Dynamic Reduced-order Model for the Air Temperature Field Inside a Data Center

G.W. Woodruff School of Mechanical EngineeringGeorgia Institute of Technology

Atlanta, GA 30332-0405

Rajat Ghosh and Yogendra Joshi

2

Project Objective

• Development of experimentally validated reduced order modeling framework for dynamic energy usage optimization of data centers and telecomsDynamic reduced order modeling framework development Experimental validation of dynamic reduced order

modeling framework • Implementation and generalization of modeling

approach in data centers and telecom test sitesAssessment and refinement of approach at a selected

facilityDevelopment of data center thermal design software

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Accomplishments

• Developed a CFD/HT model for predicting transient temperature field

• Developed an experimental setup for measuring transient temperature field

• Utilizing a reduced-order model to generate new temperature data from an existing temperature ensemble obtained from CFD/HT simulations or experiments

Modeling Algorithm

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POD coefficient calculation

.1 ;,

max 2

2

2

T

)().,,(

),,(

1tbzyx

zyxT) T(x,y,z;t

i

nk

ii

Interpolation

Ensemble generation

POD mode calculation

Number of principal components determination

nmT

CFD/HT simulation

),,( zyxi

)(tbi

k

99%

1

1

n

ii

k

ii

Reduced-order temperature computation

Error estimation

n

kiiO

1)(~Error

5

Case Study for CFD/HT Simulation

900

1016

609

5082

CR

AC

CR

AC

A1

A2

A3

A4

B4

B3

B2

B1

Cold aisle

Hot aisle

Row

XY

3000

455830

0020

003860

X

Z

Row B

Row A

121

8

Plenum

CR

AC

Adiabatic Symmetry plane

Insulated room wall

•Initial condition- T(x, y, z; t=0)=150C- V(x, y, z; t=0)=0

•Heat load/ rack= 5 KW• Air flow rate from CRAC= 5500 CFM

•Grid Size- 182,000- Adaptive meshingWith hexagonal grid-cells

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Row Inlet at a Known Time (t=30s)

2.5 3 3.5 4 4.5

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POD model can reproduce CFD/HT data accurately

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-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

Row A inlet

POD temp. Field CFD temp. field Deviation~1%Velocity field

Row B inlet

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Temperature at an Intermediate Instant (t=15 s)

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-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

• POD based model can efficiently generate temperature data at t=15 s from existing CFD/HT temperature ensemble, obviating need for independent

simulation

X

Z

POD temp. field~4 s

CFD temp. field~8 min Deviation~1%

Row A inlet

Row B inlet

8

Experimental Validation

12700 CFM CRAC unit

14 kW racks

• Parameters -Eight 14 kW racks arranged symmetrically about cold aisle

-CFM from CRAC unit=12700

•Transient ConditionSudden shutdown of CRAC unit for 2 min

-Observe following transient temperature field at cold aisle for 200 s at 10 s interval

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Temperature Measurement at Rack A Inlet

t=0 s t=30 s

t=60 s t=90 s

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Z

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Validation of POD based Interpolation

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POD temperature field ~4s Experimental temperature field ~ 3 min

Error between POD and Experimental temperature

field~1%

•Temperature data at t=45 s are not included in original temperature ensemble generated by experiments•POD based model can generate temperature data at t=45 s from existing temperature ensemble generated by experiments, , obviating need for independent experiment•POD based model is significantly faster than experiments without compromising accuracy

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Validation of POD-based Extrapolation

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POD temperature field ~4s Experimental temperature field ~ 6 min

Error between POD and Experimental temperature

field~1%

•Temperature data at t=205 s are outside the temperature range t=0-200 s•POD based model can generate temperature data at t=205 s from existing experimental observations, obviating need for independent experiment•POD based model is significantly faster than experiments without compromising accuracy

12

Publication/ Presentation

• Conference Proceedings

Ghosh, R., and Joshi, Y., 2011,”Dynamic Reduced Order Thermal Modeling of Data Center Air Temperature”, ASME InterPack 2011 Conference

• Poster Presentation

Ghosh, R., and Joshi, Y., 2010 " Dynamic Reduced Order Modeling of Convective transports in Data Centers" at NSF I/UCRC meeting

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Plan for Next Quarter

• Refining POD based model – Designing more representative experiments with

distributed temperature measuring facility• Capable of measuring instantaneous room level

temperature field

• Developing thermal design software for data centers

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Acknowledgement

We acknowledge support for this work from IBM Corporation as a sub-contract on

Department of Energy funds