A Rack-Level Cooling Redundancy Metric for Data Center Applications

2011 ◽  
Author(s):  
James W. VanGilder ◽  
Christopher M. Healey
Keyword(s):  
Power Systems ◽  
Data Center ◽  
Inlet Temperature ◽  
Cooling Systems ◽  
Cooling Performance ◽  
System Failures ◽  
Important Measure ◽  
Data Center Cooling ◽  
General Data ◽  
Local Nature

Redundancy is an important measure of an operation’s ability to withstand planned or unplanned system failures. While this concept is commonly used in power systems, redundancy can be extended to data center cooling systems, as well. We propose a rack-based redundancy metric for cooling performance that is similar in nomenclature to metrics for power systems, but also captures the local nature of data center cooling. This paper will explain how to compute this metric for general data center layouts and show how cooling redundancy can influence design choices when used in combination with typical measures of cooling coverage: inlet temperature and Capture Index.

Analytical Modeling of Energy Consumption and Thermal Performance of Data Center Cooling Systems: From the Chip to the Environment

2007 ◽  
Author(s):  
Madhusudan Iyengar ◽  
Roger R. Schmidt
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Energy Use ◽  
It Industry ◽  
Cooling Systems ◽  
Air Temperatures ◽  
Data Center Cooling ◽  
Total Data ◽  
Heat Transfer Phenomenon

The increasingly ubiquitous nature of computer and internet usage in our society, has driven advances in semiconductor technology, server packaging, and cluster level optimizations, in the IT industry. Not surprisingly this has an impact on our societal infrastructure with respect to providing the requisite energy to fuel these power hungry machines. Cooling has been found to contribute to about a third of the total data center energy consumption, and is the focus of this study. In this paper we develop and present physics based models to allow the prediction of the energy consumption and heat transfer phenomenon in a data center. These models allow the estimation of the microprocessor junction and server inlet air temperatures for different flow and temperature conditions at various parts of the data center cooling infrastructure. For a case study example considered, the chiller energy use was the biggest fraction of about 41% and also the most inefficient. The room air conditioning was the second largest energy component and also the second most inefficient. A sensitivity analysis of plant and chiller energy efficiency with chiller set point temperature and outdoor air conditions is also presented.

Principles of loop thermosyphon and its application in data center cooling systems: A review

2021 ◽  
Vol 150 ◽  
pp. 111389
Author(s):  
Tao Ding ◽  
Xiaoxuan Chen ◽  
Hanwen Cao ◽  
Zhiguang He ◽  
Jianmin Wang ◽  
...  
Keyword(s):  
Data Center ◽  
Cooling Systems ◽  
Data Center Cooling

Data Center Cooling Prediction Using Artificial Neural Network

2007 ◽  
Author(s):  
Saurabh K. Shrivastava ◽  
James W. VanGilder ◽  
Bahgat G. Sammakia
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Center ◽  
Cooling Performance ◽  
The Real ◽  
Time Prediction ◽  
Linear Relationships ◽  
The Neural Network ◽  
Data Center Cooling ◽  
Computational Fluid Dynamics Cfd

An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.

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