A Statistical Approach to Thermal Zone Mapping

2011 ◽  
Author(s):  
Hongfei Li ◽  
Hendrik F. Hamann
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Statistical Approach ◽  
Sensor Data ◽  
Reliable Operation ◽  
Statistical Solution ◽  
Spatial Allocation ◽  
Cooling Unit ◽  
Air Handling Units ◽  
Mission Critical

Although in most buildings the spatial allocation of cooling resources can be managed using multiple air handling units and an air ducting system, it can be challenging for an operator to leverage this capability, partially because of the complex interdependencies between the different control options. This is in particular important for data centers, where cooling is a major cost while the sufficient allocation of cooling resources has to ensure the reliable operation of mission-critical information processing equipment. It has been shown that thermal zones can provide valuable decision support for optimizing cooling. Such Thermal zones are generally defined as the region of influence of a particular cooling unit or cooling “source” (such as an air condition unit (ACU)). In this paper we show results using a statistical approach, where we leverage real-time sensor data to obtain thermal zones in realtime. Specifically, we model the correlations between temperatures observed from sensors located at the discharge of an ACU and the other sensors located in the room. Outputs from the statistical solution can be used to optimize the placement of equipment in a data center, investigate failure scenarios, and make sure that a proper cooling solution has been achieved.

Data Center Equipment Reliability Concerns—Contamination Issues, Standards Actions, and Case Studies

2013 ◽  
Author(s):  
Chris Muller ◽  
Chuck Arent ◽  
Henry Yu
Keyword(s):  
Case Studies ◽  
Data Center ◽  
Data Centers ◽  
Electronic Equipment ◽  
Circuit Board ◽  
Lead Free ◽  
Reliable Operation ◽  
Contamination Assessment ◽  
Equipment Reliability ◽  
Equipment Failures

Abstract Lead-free manufacturing regulations, reduction in circuit board feature sizes and the miniaturization of components to improve hardware performance have combined to make data center IT equipment more prone to attack by corrosive contaminants. Manufacturers are under pressure to control contamination in the data center environment and maintaining acceptable limits is now critical to the continued reliable operation of datacom and IT equipment. This paper will discuss ongoing reliability issues with electronic equipment in data centers and will present updates on ongoing contamination concerns, standards activities, and case studies from several different locations illustrating the successful application of contamination assessment, control, and monitoring programs to eliminate electronic equipment failures.

Perimeter Cooling Unit and Localized Row-Based Cooling Unit Transient Air Flow Effects Modeling and Characterization in Data Centers

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Bahgat G. Sammakia ◽  
Russell Tipton ◽  
Mark Seymour
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Cooling Systems ◽  
Dynamic Thermal Management ◽  
Hybrid Cooling ◽  
Cooling Unit ◽  
Cooling Air

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

Data centers as the backbone of smart cities: principal considerations for the study of facility costs and benefits

Facilities ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Patrick T.I. Lam ◽  
Daniel Lai ◽  
Chi-Kin Leung ◽  
Wenjing Yang
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Data Center ◽  
Data Centers ◽  
Smart Cities ◽  
Costs And Benefits ◽  
Content Type ◽  
Cost And Benefit ◽  
Mission Critical ◽  
The Impact

Purpose As smart cities flourish amidst rapid urbanization and information and communication technology development, the demand for building more and more data centers is rising. This paper aims to examine the principal issues and considerations of data center facilities from the cost and benefit dimensions, with an aim to illustrate the approaches for maximizing the net benefits and remain “green.” Design/methodology/approach A comprehensive literature review informs the costs and benefits of data center facilities, and through a case study of a developer in Hong Kong, the significance of real estate costs is demonstrated. Findings Major corporations, establishments and governments need data centers as a mission critical facility to enable countless electronic transactions to take place any minute of the day. Their functional importance ranges from health, transport, payment, etc., all the way to entertainment activities. Some enterprises own them, whilst others use data center services on a co-location basis, in which case data centers are regarded as an investment asset. Real estate costs affect their success to a great extent, as in the case of a metropolitan where land cost forms a substantial part of the overall development cost for data centers. Research limitations/implications As the financial information of data center projects are highly sensitive due to the competitive status of the industry, a full set of numerical data is not available. Instead, the principles for a typical framework are established. Originality/value Data centers are very energy intensive, and their construction is usually fast tracked costing much to build, not to mention the high-value equipment contents housed therein. Their site locations need careful selection due to stability and security concerns. As an essential business continuity tool, the return on investment is a complex consideration, but certainly the potential loss caused by any disruption would be a huge amount. The life cycle cost and benefit considerations are revealed for this type of mission-critical facilities. Externalities are expounded, with emphasis on sustainable issues. The impact of land shortage for data center development is also demonstrated through the case of Hong Kong.

Application of Exploratory Data Analysis (EDA) Techniques to Temperature Data in a Conventional Data Center

2007 ◽  
Author(s):  
Ratnesh Sharma ◽  
Rocky Shih ◽  
Chandrakant Patel ◽  
John Sontag
Keyword(s):  
Data Analysis ◽  
Thermal Management ◽  
High Power ◽  
Data Center ◽  
Data Centers ◽  
Exploratory Data Analysis ◽  
Temperature Data ◽  
High Density ◽  
Exploratory Data ◽  
Mission Critical

Data centers are the computational hub of the next generation. Rise in demand for computing has driven the emergence of high density datacenters. With the advent of high density, mission-critical datacenters, demand for electrical power for compute and cooling has grown. Deployment of a large number of high powered computer systems in very dense configurations in racks within data centers will result in very high power densities at room level. Hosting business and mission-critical applications also demand a high degree of reliability and flexibility. Managing such high power levels in the data center with cost effective reliable cooling solutions is essential to feasibility of pervasive compute infrastructure. Energy consumption of data centers can also be severely increased by over-designed air handling systems and rack layouts that allow the hot and cold air streams to mix. Absence of rack level temperature monitoring has contributed to lack of knowledge of air flow patterns and thermal management issues in conventional data centers. In this paper, we present results from exploratory data analysis (EDA) of rack-level temperature data collected over a period of several months from a conventional production datacenter. Typical datacenters experience surges in power consumption due to rise and fall in compute demand. These surges can be long term, short term or periodic, leading to associated thermal management challenges. Some variations may also be machine-dependent and vary across the datacenter. Yet other thermal perturbations may be localized and momentary. Random variations due to sensor response and calibration, if not identified, may lead to erroneous conclusions and expensive faults. Among other indicators, EDA techniques also reveal relationships among sensors and deployed hardware in space and time. Identification of such patterns can provide significant insight into data center dynamics for future forecasting purposes. Knowledge of such metrics enables energy-efficient thermal management by helping to create strategies for normal operation and disaster recovery for use with techniques like dynamic smart cooling.

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 Design analysis and Work load characteristics of a Micro data Center

2021 ◽  
Vol 850 (1) ◽  
pp. 012018
Author(s):  
T Renugadevi ◽  
D Hari Prasanth ◽  
Appili Yaswanth ◽  
K Muthukumar ◽  
M Venkatesan
Keyword(s):  
Data Storage ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Work Load ◽  
Micro Data ◽  
Air Gaps ◽  
Cooling Unit ◽  
Cooling Air ◽  
Quantity Of Heat

Abstract Data centers are large-scale data storage and processing systems. It is made up of a number of servers that must be capable of handling large amount of data. As a result, data centers generate a significant quantity of heat, which must be cooled and kept at an optimal temperature to avoid overheating. To address this problem, thermal analysis of the data center is carried out using numerical methods. The CFD model consists of a micro data center, where conjugate heat transfer effects are studied. A micro data center consists of servers aligned with air gaps alternatively and cooling air is passed between the air gaps to remove heat. In the present work, the design of data center rack is made in such a way that the cold air is in close proximity to servers. The temperature and airflow in the data center are estimated using the model. The air gap is optimally designed for the cooling unit. Temperature distribution of various load configurations is studied. The objective of the study is to find a favorable loading configuration of the micro data center for various loads and effectiveness of distribution of load among the servers.

Reduced Order Thermal Modeling of Data Centers via Distributed Sensor Data

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Emad Samadiani ◽  
Yogendra Joshi ◽  
Hendrik Hamann ◽  
Madhusudan K. Iyengar ◽  
Steven Kamalsy ◽  
...  
Keyword(s):  
Temperature Field ◽  
Data Center ◽  
Data Centers ◽  
Sensor Data ◽  
Temperature Fields ◽  
Average Error ◽  
Local Error ◽  
Flow Rates ◽  
Reduced Order ◽  
Distributed Sensor

In this paper, an effective and computationally efficient proper orthogonal decomposition (POD) based reduced order modeling approach is presented, which utilizes selected sets of observed thermal sensor data inside the data centers to help predict the data center temperature field as a function of the air flow rates of computer room air conditioning (CRAC) units. The approach is demonstrated through application to an operational data center of 102.2 m2 (1100 square feet) with a hot and cold aisle arrangement of racks cooled by one CRAC unit. While the thermal data throughout the facility can be collected in about 30 min using a 3D temperature mapping tool, the POD method is able to generate temperature field throughout the data center in less than 2 s on a high end desktop personal computer (PC). Comparing the obtained POD temperature fields with the experimentally measured data for two different values of CRAC flow rates shows that the method can predict the temperature field with the average error of 0.68 °C or 3.2%. The maximum local error is around 8 °C, but the total number of points where the local error is larger than 1 °C, is only ∼6% of the total domain points.

Reduced Order Thermal Modeling of Data Centers via Distributed Sensor Data

2009 ◽  
Author(s):  
Emad Samadiani ◽  
Yogendra Joshi ◽  
Hendrik Hamann ◽  
Madhusudan K. Iyengar ◽  
Steven Kamalsy ◽  
...  
Keyword(s):  
Temperature Field ◽  
Data Center ◽  
Data Centers ◽  
Sensor Data ◽  
Temperature Fields ◽  
Average Error ◽  
Flow Rates ◽  
Reduced Order ◽  
Distributed Sensor ◽  
Mapping Tool

In this paper, an effective and computationally efficient Proper Orthogonal Decomposition (POD) based reduced order modeling approach is presented, which utilizes selected sets of observed thermal sensor data inside the data centers to help predict the data center temperature field as a function of the air flow rates of Computer Room Air Conditioning (CRAC) units. The approach is demonstrated through application to an operational data center of 102.2 m2 (1,100 square feet) with a hot and cold aisle arrangement of racks cooled by one CRAC unit. While the thermal data throughout the facility can be collected in about 30 minutes using a 3D temperature mapping tool, the POD method is able to generate temperature field throughout the data center in less than 2 seconds on a high end desktop PC. Comparing the obtained POD temperature fields with the experimentally measured data for two different values of CRAC flow rates shows that the method can predict the temperature field with the average error of 0.68 °C or 3.2%.

A Statistical Approach to Real-Time Updating and Automatic Scheduling of Physical Models

2013 ◽  
Author(s):  
Huijing Jiang ◽  
Xinwei Deng ◽  
Vanessa Lopez ◽  
Hendrik Hamann
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Physical Model ◽  
Energy Management ◽  
Data Center ◽  
Data Centers ◽  
Thermal Environment ◽  
Physical Models ◽  
Sensor Data ◽  
Automatic Scheduling

Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

Measured-Temperature Interpolation and Visualization for Data Centers

2013 ◽  
Author(s):  
Xuanhang (Simon) Zhang ◽  
Christopher M. Healey ◽  
Zachary R. Sheffer ◽  
James W. VanGilder
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Three Dimensional ◽  
Measurement Data ◽  
Temperature Sensors ◽  
Sensor Data ◽  
Temperature Fields ◽  
Measured Temperature ◽  
Local Cooling ◽  
Maintenance Requirements

The growing demand for data center facilities has made intelligently managed data center operations necessary. For temperature measurement and thermal management, a common practice is to install a limited number of temperature sensors evenly distributed throughout the room. However, data center operators rarely fully equip facilities with temperature sensors due to their cost, complexity, and maintenance requirements, creating vacancies in the data center temperature and cooling picture. The local nature of sensor data can also be misinterpreted and misused. Without novel methods to interpret and visualize temperatures obtained by prediction or measurement, data center operators cannot easily identify urgent local cooling issues or quickly examine the temperature at other location. This paper presents methods to predict a full three-dimensional temperature field in data centers from a limited number of measurement points. Several different statistical interpolating schemes are discussed. We also validate the interpolated temperature fields against benchmark data from Computation Fluid Dynamics (CFD) and show good agreement.

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