dynamic reduced-order model for the air temperature field inside a data center g.w. woodruff school...
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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
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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
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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
0.2
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0.8
1
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2.5 3 3.5 4 4.5
0.2
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1.8
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
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0.06
X
Z
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POD model can reproduce CFD/HT data accurately
2.5 3 3.5 4 4.5
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1.1
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2.5 3 3.5 4 4.5
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2.5 3 3.5 4 4.5
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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)
2.5 3 3.5 4 4.5
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-0.1
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-0.06
-0.04
-0.02
0
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0.04
0.06
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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
• 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
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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
X
Z
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-1
-0.5
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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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-1.5
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-0.5
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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
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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