Transcript Document

Integrating Sensor Data with
Simulation Models for Data Center
Optimization and Energy Reduction
Paul Bemis
President
Applied Math Modeling Inc
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Integrating Sensor Data with Simulation Models for
Data Center Optimization
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As the cost of sensors and simulation technologies continue to decline,
there is now an opportunity to create a highly accurate predictive
model of the data center based on measured data including
temperature, humidity and pressure at a reasonable cost.
The predictive model can then be used to determine the optimal
operational point for minimizing energy consumption of a given data
center.
In this session we will review a case study that used this methodology
to find the optimal operating point and illustrate the steps to create the
model and used to find the minimum energy consumption while still
maintaining an N+1 design.
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Outline
The Role of sensors in the data center
Provides current state of operations
Alert operators of change in state
Allow energy optimization
The Role of CFD in the data center
Validate initial design
Predict optimal operating point for current design
Predict future condition or failure mode operation (DR modeling)
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Outline (continued)
Combining Sensor Data with CFD Modeling
Sensors provide the current state
These can be used to set up the CFD model
And can be used to validate the CFD model
The CFD model can then predict the optimal operating point
Sensors can then be used to maintain it.
And the data can be integrated into DCIM
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Data Center Metrics
Return Temperature Index (RTI) =
Rack Flow Rate
x 100
Air Handler Flow Rate
or
Return Temperature Index (RTI) =
Air Handler Delta T
Rack Delta T
x 100
Interpretation of RTI
RTI is a measure of net by-pass air or
net recirculation air in the data center
Interpretation
RTI Value
Balanced
100%
Net Rack Recirculation
> 100%
Net By-Pass Airflow
< 100%
Return Temperature Index (RTI) is a Trademark of ANCIS Incorporated (www.ancis.us).
All rights reserved. Used under authorization
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
Rack Cooling Index (RCIHI) = 1 -

Rack Cooling Index (RCILO) = 1 -
Total Over-Temp
Max Allowable Over-Temp
Total Under-Temp
Max Allowable Under-Temp
x 100
x 100
Interpretation of RCI
ASHRAE
Specifications:
Recommended
64.4 – 80.6 F
Allowable
59 – 89.6 F
RCI is a measure of compliance with
the ASHRAE thermal guideline
Interpretation
RCI Value
Ideal
100%
Good
95% to 100%
Acceptable
90% to 95%
Poor
< 90%
Rack Cooling Index (RCI) is a Registered Trademark of ANCIS Incorporated (www.ancis.us).
All rights reserved. Used under authorization
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Today’s Example
Medium sized data center
• 15,000 Sq Ft of white space
• Raised floor design with ceiling plenum return.
• 757 kW of heat load, 192 racks
• RDX units on 11 high density rack rows
• Multi level design:
• Basement level is reserved for mechanical
• Second floor is data center white space
• Third level is return plenum.
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Case example data center
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Methodology for creating valid model
1. Build model of data center using CFD model builder
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Avoid small details that will not affect airflow
Focus on dominate parameters
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Geometric placement of inlets, cooling units, racks, etc
Height of room, plenum, ceiling return
Tile placement, % area open, ceiling grill placement
Placement and size of racks
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Can add detail later if necessary
Begin with “average watts per rack”
2. Use measured data to set the boundary conditions of cooling
equipment and racks
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Set supply air temperature (SAT) using measured data
Measured “watts per rack” thermal loads
Assume 20 F delta across racks
Assume 156 CFM per kW for rack cooling
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Methodology for creating valid model
3. Use measured data to Validate CoolSim model.
a)
b)
Compare measured CRAC/CRAH return temperature to predicted
return temperature. Should be < 20%
Compare measured Rack Inlet Outlet temperature to predicted
temperatures. Should be < 20%
4. Adjust model parameters until they match measured values
within acceptable level
Governing Equation:
Q  flowrate   cP  (TR  TS )
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Case Example
Model built from drawings
Supply air flowrate/temp from measurements
Rack positions from drawings
Rack loads taken from rack power measurements
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Rack Inlet Temperatures
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Rack Inlet Temperatures Full Range
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Initial Results
Step 1: Check Air Handler Temperatures.
Measured Return Temp
CRAC Unit Name
Downflow Units
ahu_12 (A , 11)
ahu_11 (A , 18)
ahu_10 (A , 26)
ahu_9 (A , 34)
ahu_8 (U , 54)
ahu_7 (AC , 54)
ahu_6 (AK , 54)
ahu_5 (AS , 54)
ahu_13 (U , 1)
ahu_2 (AC , 1)
ahu_3 (AN , 1)
ahu_4 (AW , 1)
Error
79
80
79
76
78.5
0.21%
Predicted Return
Ave Supply
Supply Flow Rate
Heat Removal
Temperature
Temperature
(cfm)
(kW)
o
o
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
84,000
54.72
48.92
38.05
39.45
39.03
40.73
38.16
31.01
47.46
45.33
43.2
34.26
500.32
( F)
( F)
85
82
77
78
78
78
77
74
81
80
79
75
78.7
60
60
60
60
60
60
60
60
60
60
60
60
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Step 2: Check Rack Temperatures
Supply Temperatures are acceptable
Return Temperature need some adjustment
Rack Temperatures
Inlet
Return
Measured 68.77
76.75
Predicted
68.57
95
Error
0.3%
19%
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Racks with large variances
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Steps to adjust return temperatures
1.
2.
3.
4.
Look for racks with large variance
Check heat loads first
Adjust temperature rise up, or flowrate down.
Rerun simulation to “dial in” temperatures.
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Rack data adjustments
RDX heat exchangers were assumed to perform at 50%
efficiency (10F temp drop). But for higher density racks
(15-20 kW), this number increases to 75%
Change delta T assumptions on highest density racks from 10F to 15F
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Recheck Air Handlers
Measured Return Temp
CRAC Unit Name
Downflow Units
ahu_12 (A , 11)
ahu_11 (A , 18)
ahu_10 (A , 26)
ahu_9 (A , 34)
ahu_8 (U , 54)
ahu_7 (AC , 54)
ahu_6 (AK , 54)
ahu_5 (AS , 54)
ahu_13 (U , 1)
ahu_2 (AC , 1)
ahu_3 (AN , 1)
ahu_4 (AW , 1)
Average
Error
Ave Return
Ave Supply
Supply Flow Rate
Heat Removal
Temperature
Temperature
(cfm)
(kW)
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
7,000
48.87
35.16
36.22
34.64
38.28
22.44
15.07
13.94
19.24
33.71
21.95
21.63
o
79
80
79
76
78
7%
o
( F)
( F)
82
76
76
76
77
70
67
66
69
75
70
70
72.8
60
60
60
60
60
60
60
60
60
60
60
60
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Revised Rack Temperatures
Adjustments to model improve predictions
Rack Temperatures
Inlet
Return
Measured 68.77
68.77
Predicted
63.02
63.02
Error
8.4%
9%
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Model is now valid for optimization scenarios
Check the RTI and RCI values:
RTI: 38%
RCI (High): 98%
RCI (Low): 62%
More air from AHU than needed
Some racks with hot spots
Many racks below min temp
Opportunity to reduce air volume and increase supply
air temperature
• May require air management
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Visual representation of temperature data
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Data Center Modifications
Step 1: Decrease AHU airflow to reduce bypass
airflow.
Step 2: Increase SAT temperature to reduce energy
consumption.
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Step 1: Decrease AHU flowrate 30%
Results:
• Fan energy down 43%
• Racks still in compliance
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Step 2: Increase Supply Air Temperature 10F
• For every 1.8F increase, 3.5% increase in
mechanical energy efficiency alone.
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Does not take into account the improved efficiency of
economizer due to increased window of use.
Increased number of hours to operate airside economizer by
500 hours per year.
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Results: RCI High now at 97%
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Step 3: Manage the airflow where needed
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Results: RCI high increases to 98%
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Summary of steps
1.
2.
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6.
Built CFD model of current design
Validated CFD model with measurements
Used model to predict the effect of change
Reduced airflow first to improve RTI
Increased Supply Air temp to reduce energy
Used air management techniques to improve airflow
in hot regions
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Summary of results
• Reducing AHU airflow 30% => 43% savings
• Increasing Supply Air Temp 10 F => 20% savings
• Adds an additional 500 hours to “free air” opportunity
• Assuming cost to cool = cost to drive IT
• Cost of electricity = $ .10/Kwh
• Total savings potential = $400k/year
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Paul Bemis
[email protected]
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