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Experimentally Validated Computational Fluid Dynamics Model for Data Center with Active Tiles

[+] Author and Article Information
Jayati Athavale

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia - 30332, USA
jayati@gatech.edu

Yogendra Joshi

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia - 30332, USA
yogendra.joshi@me.gatech.edu

Minami Yoda

George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia - 30332, USA
minami.yoda@me.gatech.edu

1Corresponding author.

ASME doi:10.1115/1.4039025 History: Received September 18, 2017; Revised November 29, 2017

Abstract

This paper presents an experimentally validated room-level computational fluid dynamics (CFD) model for raised-floor data center configurations employing active tiles. Active tiles are perforated floor tiles with integrated fans, which increase the local volume flow rate by redistributing the cold air supplied by the computer room air conditioning (CRAC) unit to the under-floor plenum. The numerical model of the data center room consists of one cold aisle with 12 racks arranged on both sides and three CRAC units sited around the periphery of the room. The commercial CFD software package Future Facilities 6SigmaDCX is used to develop the model for three configurations: (a) an aisle populated with 10 (i.e., all) passive tiles (b) a single active tile and 9 passive tiles in the cold aisle (c) an aisle populated with all active tiles. The predictions from the CFD model are found to be in good agreement with the experimental data, with an average discrepancy between the measured and computed values for total flow rate and rack inlet temperature less than 4% and 1.7 ?, respectively. The validated models were then used to simulate steady state and transient scenarios following cooling failure. This physics-based and experimentally validated room-level model can be used for temperature and flow distributions prediction and identifying optimal number and locations of active tiles for hot spot mitigation in data centers.

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