numerical modelling method - airah · prediction is better than cure. basics cfd modelling...
TRANSCRIPT
NUMERICAL MODELLING METHOD
FOR THE DESIGN OF
DATA CENTRE COOLING
Amin Azarmi
Mechanical Engineer | Regional Digital Champion Aurecon Group
AG
END
A • INTRODUCTION
• BASICS
• APPROACH
• RESULT
• FUTURE STUDY
• CONCLUSION
NUMERICAL MODELLING METHOD FOR THE DESIGN OF DATA CENTRE COOLING
INTRODUCTIONWHAT | WHY | HOW
This research focused• Failure occurrence for Data Center Cooling System• Analysing performance of the system
• Hot and cold aisle arrangement• Optimization of air vent tiles – Tile perforation ratio and location
46%31%
8%4% 11%
IT EQUIPMENT
HVAC COOLING & FANS
UPS
LIGHTING
OTHER
[1] ASHRAE - DATACOM EQUIPMENT POWER TRENDS AND COOLING APPLICATIONS[2] ASHRAE - BEST PRACTICES FOR DATACOM FACILITY ENERGY EFFICIENCY[3] http://www.42u.com/efficiency/energy-efficiency-calculator.htm
AVERAGE DATA CENTRE POWER ALLOCATION [2]The 2005 ASHRAE power trend chart [1]
WHAT | WHY | HOW
𝑃𝑈𝐸 =𝑇𝑂𝑇𝐴𝐿 𝐹𝐴𝐶𝐼𝐿𝐼𝑇𝑈 𝑃𝑂𝑊𝐸𝑅
𝐼𝑇 𝐸𝑄𝑈𝐼𝑃𝑀𝐸𝑁𝑇 𝑃𝑂𝑊𝐸𝑅=
1
𝐷𝐶𝑖𝐸
EFFICIENCY GOAL [3]
INTRODUCTION
PUE DCiE Level of Efficiency
3.0 33% Very Inefficient
2.5 40% Inefficient
2.0 50% Average
1.5 67% Efficient
1.2 83% Very Efficient
Power Usage Effectiveness (PUE) is an industry standard for the
DC efficiency metric
WHAT | WHY | HOW
1 MEASURE PUE
2 MANAGE AIR FLOW – CFD MODELLING
3 ADJUST THERMOSTAT
4 UTILISE FREE COOLING
5 OPTIMISE POWER DISTRIBUTION
DATA CENTRE EFFICIENCY BEST PRACTICE
INTRODUCTION
INTRODUCTIONWHAT | WHY | HOW
2 MANAGE AIR FLOW – CFD MODELLING
DATA CENTRE EFFICIENCY BEST PRACTICE
• Identify issues that cannot be seen in Data Centre
• Optimise data hall design and layout before it is built
• Troubleshooting of existing DC
• Predict failures and issues
PREDICTION IS BETTER THAN CURE
BASICS
CFD MODELLINGComputational fluid dynamics (CFD) has seen to be the most
powerful tool for the numerical prediction of the fluid behaviour.
BUILDINGS WIND MODELLING [1]
FIRE AND SMOKE MODELLING [1]
[1] https://synergetics.com.au/cfd[2] http://edinburghfireresearch.blogspot.com.au/2010/10/heron-tower-and-beging-of-concept-of.html[3] https://www.iesve.com/consulting/projects/projectdetail?building=Sydney-St-Apartments-Carpark-Ventilation&id=4026
CAR PARK CO CONCENTRATION MODELLING [1]APPARENTLY AGRICULTURE
• CONSERVATION OF MASS EQUATION
• MOMENTUM EQUATION
• ENERGY EQUATION
i ij i j i m
j i j j
u uPu u u g S
x x x x
0
i
i
x
u
1j L P j s
j P j j
T Tu k c T u q
x c x x
BASICS
FLUID FLOW EQUATIONS
• Airflow is assumed to be turbulent in the entire DC
• Reynolds-Average Navier Stokes (RANS) method is utilized to solve turbulent flow at steady state.
• Single phase flow being solved under the steady state condition
• Finite volume method along with staggered grid were utilized to iteratively solve the flow and transport equations.
BASICS
DESIGN ASSUMPTIONS
BOUNDARY CONDITION• Volume flow rate,
• The capacity of the units
• Desirable cold temperature
BASICS
• Two sets of experiments are considered for validation purposes. In
each set, one of the CRAC units will be shut down.
• The numerical model validation has been accomplished based on
the experimental set up developed by Schmidt et al.
Experimental finding [1][1] Measurements and predictions of the flow distribution through perforated tiles in raised-floor data centers, by Schmidt et al
PHYSICAL MODEL FOR VALIDATION
Volume flow rate through each tile when CRAC A is off.
(a) (b) (c) (d)
Tile number
Flo
wra
te(L
/s)
5 10 15-100
-50
0
50
100
Experiment
Numeric
Row 1
Tile numberF
low
rate
(L/s
)5 10 15
-50
0
50
100
Experiment
Numeric
Row 2
Tile number
Flo
wra
te(L
/s)
5 10 15
0
30
60
90
Experiment
Numeric
Row 3
Tile number
Flo
wra
te(L
/s)
5 10 15-100
-50
0
50
100
Experiment
Numeric
Row 4
BASICS
PHYSICAL MODEL FOR VALIDATION
The comparison of the numerical results with the experimental measurements has shown the accuracy of the developed numerical model and therefore, the modelling further will develop for the Data Centre analysis.
• COLLOCATION MODELLING
• Partial model offers quick solves because it does not model the complete physics of the room.
• Full model include all the physics, but is consequently slower to solve.
• ACCURATE MODELLING OF IT DEVICES
• ACCURATE MODELLING OF CRAC/CRAH UNITS
APPROACH
GEOMETRY MODELLING
CFD MODELLING NEEDS COST & ACCURACY TRADE OFF ANALYSES
Data Hall
Equipment Layout
Temp
RH
Pressure
IT Equipment
Cabinet Kw
Servers Qty
Air flow
Air inlet side
Air exhaust side
Cooling Equipment
CRAH/CRAC
Air Flow
SA Temp
RA Temp
Pressure
SA Tile type
Cold Aisle
Temperature
Hot Aisle
Temperature
Return air plenum temp
Supply air temperature
Monitoring Points
APPROACH
Accurate CFD modelling required accurate & efficient data
MODELLING PARAMETERS
ENVIRONMENTAL CLASSES FOR DATACOM EQUIPMENT CLASSES AIR-COOLED DATA CENTRE CLASSES (PRODUCT OPERATION)
ASHRAE environmental guideline for Datacom equipment
APPROACH
Class Dry Bulb ͦC Max Dew Point ͦC Relative Humidity %
Recommended
A1 to A4 18 to 27 5.5 ͦC dp to 60% RH & 15 ͦC dp
Allowable
A1 15 to 32 17 20 to 80%
A2 10 to 35 21 20 to 80%
A3 5 to 40 24 8 to 85%
A4 5 to 45 24 8 to 85%
MODELLING PARAMETERS
GEOMETRICAL PROPERTIES OF THE SIMULATED MODEL
Collocation (Partial model) 15300 mm x 4770 mm
Ceiling height 3500 mm
Raised floor height 1100 mm
Ceiling void height 1100 mm
Supply air diffuser size 600 mm × 600 mm
Cold-aisle width 1200 mm
Hot-aisle width 600 mm
Ceiling exhaust grilles size 600 mm × 1200 mm
APPROACH
LOADS SPECIFICATION
Rack cabinets 52 – each domain 26
Servers 2 per rack
Server power density 2.33 kW
Number of servers 104
Supply temperature 24.5 oC
CRAH unit capacity 142 kW
INTERNAL CONDITION
Min and Max recommended 18 oC – 27 oC
Min and Max allowable (A1) 15 oC – 32 oC
MODELLING PARAMETERS
APPROACH
DC MODEL
APPROACH
DC MODEL
APPROACH
1Total over temperature
RCIMaxallowabletemperature
extract sup ply
server
T T Flowin serversRTI or RTI
T FlowinCRAH units
PARAMETERS FOR DEFINITION OF RCI
INDEX DEFINITION
RETURN TEMPERATURE INDEX (RTI) is the measure of recirculation of hot air in hot aisle to cold aisle and also by-passing of cold air in cold aisle to the CRAH unit without passing through the servers.
RACK COOLING INDEX (RCI) is introduced to compare the observed temperatures with those recommended in ASHRAE guideline
CASES OF FAILURE SCENARIOS IN THIS STUDY
RESULT
FAILURE SCENARIOS
RESULT
FLOW SCHEMATIC – FLOW VELOCITY
CASE A – ARRANGEMENT
TEMPERATURE PLOT
RESULT
FLOW SCHEMATIC – FLOW VELOCITY TEMPERATURE PLOT
PRESSURE PLOT
CASE B – FAILURE SCENARIO
RESULT
FLOW SCHEMATIC – FLOW VELOCITY
CASE C – FAILURE SCENARIO
TEMPERATURE PLOT
PRESSURE PLOT
Rack rows number
RC
I(%
)
1 2 3 460
62
64
66
68
70
CASE ACASE BCASE C
Rack rows number
RT
I(%
)
1 2 3 440
40.2
40.4
40.6
40.8
41
CASE ACASE BCASE C
(a) (b)
(13 RACKS CABINETS IN EACH ROW)
RESULT
FAILURE SCENARIOS
RESULT
HOT AND COLD AISLE CONTAINMENT
RESULTONLY COLD AISLE ISOLATEDCOLLOCATION WITH PARTITIONING FOR PRIVATE DOMAINS
RESULT
HOT AND COLD AISLE CONTAINMENT
ONLY COLD AISLE ISOLATEDCOLLOCATION WITH PARTITIONING FOR PRIVATE DOMAINS
RESULT
HOT AND COLD AISLE CONTAINMENT
Tile Perforation Ratio % RCI RTI
10 65.2 40.34459
15 65.2 40.34459
25 65.35 40.37339
35 65.95 40.30638
45 66.7 40.37813
60 70.715 40.34941
It can be seen that the opening area does not have significant influenceon the RTI, however RCI is affected more by the opening area.
RESULT
TILE PERFORATION RATIO
FUTURE STUDY
• WHAT-IF SCENARIO ANALYSIS • ENERGY ANALYSIS OF DC• PREDICTIVE MODELLING• MACHINE LEARNING BASED ON NUMERICAL MODELLING RESULTS
[1]
[1] https://www.itnews.com.au/gallery/photos-equinixs-sydney3-data-centre-261690/page5
CONCLUSION
• BY VALIDATING OF CFD MODEL WITH AN EXPERIMENTAL SET UP, THE ACCURACY OF THE MESH DENSITY AND ANALYSIS DETAILS HAVE BEEN PROPERLY ADJUSTED.
• TWO NON-DIMENSIONAL INDEXES (RCI & RTI) WERE USED FOR PARAMETRIC STUDY AND THE EFFECT OF VARIOUS IMPORTANT FACTORS HAVE BEEN DISCUSSED.
• PARTIAL CFD MODEL OF A COLLOCATION HAS BEEN CONSTRUCTED AND VARIOUS PARAMETERS AND CONDITIONS HAVE BEEN INVESTIGATED
• COLD AISLE ARRANGEMENT RECOMMENDED
• SIMULATION OF FAILURE SCENARIOS ARE IMPORTANT FOR CONTROL STRATEGY IN RESPOND TO ANY FAILURES
• CFD MODELLING NEEDS COST & ACCURACY TRADE OFF ANALYSES
