IBM Mobile Measurement Technology - ETCC · 2020. 1. 2. · IBM Mobile Measurement Technology ET08SCE1030 Southern California Edison Page 10 Design & Engineering Services December

Design & Engineering Services

IBM Mobile Measurement Technology

ET08SCE1030 Report

Prepared by:

Design & Engineering Services

Customer Service Business Unit

Southern California Edison

December 2010

IBM Mobile Measurement Technology ET08SCE1030

Southern California Edison

Design & Engineering Services December 2010

Acknowledgements

Southern California Edison’s Design & Engineering Services (DES) group is responsible for

this project. It was developed as part of Southern California Edison’s Emerging Technologies

Program under internal project number ET08SCE1030. DES project manager Devin Rauss

conducted this technology evaluation with overall guidance and management from Henry

Lau and Paul Delaney. For more information on this project, contact [email protected].

Disclaimer

This report was prepared by Southern California Edison (SCE) and funded by California

utility customers under the auspices of the California Public Utilities Commission.

Reproduction or distribution of the whole or any part of the contents of this document

without the express written permission of SCE is prohibited. This work was performed with

reasonable care and in accordance with professional standards. However, neither SCE nor

any entity performing the work pursuant to SCE’s authority make any warranty or

representation, expressed or implied, with regard to this report, the merchantability or

fitness for a particular purpose of the results of the work, or any analyses, or conclusions

contained in this report. The results reflected in the work are generally representative of

operating conditions; however, the results in any other situation may vary depending upon

particular operating conditions.


Southern California Edison Page i Design & Engineering Services December 2010

ABBREVIATIONS AND ACRONYMS

ASHRAE American Society of Heating, Refrigeration, and Air Conditioning Engineers

EEM Energy Efficiency Measure

FIRM Facility Integrated Resource Management

IT Information Technology

LBNL Lawrence Berkeley National Laboratory

MMT Mobile Measurement Technology


Southern California Edison Page ii Design & Engineering Services December 2010

FIGURES Figure 1. Typical Breakdown of Energy Consumption within a

Data Center ................................................................. 7

Figure 2. IBM Mobile Measurement Technology (MMT) .................... 8

Figure 3. Sample Temperature Map of Data Center ........................ 9

Figure 4. Data Center Layout .................................................... 11

Figure 5. Temperature Map at 3.5 Feet ....................................... 12



Figure 8. Humidity Map at 4.5 Feet ............................................ 13

Figure 9. K20 Logger Points ...................................................... 15

Figure 10. Toyota HQ Central Plant Control System ....................... 16

Figure 11. Mechanical System Points ........................................... 16

Figure 12. Data Room Space Temperature and Humidity Points ....... 17

Figure 13. Project Monitoring Boundaries ...................................... 18

Figure 14. Daily kWh Consumption of IT Equipment ....................... 19

Figure 15. Daily kWh Consumption of CRAC Units .......................... 20

Figure 16. Ambient Dry Bulb Temperature of Surrounding Areas ..... 21

Figure 17. Modeled IT Equipment Inlet Temperature ...................... 22


Southern California Edison Page iii Design & Engineering Services December 2010

TABLES Table 1. Data Table DC1 THM Point List ..................................... 24

Table 2. Data Table DC1 THM2 Point List ................................... 24

Table 3. Data Table K20 - T402 Point List ................................... 25

Table 4. Data Table K20 - T403 Point List .................................. 25





Table 9. Data Table E1st Point List ............................................ 27

Table 10. Data Table PLANT Point List ......................................... 27

Table 11. Data Table TOWER33 Point List .................................... 28

Table 12. Data Table CWP Point List ........................................... 28

Table 13. Data Table Rsview Point List ........................................ 28

Table 14. CRAC Specifications ...................................................... 28

Table 15. One-Time Water Flow Measurements .............................. 29

Table 16. One-Time Air Flow Measurements .................................. 29


Southern California Edison Page iv


CONTENTS

EXECUTIVE SUMMARY _______________________________________________ 1

INTRODUCTION ____________________________________________________ 7

ASSESSMENT OBJECTIVES ___________________________________________ 10

TECHNICAL APPROACH/TEST METHODOLOGY ___________________________ 11

MMT Implementation ............................................................ 11

Monitoring Plan ..................................................................... 14

RESULTS_________________________________________________________ 19

RECOMMENDATIONS ______________________________________________ 23

RECOMMENDATIONS ______________________________________________ 23

APPENDICES _____________________________________________________ 24

REFERENCES, OPTIONAL ____________________________________________ 30


Southern California Edison Page 1


EXECUTIVE SUMMARY This project assesses IBM’s Mobile Measurement Technology (MMT) at a customer’s data

center. The MMT tool performs an audit of a data center, producing temperature and

humidity maps. From this increased knowledge of operating characteristics, modifications

can be made that improve the airflow within a data center, and ultimately energy efficiency

measures can be implemented. Through the implementation of these measures, energy

savings can be found, while maintaining safe operation of the data center.

In this particular data center, the MMT tool and IBM team were able to make a number of

recommendations that yielded significant savings. The first recommendation was a series of

steps to improve the air distribution, namely; installing blanking panels, strip curtains to

partition the room, adjusting and installing new perforated floor panels, reducing leakage

around cables, and adjusting fan settings. Once these measures were implemented, the

improvement in airflow allowed for three Computer Room Air Conditioners to be shut off and

capped, as well as for all thermostatic setpoints to be raised 5°F.

The monitoring of this site showed that through the implementation of these measures,

daily cooling energy consumption was reduced 15.9%. The demand associated with these

energy savings is likely to be of similar magnitude, given the 24/7 operation of the cooling

equipment. However, in analyzing this data there appeared to be no savings attributable to

the raising of the setpoints, which goes against standard HVAC rules of thumb. Further

analysis indicated that this may be a result of the ambient temperatures, but no real

solution could be found.

This project was split into two phases; the first being the analysis of the field monitored

data, with a second, forthcoming, phase to develop a simulation model and utilize the field

monitored data to develop annual, normalized savings. It is recommended that this second

phase be completed in order to understand the setpoint energy consumption anomaly, the

annual energy savings, potential savings in different climate zones, and how various IT

loads would impact the savings.




INTRODUCTION Energy consumption in data centers is a growing problem, as both the number of data

centers and the power draw of the equipment within continues to expand. A study

performed by the Lawrence Berkeley National Lab (LBNL) concluded that data centers

accounted for 1.2% of the total U.S. electrical consumption in 2005, and forecasted that this

number would double by 20101.

The Information Technology (IT) equipment, both in the energy required to run

computations and to keep this equipment cool, drives energy consumed within a data

center. Another study performed by LBNL found that in a typical data center, the IT

equipment accounted for 51% of the energy consumed, with the cooling equipment

accounting for another 33%2, as shown in Figure 1. The necessity to keep these

computations going, with no delay, often leads to over designed data centers, with little

regard for energy consumption.

FIGURE 1. TYPICAL BREAKDOWN OF ENERGY CONSUMPTION WITHIN A DATA CENTER

As energy costs have continued to grow, data center energy consumption has become a

greater focus. The IT and HVAC industries have seen the much work performed, particularly

by the American Society of Heating, Refrigeration, and Air Conditioning Engineers

(ASHRAE), to identify best practices for data center operation. Much of this work has dealt

with the cooling side of operations, as many times there is no room for improvement with

the computations. A major emphasis of ASHRAE’s work has been the optimization of airflow

within data centers, and resultantly optimizing temperatures.

This project assesses a technology developed by IBM, the Mobile Measurement Technology

(MMT), which produces energy savings on the cooling side. This project was performed at a

roughly 30,000 square foot data center in Torrance, California, with under floor air

distribution. The IBM MMT provided an assessment of the data center’s operations.

Recommendations for improvements were made and ultimately implemented. Field

monitoring was done to quantify the impacts of these recommendations, as well as provide

enough data to perform building energy simulations as part of Phase 2 of this project. This

report provides details on the technology, as well as the findings from Phase 1.




BACKGROUND Seeing a need within the data center industry for a tool to increase data center

optimization, IBM developed the MMT. This technology, or more appropriately tool, is shown

in Figure 2.

FIGURE 2. IBM MOBILE MEASUREMENT TECHNOLOGY (MMT)

As shown, the MMT is a motorized cart outfitted with a variety of sensors (e.g.,

temperature, pressure, etc.) and a computer for data storage. This cart drives through the

data center, during normal conditions, to capture operating characteristics. These

characteristics are converted into 3D temperature maps, at various heights, which provide

the basis for energy efficiency measure (EEM) recommendations. Figure 3 shows a

temperature map recorded at the test site.




FIGURE 3. SAMPLE TEMPERATURE MAP OF DATA CENTER

The primary focus of these temperature maps is the identification of hot or cold spots in the

data center. ASHRAE publishes recommended temperature ranges for IT equipment that

most data center designers and operators follow. However, these recommendations are not

for the average space condition, rather they are for specific IT equipment inlet

temperatures. That is to say, the recommendation is that no piece of IT equipment falls

outside of the recommended range. In many cases, there is a single area or piece of

equipment that drives the cooling needs for the entire data center. These areas are

identified as hot or cold spots through the temperature mapping.

Once these spots have been identified, recommendations can be made to mitigate them.

The first recommendation, which is a pre-requisite to any further recommendations, is to

improve the distribution of the cold air. In many data centers, this amounts to installing

blanking plates, re-positioning perforated tiles, better sealing around wires, adjusting fan

settings, and various other measures to improve air distribution. Once these adjustments

have been made, the EEMs can be implemented. The improved air distribution often yields

recommendations to raise thermostatic setpoints, turn off air conditioning units, or both.




ASSESSMENT OBJECTIVES The objectives of this project are twofold. The first objective of this project was to quantify

the impacts of the recommendations made because of the implementation of the MMT tool.

The second objective was to collect enough data to perform energy-building simulations that

would provide normalized, annual savings and could help to calibrate the data center

portion of eQuest underdevelopment.

The first objective was to be completed by working in conjunction with IBM and the site

energy management team, Roy Jorgensen and Associates Facility Integration Resource

Management (FIRM) team, to implement the MMT process and the recommended EEMs.

FIRM was also selected as the team to install the data collection hardware, as well as collect

and analyze the data. This field-collected data provided the basis for the quantification of

the MMT’s impacts. This objective was completed as part of Phase 1.

The second objective was also supported through the gathering of field data. Additionally,

construction documents were gathered to develop the model of the data center (e.g., how is

it constructed, what are the dimensions, etc.). Finally, details on the equipment within the

data center and its schedule were also collected. This information will be used to develop a

simulation model as part of Phase 2 of this project.




TECHNICAL APPROACH/TEST METHODOLOGY The technical approach has been divided into two categories; MMT Implementation and

Monitoring Plan. These two categories are described in detail below.

MMT IMPLEMENTATION The data center used for this project is shown in Figure 4. It is roughly 30,000

square feet, with a main computer floor room, a printer room, a control room (shown

as KVM), and a test lab.

FIGURE 4. DATA CENTER LAYOUT

In August 2008, the MMT tool was used to diagnose the performance of this data

center. Figures 5 through 8 show temperatures maps produced at various heights, as

well as one humidity map. From these maps, airflow modifications were

recommended.




FIGURE 5. TEMPERATURE MAP AT 3.5 FEET






FIGURE 8. HUMIDITY MAP AT 4.5 FEET




The recommended airflow modifications were made in December 2008 (after

baseline data was collected). The modifications made included:

Installing blanking panels to reduce leaks through servers

Installing material to reduce leaks around wires

Installing curtains to create hot and cold aisles

Installing higher performance perforated floor panels

Repositioning perforated floor panels

Adjusting air handler fan settings

After these modifications were made, additional data was collected to assess the

impacts. Additionally, monitoring sensors were installed by IBM, which allowed for

continued monitoring of room temperatures. Through the initial assessment and the

continued monitoring, the following EEMs were recommended and implemented:

Turn off three Computer Room Air Conditioners (CRACs)

Raise thermostatic setpoint 5°F

Each EEM was implemented incrementally, i.e., shut off one CRAC at a time, and

raise the setpoint 1°F at a time, with a stabilization period in between to ensure no

adverse effects. Data was collected through each period. Because of the incremental

steps, these EEMs took some time to implement. The CRACs were shut off from

January to February 2009, while the setpoint was raised over the course of March

2009. Detailed monitoring information is as follows.

MONITORING PLAN Jorgensen’s FIRM team installed temporary data loggers to record the agreed-upon

data center floor-mounted computer room air conditioning units’ (CRACs’) points, as

well as a couple of loads served by the static switches but outside the boundaries of

the data room under study. K20 loggers by ENERNET Corp. were selected because of

the total number of points each was capable of monitoring. These loggers collected

data on a 5-minute interval. Figure 9 provides a graphic perspective of the installed

system.




FIGURE 9. K20 LOGGER POINTS

One-time water flow for each CRAC was measured using a leased Shenitech STUF-

200H portable flow meter. FIRM retained American Air Balance Co. to conduct airflow

measurements for two representative CRAC units (one 20-ton and one 30-ton). A

portable data logger (Dent ELITEpro) was used to record the energy for the closed

system cooling water pumps. The logger remained in place for approximately three

weeks.

FIRM was able to use the existing control system located in the remote central plant

to collect temperature and flow data on the remote pumps and cooling towers, as

well as the outside air temperature and humidity. A screen capture of the control

system is included as Figure 10. Further, Figure 11 provides a graphic view of the

specific points that are included in the monitoring project.




FIGURE 10. TOYOTA HQ CENTRAL PLANT CONTROL SYSTEM

FIGURE 11. MECHANICAL SYSTEM POINTS




An existing Stonewater Controls energy monitoring system provides energy use data

on the central plant pumps and cooling towers as well as the two static switches

serving the data equipment load on the raised floor. The system structure is depicted

as a part of Figure 2. Space temperatures and humidity are collected utilizing the

existing Leibert SiteScan system, which FIRM connected to Toyota’s Delta

Automation data server. The location of the sensors on the floor is shown in

Figure 12.

FIGURE 12. DATA ROOM SPACE TEMPERATURE AND HUMIDITY POINTS

FIRM installed six K20 data loggers to accommodate the number of points required

to determine the total energy use within the project boundaries. Figure 13 shows the

boundaries of the project, less the two areas marked in bright green.




FIGURE 13. PROJECT MONITORING BOUNDARIES

The data loggers were installed in one of the two electrical rooms located outside the

project boundaries (not shown in the image above). The points monitored by these

loggers are noted in the tables in the Appendix.

There are three electric panels that serve load outside the project boundaries that

are served by the static switches. These panels, designated KVM, TestLab#1 and

TestLab#2, were monitored by points on the K20 loggers and their load subtracted

from the total data center load.




RESULTS As noted previously in this report, the focus of data center operation is to provide adequate

cooling so that the IT equipment does not fail. The amount of heat that must be removed is

in direct relation to the amount of power consumed by the IT equipment. For any fair

comparison is to be drawn, the IT equipment load must be kept consistent. This load was

monitored throughout the project and is shown in Figure 14. From this chart, it can be

concluded that the IT equipment load was kept within a reasonable tolerance (the green

trend line shows almost no change). Therefore, the cooling equipment has to remove the

same load throughout the project duration.

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ay)

FIGURE 14. DAILY KWH CONSUMPTION OF IT EQUIPMENT

The CRAC units’ energy consumption was monitored throughout the project and is graphed

in Figure 15. The average daily consumption of the baseline period was 6411 kWh/day.

Once the airflow modifications were made, the average daily consumption was slightly

increased to 6436 kWh/day. The modifications to the air distribution system were expected

to be negligible; this 0.3% increase is considered within the error of the testing. With the

shut off of the first CRAC unit, also the first EEM recommended, came the first noticeable

change in consumption. The daily energy consumption dropped to 6170 kWh/day, a

reduction of 3.7% compared to the baseline. As the next CRAC was shut off, greater savings

were seen. The daily consumption was reduced further to 5891 kWh/day, a 8.1% reduction

from the baseline. With the third and final CRAC shut off came even more savings. The daily




energy consumption went down to 5388 kWh/day, a 15.9% reduction in consumption. The

final EEM was to raise the setpoints on the CRAC units by 5°F. From Figure 15 it is evident

that this measure did not provide any additional savings (average daily energy consumption

remained at 5388 kWh/day). This result was not anticipated. Further data analysis was

completed in order to hypothesize the reason for this result.

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Baseline Airflow Mitigation CRAC 1 Shut Off CRAC 2 Shut Off CRAC 3 Shut Off Raise Setpoint

FIGURE 15. DAILY KWH CONSUMPTION OF CRAC UNITS

Although data centers are internally load driven and generally not affected by ambient

conditions, this particular data center used air-cooled CRAC units. The heat absorbed within

the data center by the CRAC units is rejected to externally located condensing units, which

are subject to the ambient conditions. On-site weather data was not collected, so alternative

resources were sought. Figure 16 shows the weather data from the two SCE weather

stations that were closest to the test site; Long Beach and El Segundo, California. A linear

fit to the El Segundo dry bulb temperature shows little to no change over the course of the

testing. However, a similar fit to the Long Beach data shows a steady increase in

temperature. It is believed that this increase is what caused the unexpected results

accompanying the raised setpoint EEM.




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11/22/2008 12/12/2008 1/1/2009 1/21/2009 2/10/2009 3/2/2009 3/22/2009 4/11/2009 5/1/2009

Date

Dry

Bu

lb T

em

pera

ture

(°F

)

Long Beach El Segundo Linear (Long Beach) Linear (El Segundo)

FIGURE 16. AMBIENT DRY BULB TEMPERATURE OF SURROUNDING AREAS

As noted previously, the operating temperature of the data center is not optimized for some

point within the room, but rather for the IT equipment inlet. The IBM team modeled the

inlet temperatures for the IT equipment throughout the project, the results of which appear

in Figure 17. Clearly, the airflow distribution or hotspot mitigation work, yielded lower inlet

temperatures to the IT equipment. As a result, the various EEMs could be deployed without

raising the inlet temperature above the baseline conditions. This allowed for the IT

equipment, running the same number of computations, to be cooled to the same

temperature, utilizing less energy.




FIGURE 17. MODELED IT EQUIPMENT INLET TEMPERATURE




RECOMMENDATIONS From this phase of the project, it is clear that implementing airflow distribution measures,

combined with shutting off CRAC units and raising thermostatic setpoints, can yield

significant savings. However, given the short timeframe of this project, and the lack of

weather-normalized data, there is some need to continue this work. It is recommended that

Phase 2 of this project, and the objectives identified earlier, be completed in order to truly

understand the potential for this tool. It is also important to understand that much of the

savings seen within this project are a result of holding the operation of the data center

consistent. In actuality, data centers are very dynamic building types, with wide ranges of

loads and operating characteristics. The use of the building energy simulation, to be

developed in Phase 2, would also help to better understand how these EEMs would perform

under not only differing weather conditions, but also IT equipment loads.




APPENDICES The following tables detail the data collection points, as captured in the tables in the data

database.

TABLE 1. DATA TABLE DC1 THM POINT LIST

POINT ID POINT DESCRIPTION dt Date and time

TEMP 1 Space Temperature – refer to Figure 3

TEMP 2 Space Temperature - refer to Figure 3






HUMID 1 Space Humidity - refer to Figure 3







TABLE 2. DATA TABLE DC1 THM2 POINT LIST

POINT ID POINT DESCRIPTION

dt Date and time










TABLE 3. DATA TABLE K20 - T402 POINT LIST


dt Date and time

1AC_kwa AC-1 Phase A kW

1AC_kwb AC-1 Phase B kW

1AC_kwc AC-1 Phase C kW

1Comp_kwa AC-1 Compressor Phase A kW

1Comp_kwb AC-1 Compressor Phase B kW

1Comp_kwc AC-1 Compressor Phase C kW

1SF_kwa AC-1 Supply Fan Phase A kW

1SF_kwc AC-1 Supply Fan Phase C kW

1Hum_kwa AC-1 Humidifier Phase A kW

1Hum_kwb AC-1 Humidifier Phase B kW

1Hum_kwc AC-1 Humidifier Phase C kW

1Reh_kwa AC-1 Reheat Phase A kW

1Reh_kwb AC-1 Reheat Phase B kW

1Reh_kwc AC-1 Reheat Phase C kW

1AC_SAT AC-1 Supply Air Temp (F)

1AC_SAH AC-1 Supply Air Humidity (%)

1AC_RAT AC-1 Return Air Temp (F)

1AC_RAH AC-1 Return Air Humidity (%)

1AC_SWT AC-1 Supply Water Temp (F)

1AC_RWT AC-1 Return Water Temp (F)



dt Date and time













































9AC_SWT AC-9 Supply Water Temp (F)

9AC_RWT AC-9 Return Water Temp (F)



dt Date and time

4AC_kwa AC 4 Phase A kW

4AC_kwb AC 4 Phase B kW

4AC_kwc AC 4 Phase C kW


















6Cond_kwa AC-6 Condenser Phase A kW

6Cond_kwb AC-6 Condenser Phase B kW

6Cond_kwc AC-6 Condenser Phase C kW

PRCond1_kwa Print Room Condenser Phase A kW

PRCond1_kwb Print Room Condenser Phase B kW

PRCond1_kwc Print Room Condenser Phase C kW

PRAHU_kwa Print Room Leibert Phase A kW

PRAHU_kwb Print Room Leibert Phase B kW

PRAHU_kwc Print Room Leibert Phase C kW

TLAB1_kwa Tech Lab Panel 1 Phase A kW

TLAB1_kwb Tech Lab Panel 1 Phase B kW

TLAB1_kwc Tech Lab Panel 1 Phase C kW

TLAB2_kwa Tech Lab Panel 2 Phase A kW

TLAB2_kwb Tech Lab Panel 2 Phase B kW

TLAB2_kwc Tech Lab Panel 2 Phase C kW



dt Date and time







7Cond_kwa AC-7 Condenser Phase A kW

7Cond_kwb AC-7 Condenser Phase B kW

7Cond_kwc AC-7 Condenser Phase C kW

PRCond2_kwa Print Room Condenser 2 Phase A kW

PRCond2_kwb Print Room Condenser 2 Phase B kW

PRCond2_kwc Print Room Condenser 2 Phase C kW

KVM_kwa KVM Room Panel Phase A kW

KVM_kwb KVM Room Panel Phase B kW

KVM_kwc KVM Room Panel Phase C kW

TABLE 9. DATA TABLE E1ST POINT LIST


dt Date and time

StaticSw2 Static Switch #1 Total kW

StaticSw1 Static Switch #2 Total kW

DC1CTwrs Data Center Cooling Towers and Pump Total kW

TABLE 10. DATA TABLE PLANT POINT LIST


OAT Outside Air Temp (F)

OAH Outside Air Humidity (%)




TABLE 11. DATA TABLE TOWER33 POINT LIST


CWST Closed Loop Condenser Water Supply Temp (F)

CWRT Closed Loop Condenser Water Return Temp (F)

TABLE 12. DATA TABLE CWP POINT LIST


KW1 Phase A kW

KW2 Phase B kW

KW3 Phase C kW

AvgVolt Average voltage

AvgAmp Average Amps

KW Hour Total Pump kWh

TABLE 13. DATA TABLE RSVIEW POINT LIST


HXSTemp Heat Exchanger Supply Water Temp

HXRTemp Heat Exchanger Return Water Temp

Twr3Flow Cooling Tower 3 Flow (gpm)

Twr3TempIN Cooling Tower 3 Inlet Temp (F)

Twr3TempOut Cooling Tower 3 Outlet Temp (F)

Twr4Flow Cooling Tower 4 Flow (gpm)

Twr4TempIN Cooling Tower 4 Inlet Temp (F)

Twr4TempOut Cooling Tower 4 Outlet Temp (F)

TABLE 14. CRAC SPECIFICATIONS

UNIT # MODEL # SERIAL # TONS COND. DESIGN

CFM

HUMIDIFIER

DESIGN KW

FAN

DESIGN

HP

CRAC-1 FE267W-AGES 365350-003 20 Water 10,200 9.6 7.5





CRAC-6 DH380AAGE1 393233-003 30 Air 15,200 9.6 10

CRAC-7 DH380AAGE1 393233-001 30 Air 15,200 9.6 10

CRAC-8 DH412WUAGES 524730-003 30 Water 15,200 9.6 10



Cond-6 CDF510CA 0037C37657

Cond-7 CDF510CA 0037C37659




TABLE 15. ONE-TIME WATER FLOW MEASUREMENTS

POINT DATE/TIME MEASURED MEASURED FLOW (GPM) CRAC-1 2/7/09 – 16:30 20.5

CRAC-2 2/7/09 – 12:15 53.7

CRAC-3 2/7/09 – 14:30 34.8

CRAC-4 2/7/09 – 14:50 0

CRAC-5 2/7/09 – 12:40 0

CRAC-6 NA NA

CRAC-7 NA NA

CRAC-8 2/7/09 – 15:30 0

CRAC-9 2/7/09 – 15:55 56.5

CRAC-10 2/7/09 – 16:45 94.3

Closed Cooling Loop 2/7/09 – 11:15 428.3

Tower-3 Open Loop 2/7/09 – 09:00 662.8

Tower-4 Open Loop 2/7/09 – 10:15 641.9

TABLE 16. ONE-TIME AIR FLOW MEASUREMENTS

POINT DATE MEASURED MEASURED FLOW (CFM) CRAC-9 2/7/09 20.5

CRAC-6 2/7/09 18,391




REFERENCES 1 Koomey, Jonathan G. (February 15, 2007), "Estimating Total Power Consumption by

Servers in the U.S. and the World". Final Report, Lawrence Berkeley National Laboratory.

2 Tschudi, William (March 30, 2004), "Energy Efficient Data Centers". Final Report,

Lawrence Berkeley National Laboratory.

Documents

IBM Mobile Measurement Technology - ETCC · 2020. 1. 2. · IBM Mobile Measurement Technology ET08SCE1030 Southern California Edison Page 10 Design & Engineering Services December