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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 30332-040 5. Project Objective.

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

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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%

  7. 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

  8. 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

  9. Temperature Measurement at Rack A Inlet Z t=0 s t=30 s X t=60 s t=90 s

  10. 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

  11. 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

  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

  13. 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

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

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