Dynamic reduced order model for the air temperature field inside a data center
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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 Inside a Data Center

  • 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 Inside a Data Center

  • 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 Inside a Data Center

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 Inside a Data Center

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) Inside a Data Center

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) Inside a Data Center

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 Inside a Data Center

  • 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


Temperature measurement at rack a inlet
Temperature Measurement at Rack A Inlet Inside a Data Center

Z

t=0 s

t=30 s

X

t=60 s

t=90 s


Validation of pod based interpolation
Validation of POD based Interpolation Inside a Data Center

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 Inside a Data Center

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 Inside a Data Center

  • 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 Inside a Data Center

  • 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 Inside a Data Center

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