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Research Papers

A Comparison of Parametric and Multivariable Optimization Techniques in a Raised-Floor Data Center

[+] Author and Article Information
Srinarayana Nagarathinam

Post-doctoral Fellow

Babak Fakhim

PhD Student

Masud Behnia

Professor
e-mail: masud.behnia@sydney.edu.au

Steve Armfield

Professor School of Aerospace,
Mechanical and Mechatronic Engineering,
The University of Sydney,
Sydney, New South Wales 2006, Australia

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received December 28, 2011; final manuscript received November 12, 2012; published online July 24, 2013. Assoc. Editor: Saurabh Shrivastava.

J. Electron. Packag 135(3), 030905 (Jul 24, 2013) (8 pages) Paper No: EP-11-1103; doi: 10.1115/1.4023214 History: Received December 28, 2011; Revised November 12, 2012

It is well known that the flow distribution in data centers can be effected by a variety of parameters such as rack and computer room air conditioning (CRAC) positions, raised-floor height, ceiling height, and percentage opening of perforated tiles. In the present paper, numerical simulations are conducted to optimize the layout of a raised-floor data center with respect to these parameters. Two different approaches have been used: parametric optimization; and a multivariable approach using response surface optimization. In the parametric optimization procedure, the data center is optimized with respect to the maximum temperature in the room. While in the multivariable approach, a cost function is constructed from all the rack inlet temperatures and is minimized. The results show that the multivariable approach is computationally economical and the optimized layout gives a better thermal performance compared to that of parametric optimization.

Copyright © 2013 by ASME
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References

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Figures

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Fig. 7

Variation of the maximum temperature with % opening of perforated tile

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Fig. 6

Pressure field just below the raised-floor

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Fig. 5

Variation in perforated tile flow rates

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Fig. 4

Temperature field of data center base layout in XZ- and XY-planes

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Fig. 3

Variation of the maximum temperature in the data center with grid size

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Fig. 2

A schematic of the data center layout

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Fig. 1

Hot-aisle/cold-aisle configuration

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Fig. 8

Variation of the standard deviation of perforated tile flow rate with % opening of perforated tile

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Fig. 9

Variation of the standard deviation of perforated tile flow rate with raised-floor height

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Fig. 10

Variation of the maximum temperature with raised-floor height

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Fig. 13

Comparison of pressure distribution between the symmetric and optimum CRAC positions just below the raised-floor

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Fig. 15

Multivariable optimization cost function scenarios

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Fig. 11

Variation of the maximum temperature with ceiling height

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Fig. 12

Temperature field at the top of the racks for three different ceiling heights

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Fig. 14

Comparison of pressure distribution just below the raised-floor for CRACs with and without turning vanes

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