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Research Papers

Experimentally Validated Computational Fluid Dynamics Model for a Data Center With Cold Aisle Containment

[+] Author and Article Information
Sami A. Alkharabsheh

Department of Mechanical Engineering,
Binghamton University,
State University of New York,
Binghamton, NY 13902
e-mail: salkhar1@binghamton.edu

Bahgat G. Sammakia

Department of Mechanical Engineering,
Binghamton University,
State University of New York,
Binghamton, NY 13902

Saurabh K. Shrivastava

PANDUIT Corporation,
Panduit Dr.,
Tinley Park, IL 60487

1Corresponding author.

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received February 11, 2014; final manuscript received December 5, 2014; published online January 21, 2015. Assoc. Editor: Madhusudan Iyengar.

J. Electron. Packag 137(2), 021010 (Jun 01, 2015) (9 pages) Paper No: EP-14-1016; doi: 10.1115/1.4029344 History: Received February 11, 2014; Revised December 05, 2014; Online January 21, 2015

This paper presents the results of an experimentally validated computational fluid dynamics (CFD) model for a data center with fully implemented fan curves on both the servers and the computer room air conditioner (CRAC). Both open and contained cold aisle systems are considered in this study. This work is divided into sections for the baseline system (prior to installing containment) calibration and the fully contained cold aisle system calibration and leakage characterization. In the open system, the fan curve of the CRAC unit is extracted from the manufacturer data, while the fan curve of the load banks is obtained through experimental measurements. The experimental results are found to be in good agreement with the average model predictions. In the fully contained cold aisle system, a detailed containment CFD model is developed based on experimental measurements. The model is validated by comparing the flow rate through the perforated floor tiles and the rack inlet temperatures with the experimental measurements. The CFD results are found to be in good agreement with the experimental data with an average relative error between the measured and computed flow rate of approximately 6.7%. Temperature measurements are used to calibrate the sources of leakage in the containment and rack mounting rails. The temperature measurements and the CFD results agree well with an average difference of less than 1 °C. This study provides important modeling guidelines for data centers. In order to predict the performance of contained cold aisle systems flow distribution, it is crucial that physics based models of fan curves, server internal resistances, detailed rack models, and other design details are all accurate and experimentally verified.

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Figures

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Fig. 2

Detailed experimental measurements per tile at 100% CRAC fan speed. This data are used for airflow calibration in the numerical model.

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Fig. 1

Modular data center: (a) 3D view and (b) top view

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Fig. 3

Calibrated CRAC operating point and fan characteristics curve. The internal resistance simulating the internal components of a CRAC is modeled based on the manufacturer data.

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Fig. 4

(a) Server simulator (load bank) and (b) measured fan curves used in the numerical model. The experimental measurements take into account the internal resistance of the load bank without the need to simulate the internal resistance in the numerical model.

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Fig. 5

Schematic of (a) detailed CAC model and (b) detailed rack model

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Fig. 9

The inlet temperatures at different elevations of the rack using simulations. The rise in inlet temperature as a function of elevation in the cold aisle is due to cold air escaping the cold aisle at low elevations, and hot air penetrating at the upper portion of the cold aisle. Due to fixed temperature at the lower part of the racks, a broken y-axis is used between 0.8 and 1.8 m.

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Fig. 6

Total CRAC flow rate at different load banks internal resistance. It is noteworthy that for open systems increasing the internal resistance has little impact on the flow rate, but for contained systems, a significant drop in flow rate is observed as the resistance is increased.

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Fig. 7

Experimental measurements per tile at 60% CRAC fans speed for CAC system. This data are used for CAC system validation.

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Fig. 8

Comparison between the measured inlet temperatures and the detailed model. The average temperature difference between the simulations and the measurements is 0.99 °C.

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Fig. 10

Comparison between the measured inlet temperatures and the simplified model. The average temperature difference between the simulations and the measurements is 1.32 °C.

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Fig. 14

The effect of leakage area ratio on the airflow rate through leakage. The airflow that leaks from the leakage locations increases as the leakage area ratio increases. While the leakage airflow rate is constant above a certain ratio, the leakage airflow rate through doors and ceiling varies which explains the change in temperature.

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Fig. 11

The effect of varying the containment leakage on the total static pressure and total flow rate. After certain leakage area ratio, the CAC system behaves in a similar manner as an uncontained cold aisle system.

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Fig. 12

Effect of the leakage variation on the inlet temperature. At small leakage area ratios, the increase in temperature is compensated by the increase in the cooling flow rate. At high leakage area ratios, the cooling flow rate becomes steady and the hot air leakage increases.

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Fig. 13

The effect of leakage on the flow rate of rack 7/9 RU. The flow rate increases due to the decrease in the static pressure, however, this does not indicate better cooling because of the mixing with the leaking hot air at high leakage area ratios.

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