Experimental Analysis of Server Fan Control Strategies for Improved Data Center Air-based Thermal Management*

2020 ◽  
Author(s):  
Jeffrey Sarkinen ◽  
Rickard Brannvall ◽  
Jonas Gustafsson ◽  
Jon Summers
Keyword(s):  
Thermal Management ◽  
Experimental Analysis ◽  
Data Center ◽  
Control Strategies

scholarly journals An open source fast fluid dynamics model for data center thermal management

2021 ◽  
Vol 230 ◽  
pp. 110599
Author(s):  
Xu Han ◽  
Wei Tian ◽  
Jim VanGilder ◽  
Wangda Zuo ◽  
Cary Faulkner
Keyword(s):  
Fluid Dynamics ◽  
Open Source ◽  
Thermal Management ◽  
Data Center ◽  
Dynamics Model

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.

Exergy Analysis of Data Center Thermal Management Systems

2003 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Exergy Analysis ◽  
Thermal Control ◽  
Management Systems ◽  
World Systems ◽  
Sample Data ◽  
Potential Applications ◽  
Temperature Ranges

Data centers today contain more computing and networking equipment than ever before. As a result, a higher amount of cooling is required to maintain facilities within operable temperature ranges. Increasing amounts of resources are spent to achieve thermal control, and tremendous potential benefit lies in the optimization of the cooling process. This paper describes a study performed on data center thermal management systems using the thermodynamic concept of exergy. Specifically, an exergy analysis has been performed on sample data centers in an attempt to identify local and overall inefficiencies within thermal management systems. The development of a model using finite volume analysis has been described, and potential applications to real-world systems have been illustrated. Preliminary results suggest that such an exergy-based analysis can be a useful tool in the design and enhancement of thermal management systems.

scholarly journals Thermal Management of Electrified Propulsion System for Low-Carbon Vehicles

2020 ◽  
Vol 3 (4) ◽  
pp. 299-316
Author(s):  
Bo Li ◽  
Huang Kuo ◽  
Xuehui Wang ◽  
Yiyi Chen ◽  
Yangang Wang ◽  
...  
Keyword(s):  
Thermal Management ◽  
Control Strategies ◽  
Holistic Approach ◽  
Spray Cooling ◽  
Electric Machine ◽  
Low Carbon ◽  
Liquid Cooling ◽  
Management Techniques ◽  
And Control

AbstractAn overview of current thermal challenges in transport electrification is introduced in order to underpin the research developments and trends of recent thermal management techniques. Currently, explorations of intelligent thermal management and control strategies prevail among car manufacturers in the context of climate change and global warming impacts. Therefore, major cutting-edge systematic approaches in electrified powertrain are summarized in the first place. In particular, the important role of heating, ventilation and air-condition system (HVAC) is emphasised. The trends in developing efficient HVAC system for future electrified powertrain are analysed. Then electric machine efficiency is under spotlight which could be improved by introducing new thermal management techniques and strengthening the efforts of driveline integrations. The demanded integration efforts are expected to provide better value per volume, or more power output/torque per unit with smaller form factor. Driven by demands, major thermal issues of high-power density machines are raised including the comprehensive understanding of thermal path, and multiphysics challenges are addressed whilst embedding power electronic semiconductors, non-isotropic electromagnetic materials and thermal insulation materials. Last but not least, the present review has listed several typical cooling techniques such as liquid cooling jacket, impingement/spray cooling and immersion cooling that could be applied to facilitate the development of integrated electric machine, and a mechanic-electric-thermal holistic approach is suggested at early design phase. Conclusively, a brief summary of the emerging new cooling techniques is presented and the keys to a successful integration are concluded.

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.

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.

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.

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