Energy Efficient Thermal Management for Information Technology Infrastructure Facilities - The Data Center Challenges

Transcription

1 Energy Efficient Thermal Management for Information Technology Infrastructure Facilities - The Data Center Challenges Yogendra Joshi G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332

2 Multi-scale Nature of Data Center Thermal Problems Macro-scale covers O(105) length scales 2m 35 mm ~0.6 m ~10+ m aisle cabinet server chip dm m cm mm

3 Electronics coolers Facility Designers Heat rejection Heat generation within devices and interconnects System level optimized solutions require consideration of all length

4 Total Power = 580 kw HVAC Power Consumption All values are shown as a fraction of the respective data center total power consumption. 36% 29% Computer Loads 38% 54% Worst Case "HVAC Efficiency" 50% 31% Data Center % Best Case "HVAC Efficiency" 35% UPS Losses 6% HVAC 54% 32% 31% Lighting 2% 25% 22% Computer Loads 63% Total Power = 1700 kw Data Center Facility No. - Data Center No. Message: Energy efficiency is key to reducing operation costs and saving environment Source: W. Tschudi, Lawrence Berkeley Laboratories UPS Losses 13% Lighting 1% HVAC Chilled Water Plant 14% HVAC - Air Movement 9%

5 70 Cabinet Heat Loads and Other Trends Heat Load in KW Per Rack 60 Compute Servers - 1U, Blade, Custom Communication - Extreme Density IBM eserver BladeCentert 50 Increase in processor speeds demands for miniaturization chip level heat fluxes CRAC units: ~25 years 10 Computing hardware: ~2 *years Year of Product Announcement Lifecycle Mismatch *ASHRAE, Datacom Equipment Power Trends and Cooling Applications

6 a b GSI Chip d e c f g Cooling liquid a. cover-plate or overcoat b: microchannel c: optical device d: thermal-fluidic pipe e: optical or dual-mode pillar f: electrical pillar g: solder coating or conductive adhesive Next Generation Integrated I/Os: Electrical, Optical, and Thermal-fluidic Polymer pillars Overcoat Channel Si B. Dang, Ph.D. Dissertation, GIT, 2006; B. Dang, et al., IITC 2005; InterPACK, 2005 Micropipes

7 Technical Principles Reduced Order Modeling Robust Design

8 Reduced Order Models Level of Description Accurate but too large for (most) available analysis and design tools Model Reduction System Identification Reduced-Order Models Compact Models CFD /HT Simulations The Desired Compromise: Sufficiently accurate and within scope of most analysis and design tools Fits into available tools but lack Lumped Parameter Models predictive capabilities Model Size (DOF) < , ,000

9 Y Robust Design Response Deviation at Optimal Solution Deviation at Robust Solution Objective Function Optimal Solution 2 x Robust Solution Design Variable Optimization minimizes the function of expected response Robust solution also minimizes variation in the response * X *N. Rolander, MS Thesis, ME, GTech, 2005.

10 State-of-the-Art Limited Interconnected Reduced Order Models Multi-objective Optimization of Subsystems

11 Connect component-level ROMs lumped pressure models ROM of pressure fields Couple with Flow Network Modeling (SIMPLE algorithm) Interfaces mass fluxes error ~3% Discontinuities at component interfaces

12 max Point sum(q j ) Point Point Conventional MOGA New Approach min max(t j ) Point max Q Point Q Q T T T 1000 sum(q j ) Point min max(t j ) MultiObjective Optimization of Cabinets 90

13 Multi-scale Modeling Future Goals Reduced-Order Model (ROM) Integrate ROMs into full scale CFD/HT computations Efficiently bridge length scales: Vary model fidelity at different length scales Do not smallest feature size dictate number of model DOF

14 Energy Efficient Thermal Design of Data Centers Data Center Thermal Analysis Global Cooling Scheme Facility Layout Local / Supplemental Cooling Rack-Level Cooling Solutions Energy efficiency Whole building considerations, path to environment Rack Modeling Liquid Cooling Thermal Performance Metrics Locating HighPowered Racks Lifecycle Analysis CRAC Modeling Dynamic Control Alternative Supply / Return Schemes Raised Floor Plenum Modeling Control Perforated Tile Flow Distribution Plenum Obstructions

15 Barriers Multi-scale modeling of turbulent flows Handshaking Compact models Experimental validation What to validate Metrology Energy savings metrics Industry barriers

16 Recommendations 1. Multi-scale simulation methodologies 2. Reduced order model development 3. Experimental validation of transport in high heat flux facilities 4. Multi-objective design under uncertainty 5. Control of multi-scale thermal systems

17 References Lawrence Berkeley National Laboratory and Rumsey Engineers, 2003, "Data Center Energy Benchmarking Case Study", accessed on 11/ Modeling of Data Center Airflow and Heat Transfer: State of the Art and Future Trends, J. Rambo and Y. Joshi, Distributed and Parallel Database, Special Issue on High Density Data Centers, 2007 (to appear) Optimizing Thermal Design of Data Center Cabinets with a New Multi-Objective Genetic Algorithm, G. Li, M. Li, S. Azarm, J. Rambo and Y. Joshi, Distributed and Parallel Database, Special Issue on High Density Data Centers, 2007 (to appear) An Approach for Robust Design of Turbulent Convective Systems, N. Rolander, J. Rambo, Y. Joshi, J.K. Allen, and F. Mistree, ASME J. Mechanical Design, Vol. 128, pp , Reduced-Order Modeling Of Turbulent Flows In Multiscale Domains, J. Rambo and Y. Joshi, 13th International Heat Transfer Conference, Sydney, Australia, August, ASHRAE, Datacom Equipment Power Trends and Cooling Applications. 2005, Atlanta: American Society of Heating, Refrigeration and Air-Conditioning Engineers Atlanta.