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Dynamic Reduced-order Model for the Air Temperature Field Inside a Data Center. Rajat Ghosh and Yogendra Joshi. G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332-040 5. Project Objective.

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dynamic reduced order model for the air temperature field inside a data center

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

Rajat Ghosh and Yogendra Joshi

G.W. Woodruff School of Mechanical Engineering

Georgia Institute of Technology

Atlanta, GA 30332-0405

project objective
Project Objective
  • Development of experimentally validated reduced order modeling framework for dynamic energy usage optimization of data centers and telecoms
    • Dynamic 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 sites
    • Assessment and refinement of approach at a selected facility
    • Development of data center thermal design software
accomplishments
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
Modeling Algorithm

Ensemble generation

CFD/HT simulation

POD mode calculation

Interpolation

POD coefficient calculation

Number of principal components determination

Reduced-order temperature computation

Error estimation

case study for cfd ht simulation
Case Study for CFD/HT Simulation

4558

  • 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 meshing
    • With hexagonal grid-cells

Row B

609

Hot aisle

B3

900

B1

B2

B4

1016

CRAC

5082

3000

1218

Cold aisle

Adiabatic Symmetry plane

A3

A4

A2

A1

Insulated room wall

CRAC

Y

Row A

X

Plenum

3860

3000

2000

CRAC

Row

Z

X

row inlet at a known time t 30s
Row Inlet at a Known Time (t=30s)

Velocity field

POD temp. Field

CFD temp. field

Deviation~1%

Z

Row A inlet

X

Row B inlet

  • POD model can reproduce CFD/HT data accurately

Error~1%

temperature at an intermediate instant t 15 s
Temperature at an Intermediate Instant (t=15 s)

POD temp. field

~4 s

CFD temp. field

~8 min

Deviation~1%

Z

X

Row A inlet

Row B inlet

  • POD based model can efficiently generate temperature data at t=15 s from existing CFD/HT temperature ensemble, obviating need for independent simulation
experimental validation
Experimental Validation
  • Parameters
  • -Eight 14 kW racks arranged symmetrically about cold aisle
  • -CFM from CRAC unit=12700
  • Transient Condition
    • Sudden shutdown of CRAC unit for 2 min
    • -Observe following transient temperature field at cold aisle for 200 s at 10 s interval

14 kW racks

12700 CFM CRAC unit

validation of pod based interpolation
Validation of POD based Interpolation

Z

Error between POD and Experimental temperature field~1%

POD temperature field ~4s

Experimental temperature field ~ 3 min

X

  • 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
validation of pod based extrapolation
Validation of POD-based Extrapolation

Experimental temperature field ~ 6 min

Error between POD and Experimental temperature field~1%

POD temperature field ~4s

Z

X

  • 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
publication presentation
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

plan for next quarter
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
acknowledgement
Acknowledgement

We acknowledge support for this work from IBM Corporation as a sub-contract on Department of Energy funds

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