intelligent air conditioning system, improved the cooling efficiency of air conditioning system through a reasonable set of hot and cold aisles, reduced the running time of HVAC by using the intelligent heat exchange system, an provided a reference for energy saving research of air conditioning system of data centers.

Raised Floor Hybrid Cooled Data Center: Effect on Rack Inlet Air Temperatures When In Row Cooling Units are Installed Between the Racks

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Russell Tipton ◽  
Bruce Murray ◽  
Bahgat G. Sammakia ◽  
...  
Keyword(s):  
Flow Rate ◽  
Data Center ◽  
Heat Load ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Inlet Temperature ◽  
Air Flow Rate ◽  
Air Temperatures ◽  
Cooling Unit

The heat dissipated by high performance IT equipment such as servers and switches in data centers is increasing rapidly, which makes the thermal management even more challenging. IT equipment is typically designed to operate at a rack inlet air temperature ranging between 10 °C and 35 °C. The newest published environmental standards for operating IT equipment proposed by ASHARE specify a long term recommended dry bulb IT air inlet temperature range as 18°C to 27°C. In terms of the short term specification, the largest allowable inlet temperature range to operate at is between 5°C and 45°C. Failure in maintaining these specifications will lead to significantly detrimental impacts to the performance and reliability of these electronic devices. Thus, understanding the cooling system is of paramount importance for the design and operation of data centers. In this paper, a hybrid cooling system is numerically modeled and investigated. The numerical modeling is conducted using a commercial computational fluid dynamics (CFD) code. The hybrid cooling strategy is specified by mounting the in row cooling units between the server racks to assist the raised floor air cooling. The effect of several input variables, including rack heat load and heat density, rack air flow rate, in row cooling unit operating cooling fluid flow rate and temperature, in row coil effectiveness, centralized cooling unit supply air flow rate, non-uniformity in rack heat load, and raised floor height are studied parametrically. Their detailed effects on the rack inlet air temperatures and the in row cooler performance are presented. The modeling results and corresponding analyses are used to develop general installation and operation guidance for the in row cooler strategy of a data center.

scholarly journals Thermal Performance and Energy Saving Analysis of Indoor Air–Water Heat Exchanger Based on Micro Heat Pipe Array for Data Center

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 393 ◽  
Author(s):  
Heran Jing ◽  
Zhenhua Quan ◽  
Yaohua Zhao ◽  
Lincheng Wang ◽  
Ruyang Ren ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Energy Saving ◽  
Heat Pipe ◽  
Flow Rate ◽  
Data Center ◽  
Data Centers ◽  
Air Flow ◽  
Air Flow Rate ◽  
Cold Energy

According to the temperature regulations and high energy consumption of air conditioning (AC) system in data centers (DCs), natural cold energy becomes the focus of energy saving in data center in winter and transition season. A new type of air–water heat exchanger (AWHE) for the indoor side of DCs was designed to use natural cold energy in order to reduce the power consumption of AC. The AWHE applied micro-heat pipe arrays (MHPAs) with serrated fins on its surface to enhance heat transfer. The performance of MHPA-AWHE for different inlet water temperatures, water and air flow rates was investigated, respectively. The results showed that the maximum efficiency of the heat exchanger was 81.4% by using the effectiveness number of transfer units (ε-NTU) method. When the max air flow rate was 3000 m3/h and the water inlet temperature was 5 °C, the maximum heat transfer rate was 9.29 kW. The maximum pressure drop of the air side and water side were 339.8 Pa and 8.86 kPa, respectively. The comprehensive evaluation index j/f1/2 of the MHPA-AWHE increased by 10.8% compared to the plate–fin heat exchanger with louvered fins. The energy saving characteristics of an example DCs in Beijing was analyzed, and when the air flow rate was 2500 m3/h and the number of MHPA-AWHE modules was five, the minimum payback period of the MHPA-AWHE system was 2.3 years, which was the shortest and the most economical recorded. The maximum comprehensive energy efficiency ratio (EER) of the system after the transformation was 21.8, the electric power reduced by 28.3% compared to the system before the transformation, and the control strategy was carried out. The comprehensive performance provides a reference for MHPA-AWHE application in data centers.

Multi-Scale Modeling of High Power Density Data Centers

2003 ◽  
Author(s):  
Jeffrey D. Rambo ◽  
Yogendra K. Joshi
Keyword(s):  
Power Density ◽  
Flow Rate ◽  
High Power ◽  
Data Center ◽  
Data Centers ◽  
High Power Density ◽  
Length Scales ◽  
Density Data ◽  
Multi Scale ◽  
The Cost

Data center facilities, which house thousands of servers, storage devices and computing hardware, arranged in 2 meter high racks are providing many thermal challenges. Each rack can dissipate 10–15 kW, and with facilities as large as tens of thousands of square feet, the net power dissipated is typically on the order of several MW. The cost to power these facilities alone can be millions of dollars a year, with the cost to provide adequate cooling not far behind. Significant savings can be realized for the end user by improved design methodology of these high power density data centers. The fundamental need for improved characterization is motivated by inadequacies of simple energy balances to identify local ‘hot spots’ and ultimately provide a reliable modeling framework by which the data centers of the future can be designed. Recent attempts in computational fluid dynamics (CFD) modeling of data centers have been based around a simple rack model, either as a uniform heat generator or specified temperature rise across the rack. This desensitizes the solution to variations of heat load and corresponding flow rate needed to cool the servers throughout the rack. Heat generated at the smaller scales (the chip level) produces changes in the larger length scales of the data center. Accurate simulations of these facilities should attempt to resolve the range of length scales present. In this paper, a multi-scale model where each rack is subdivided into a series of sub-models to better mimic the behavior of individual servers inside the data center is proposed. A Reynolds-averaged Navier-Stokes CFD model of a 110 m2 (1,200 ft2) representative data center with the raised floor cooling scheme was constructed around this multi-scale rack model. Each of the 28 racks dissipated 4.23 kW, giving the data center a power density of 1076 W/m2 (100 W/ft2) based on total floor space. Parametric studies of varying heat loads within the rack and throughout the data center were performed to better characterize the interactions of the sub-rack scale heat generation and the data center. Major results include 1) the presence of a nonlinear thermal response in the upper portion of each rack due to recirculation effects and 2) significant changes in the surrounding racks (up to 10% increase in maximum temperature) observed in response to changes in rack flow rate (50% decrease).

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