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RESEARCH PAPERS

An Approach to Robust Design of Turbulent Convective Systems

[+] Author and Article Information
Nathan Rolander, Jeffrey Rambo, Yogendra Joshi, Janet K. Allen

The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405

Farrokh Mistree1

The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405farrokh.mistree@me.gatech.edu

The Uptime Institute, 2004, “Heat Density Trends in Data Processing, Computer Systems and Telecommunications Equipment,” http://www.upsite.com/TU1pages/tuiwhite.html, accessed on 2/16 2004.

Lawrence Berkeley National Laboratory and Rumsey Engineers, 2003, “Data Center Energy Benchmarking Case Study,” http://datacenters.lbl.gov/ accessed on 11/20 2003.

Single Intel P4 2.4-GHz processor with 2GB of RAM.

In literature this equation is often of the form y=f(x,z), however, in this application there are no noise variables (z)

1

Corresponding author.

J. Mech. Des 128(4), 844-855 (Jan 18, 2006) (12 pages) doi:10.1115/1.2202882 History: Received September 15, 2005; Revised January 18, 2006

The complex turbulent flow regimes encountered in many thermal-fluid engineering applications have proven resistant to the effective application of systematic design because of the computational expense of model evaluation and the inherent variability of turbulent systems. In this paper the integration of a novel reduced order turbulent convection modeling approach based upon the proper orthogonal decomposition technique with the application of robust design principles implemented using the compromise decision support problem is investigated as an effective design approach for this domain. In the example application considered, thermally efficient computer server cabinet configurations that are insensitive to variations in operating conditions are determined. The computer servers are cooled by turbulent convection and have unsteady heat generation and cooling air flows, yielding substantial variability, yet have some of the most stringent operational requirements of any engineering system. Results of the application of this approach to an enclosed cabinet example show that the resulting robust thermally efficient configurations are capable of dissipating up to a 50% greater heat load and a 60% decrease in the temperature variability using the same cooling infrastructure.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Requirements, constructs, and integration for a robust server cabinet design approach

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Figure 2

y distance regression vs orthogonal distance regression visualization

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Figure 3

Type-II robust design (a) goals and (b) constraints representation

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Figure 9

Cabinet chip temperature variability for optimal to robust design objectives

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Figure 4

Cabinet configuration and variables

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Figure 5

Cabinet (a) velocity field (b) chip temperature profile

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Figure 6

Server cabinet system model diagram

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Figure 7

(a) Inlet air velocity and power distribution (b) maximum chip temperature and bounds vs total cabinet power

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Figure 8

Pareto frontiers with changing weighting

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