Fundamentals of CFD and Data Center Cooling Amir Radmehr, Ph.D. Innovative Research, Inc.

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1 Minneapolis Symposium September 30 th, 2015 Fundamentals of CFD and Data Center Cooling Amir Radmehr, Ph.D. Innovative Research, Inc.

2 Learning Objectives 1. Gain familiarity with Computational Fluid Dynamics (CFD) technique, for simulating the cooling performance of data centers. 2. Understand the factors that influence the airflow motion in the data center and the parameters that affect the cooling of the equipment. 3. Be able to apply simple and cost-effective remedies to eliminate hot spots and control mechanical cooling energy usage. 4. Understand how airflow simulation can be used to design highly efficient (green) data centers. The energy consumption and operating cost of such data centers are substantially less than the traditional data centers.

3 What is CFD CFD (Computational Fluid Dynamics) is a method of calculating, in great detail, flow fields of any complexity. It gives you the velocity components, pressure, temperature, and other variables at every point in the flow domain. CFD involves placing a large number of points in the domain, setting up equations for the flow variables at these points, and solving these (thousands or millions of) equations. CFD is commonly used in aerospace and automotive applications, combustion chambers, electronics cooling, and chemical industries.

4 CFD Procedure for Data Centers Construct a computer prototype of the data center Calculate the airflow and temperature distributions Visualize the results Make changes to the model to optimize cooling Implement changes to improve the cooling of the data center, save energy, and reduce operating cost

5 Ballistic Trajectory of a Projectile

6 CFD Simulation in a Data Center

7 Navier Stokes Equations

8 CFD Grid Grid for a simple geometry Grid for a complex geometry Gas Turbine Blades

9 Sample CFD Model for a Data Center

10 Required Data Shape and size of the data center Raised floor and ceiling heights Location and type of cooling units Location and open area of perforated tiles Location and open area of cutouts Location and size of under-floor obstructions Location, orientation, heat load, and airflow of racks Location and size of above-floor obstructions

11 Results Airflow from perforated tiles Pressure distribution under floor Racks inlet and exhaust temperatures Temperature and airflow patterns in the room CRAC units return temperature

12 How Accurate Are Our Calculations?

13 How Accurate Are Our Calculations?

14 Validation of Results NCEP Data Center Bethesda, MD The measurements were conducted by Dr. Roger Schmidt of IBM and were presented at the 2004 ASHRAE meeting in Nashville, TN

15 Airflow Rates

16 Rack Inlet Temperatures

17 CRAC Return Temperatures

18 Improve Cooling in an Existing Data Center Model the data center in its current state Identify the causes of cooling problems Model modified layouts Implement changes Prevent equipment failure, save energy, reduce operating cost

19 Design Highly Efficient Data Centers Make the right decision about Raised floor height Location of the CRAC units Use of return plenum Use of supplemental cooling (overhead ducts, In-row coolers) Layout of the racks Layout of pipes and cable trays Etc.

20 The Cause of Flow Maldistribution

21 Initial Validation Measurements by Dr. Roger Schmidt of IBM in a Poughkeepsie data center.

22 Maldistribution Revisited

23 Effect of Plenum Height

24 Effect of Tile Open Area

25 Use of Perforated Partitions Proposed Locations for Perforated Partitions

26 Perforated Partitions (80% and 65%)

27 Above-Floor Two-Part Simulation Strategy Under-Floor Simulation Above-Floor Simulation

28 A Simple Layout

29 Rack Inlet Temperatures Heat Load = 88 kw Rack Demand = 10,406 CFM CRAC Flow = 7,500 CFM

30 Hot Air Recirculation

31 Increased Cooling Airflow Heat Load = 88 kw Rack Demand = 10,406 CFM CRAC Flow = 10,000 CFM

32 Rack Inlet Temperatures

33 Side Recirculation (End Effect)

34 Create an Air Curtain

35 Perf Tiles for the Air Curtain

36 Rack Inlet Temperatures

37 Effect of the Air Curtain

38 Partitions at the Ends of the Cold Aisle Heat Load = 88 kw Rack Demand = 10,406 CFM CRAC Flow = 10,000 CFM

39 Rack Inlet Temperatures

40 Placement of Partitions

41 Case Study Simple Solutions for a Complex Problem Heat Load = 720 kw Racks Demand = 32,000 CFM CRACs Flow = 35,000 CFM

42 Case Study Simple Solutions for a Complex Problem

43 Airflow Rates from Perforated Tiles

44 Under-Floor Pressure and Velocity

45 Airflow Demand vs. Airflow Supplied

46 Rack Inlet Temperature Distribution

47 Temperature Distribution at Vertical Planes

48 Temperature Distribution at 5.5 ft.

49 Airflow Streams

50 Airflow Rates Total Airflow Demand = 32,000 CFM Total Airflow Supplied= 35,000 CFM

51 Modification (Level 1)

52 Airflow Rates from Perforated Tiles

53 Rack Inlet Temperature Distribution

54 Temperature Distribution at 5.5 ft.

55 Modification (Level 2)

56 Rack Inlet Temperature Distribution

57 Temperature Distribution at 5.5 ft.

58 Alternative Modification (Level 2)

59 Airflow Streams

60 Temperature Distribution at 5.5 ft.

61 Rack Inlet Temperature Distribution

62 Closing Remarks CFD can be used to simulate the airflow motion and temperature distribution in data centers. Using CFD, the cooling performance of existing data centers can be improved and cooling design of new data centers can be optimized. The accuracy of the CFD results depends on the accuracy of the data provided. In raised-floor data centers, higher plenum height and more restrictive perforated tiles result in more uniform airflow distribution. Simple and cost effective remedies such as closing cable openings or selective use of partitions can tremendously improve the cooling performance of a data center.

Using CFD Analysis to Predict Cooling System Performance in Data Centers Ben Steinberg, P.E. Staff Applications Engineer

Using CFD Analysis to Predict Cooling System Performance in Data Centers Ben Steinberg, P.E. Staff Applications Engineer Agenda What is CFD? Applications of CFD Applicability to the Data Center How it