Energy Efficient Data Center Design. Can Ozcan Ozen Engineering Emre Türköz Ozen Engineering

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1 Energy Efficient Data Center Design Can Ozcan Ozen Engineering Emre Türköz Ozen Engineering 1

2 Bio Can Ozcan received his Master of Science in Mechanical Engineering from Bogazici University of Turkey in 2005, which involved fracture mechanics of thin films using Ansys Mechanical software. Mr. Ozcan has been involved in CAE consulting and technical support using Ansys software for 6 years with Ozen Engineering in Silicon Valley; specifically working on Applied Mechanics problems. Mr. Ozcan has presented at international conferences in Europe and America and has published articles on various simulation related topics. Emre Turkoz is studying Mechanical Engineering at Bogazici University, Istanbul, Turkey. He is working part-time in the CFD/FMS Lab at the university, dealing mainly with modelling complicated flow problems using particle tracking methods. He s also participating in active research about Domain Decomposition and High Performance Computing algorithm development. 2

3 Ozen Engineering Inc We are the local ANSYS channel partner With over 25 years of experience in Finite Elements Simulations and Engineering Consulting, we collaborate with customers to provide the best in class expertise and solutions to their problems, enabling them to succeed. 3

4 Main Guides Build the Best Data Facility for Your Business, by Dougles Alger The Dell Smart Solution Advisor for data centers 4

5 Air Handler Placement Options Having airflow between the corridors is desired for efficient cooling (Air Handler C) 5

9 m 2 # cabinets = 78 Area per cabinet = 2.

6 Typical Data Center Layouts Larger and smaller data center layouts Area= 2270 ft 2 = m 2 # cabinets = 78 Area per cabinet = 2.7 m 2 Grid edge length ~= 0.6 m Area= 1301 ft 2 = m 2 # cabinets = 36 Area per cabinet = 3.4 m 2 Grid edge length ~= 0.6 m 6

7 Detailed Data Center HVAC Analysis Each unit can be examined in more details by dividing into sub-divisions 7

8 Data Center Cooling Process Overhead or Underfloor? Air handlers circulate air within the Data Center, drawing in warm air from the space between the floor and ceiling and discharging cold air into the room's plenum. (This occurs typically below the raised floor if a Data Center has one, and above the false ceiling if it doesn't.) Air is cooled within the handler by passing over coils containing chilled liquid think of wind blowing across a block of ice and then expelled into the Data Center. The coils are generally maintained at about 43 or 44 Fahrenheit (6.1 or 6.7 Celsius). 8

9 Overhead or Underfloor? Underfloor is harder to install, might need a ramp at the entrance, more expensive Overhead is easier and much cheaper to install. But, It is significantly more difficult to cool a server environment by pushing cold air downward So, if our concern is the energy-efficiency, we should simulate an underfloor simulation. 9

10 Underfloor Cooling Underfloor is preferred for energy efficient cooling 10

11 Cooling Quantities 1 ton of cooling = BTUs 1 Watt = 3.41 BTUs -> 1 ton of cooling ~= 3.5 kw Watts per square foot/meter => a measure of how much energy is used in the data center. 11

Simulation Setup For the design, we choose underfloor configuration, i.e. air vent openings will be distributed on the floor Air Handler units will be assigned as outlets Every cabinet/rack will generate 10 kw heat with 0,85 m 3 /s flow rate.

12 Simulation Setup For the design, we choose underfloor configuration, i.e. air vent openings will be distributed on the floor Air Handler units will be assigned as outlets Every cabinet/rack will generate 10 kw heat with 0,85 m 3 /s flow rate. [5] Server environment should be kept between 18 and 23 degrees Celcius. AC unit inlet temperature is between 10 and 17 degrees Celcius. AC unit volumetric flow rate ~8 m 3 /s. 12

$Inlet turbulence fraction intensity: 0.30 Inlet eddy length scale: 0.$

13 Solver Settings Values taken from the previous analysis: Heat flux from Walls: adiabatic Fluid Material: Air at 25 degrees Celcius Buoyancy Model: Buoyant Turbulence Model: Shear Stress Transport Radiation: Off Inlet turbulence fraction intensity: 0.30 Inlet eddy length scale: 0.1 m 13

Geometry for CFD Domain Geometry Details room dimensions (width)x(length)x(height) : 4800mm x 9000mm x 3800mm rack dimensions (width)x(length)x(height) : 1200mm x 600mm x 2100mm air handler

14 Geometry for CFD Domain Geometry Details room dimensions (width)x(length)x(height) : 4800mm x 9000mm x 3800mm rack dimensions (width)x(length)x(height) : 1200mm x 600mm x 2100mm air handler dimensions (width)x(length)x(height): 3000mm x 1200mm x 1600mm # of rows of cabinets: 2 # of Cabinets: 8 per each row (10kW each) Floor vent tile openings are modeled as a porous domain with directional loss coefficients plenum 14

15 Problem Setup Boundary Conditions applied on the problem Air Handling Unit (Outlet) Service Racks (8 cabinets, 10kW each) 15 [image textured]

16 Problem Setup Boundary Conditions applied on the problem Air Handling Unit (Outlet 0 pressure) 8.0 m 3 /s flow rate Underfloor Cooling Vent Tile Openings (Porous Domain) 16 [image textured]

17 Problem Setup Boundary Conditions applied on the problem Air Handling Unit (Outlet 0 pressure) 8.0 m 3 /s flow rate Underfloor Air Handling Unit 17C 17 [image textured]

18 Problem Setup Vent tile openings are modeled as porous domain with directional losses [4] Server Room (Lower Pressure wrt. Plenum, pressure drop due to air grill structure) Air Grill (Porous with Loss Coefficient) Plenum (Higher Pressure at Air Inlet Side) 18

Optimization for Energy Efficiency We would like to have efficient cooling by finding the right grill sizing and putting correct air deflectors in the room (of

19 Optimization for Energy Efficiency We would like to have efficient cooling by finding the right grill sizing and putting correct air deflectors in the room (of course other/more parameters can be used depending on the need ) Input Parameters Grill Free Area Ratio Deflector Length Deflector Height Deflector Position 19

20 Optimization for Energy Efficiency We would like to have efficient cooling by finding the right grill sizing and putting correct air deflectors in the room (of course other/more parameters can be used depending on the need ) Output Parameters Rack Temperature Temperature Uniformity 20

Response Surfaces Optimization Design Explorer is employed for performing response surface based optimization Initial DOE generated by Optimal Space Filling algorithm which is suitable for nonlinear

21 Response Surfaces Optimization Design Explorer is employed for performing response surface based optimization Initial DOE generated by Optimal Space Filling algorithm which is suitable for nonlinear response Non-parametric regression algorithm is employed for response surface MOGA (Multi Objective Genetic Algorithm) is used for performing optimization Optimal design candidates are verified for accuracy 21

22 Effect Of Vent Tile Free Area Ratio Air vent tile free area ratio has around minimum 22

23 Effect Of Air Deflector Position Deflector position is found out to be optimum between 3 mto 4m 23

24 Effect Of Air Deflector Length & Height Deflector length should be small Deflector height should be large 24

25 Optimization - TradeOff Pareto frontier surface is useful to determine for multi-objective optimization We would like to minimize both Rack Average Temperatures and Standard Deviation for uniform cooling 25

26 Design Improvements As a result of the parametric optimization study, we were able to improve cooling conditions as outlined below 26 Initial Design Optimized Design

27 Design Improvements As a result of the parametric optimization study, we were able to improve cooling conditions as outlined below 27 Initial Design Optimized Design

28 Design Improvements As a result of the parametric optimization study, we were able to improve cooling conditions as outlined below 28 Initial Design Optimized Design

29 Velocity Vectors Velocity vectors are plotted to see how flow distributes over the grills and around the server room 29 [image textured]

30 Ansys CFX can be used to model thermal-flow conditions of data centers Ansys Design Explorer has robust tools to make parametric design space exploration and optimization based on multiple models Data centers can be designed as energy efficient and kept as energy efficient using simulation technology as needs change Conclusions 30

31 References *1+ U. Singh et.al. CFD-Based Operational Thermal Efficiency Improvement of a Production Data Center [2] S.V. Patankar Computational Modeling of Airflow in Raised-Floor Data Centers *3+ C.D. Patel et. al. Thermal Consideration in Cooling Large Scale High Compute Density Data Centers, EICTT Conference 2002 CA USA [4] Handbook of Hydraulic Resistance 3rd Edition I. E. Idelchik, CRC Begell House 1994 [5] ype=pdf 31

32 QUESTIONS? Thank you for your attention Please join us for Happy Hour following the conference. Ozen Engineering Inc: 1210 E. Arques Ave #207 Sunnyvale, CA (408) Directions: Right on Tasman Left on Lawrence Left on Arques Ave. Second Right into OEI parking lot. 32

Topology Optimization in Fluid Dynamics

A Methodology for Topology Optimization in Fluid Dynamics 1 Chris Cowan Ozen Engineering, Inc. 1210 E. Arques Ave, Suite 207 Sunnyvale, CA 94085 info@ozeninc.com Ozen Engineering Inc. We are your local