Class-8 Heavy Truck Duty Cycle Project Final Report - Center for ...

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fuel sensor could not be calibrated; however, the literature indicates that the databus information regarding fuel consumption is accurate, with some researchers reporting errors of just 1%. ORNL’s limited analysis involving one of the participating trucks and using just a few short segments of data for which complete fuel ticket information was available indicates that the databus for the type of truck and engine used in this study underestimates fuel consumption by less than 8% (note: in these computations it was not possible to determine with 100% accuracy if the fuel tank was full at both the starting and ending point of the data segment used). Assuming that this is a systematic bias, even if the actual fuel efficiencies are erroneous, the percent differences are still accurate as was discussed in Section 6.2.2. Nevertheless, future sensitivity analyses will be performed that will study the differences in fuel efficiencies when instead of systematic biases in the databus readings, random errors (within a certain range) are assumed to be present in those readings. These results were statistically analyzed by performing a test of hypothesis. The null hypothesis stating that there were no significant differences among any of the four distributions (i.e., one for each truck-tire combination studied) of observed fuel efficiencies was tested against the alternative hypothesis indicating that the average fuel efficiency of any combination that had NGSWBTs was larger than the average fuel efficiency of the base case (Duals-Duals). In any case, it was possible to reject the null hypothesis with 100% confidence in favor of the alternative hypothesis. This strongly suggests that fuel efficiencies in these cases involving NGSWBTs are higher than in the case of a truck equipped with all dual tires. In other words, the percent improvements in fuel efficiencies that are achieved when using all NGSWBTs or combinations of duals and NGSWBTs are statistically significant. The search engine of the DCGenT Prototype was used to parse the data by truck-tire combination and vehicle load level. The latter was divided into four categories: 1) Tractor Only (trips made without any trailer), 2) Light Load (total vehicle weight between 24,000lbs and 44,000lbs); 3) Medium Load (total vehicle weight between 44,000lbs and 62,000lbs); and 4) Heavy Load (total vehicle weight between 62,000lbs and 80,000lbs). As in the previous case, 100-mile segments were considered for which the fuel efficiency was computed. Once again, for any load level considered, the results of the analysis show that there is always an improvement in fuel efficiency with respect to the base case (i.e., duals-duals) when NGSWBTs are used. In fact, for the particular case in which all tires are NGSWBTs, there was considerable improvement in fuel efficiency with respect to the base case (i.e., improvements that were above 10%), and those improvements were more significant as the load level increased. They were also statistically significant at the 99% level of confidence. 8.3 FUEL EFFICIENCY AND TRUCK SPEED An analysis of the fuel efficiency of Class-8 trucks as a function of vehicle speed was also conducted. The results of this analysis showed that under congested conditions (i.e., speeds between 0+ and 20 mph) any combination of tires that includes NGSWBTs performs better than the base case. However, in this case the percentage differences with respect to the base case were less than 5%. This result needs to be studied further by controlling for the type of class of road (i.e., freeways vs. surface streets) since what can be classified as congestion on a given facility (e.g., freeways) may be normal operations under another one (e.g., arterials and collectors). For speeds above 70 mph, there was a noticeable increase in fuel efficiency. It is very likely that this increase was due to the vehicle achieving those high speeds while traveling down slope, in which case the fuel consumption would be comparatively small, thus reflecting higher fuel efficiency. This needs to be studied further by controlling for the type of terrain (mainly, grade) to isolate its effect on achieved fuel efficiency. 110
9. FUTURE DIRECTIONS The Heavy Truck Duty Cycle (HTDC) Project has provided a rich source of Class-8 long-haul performance data. This data has breadth in that it addresses a wide range of parameters that can influence performance, and it has depth in that it provides values of particular parameters for large ranges of the values of other parameters. In particular, the fuel efficiency of the test vehicles in terms of the variation in payload, road grade, weather, congestion/road type, etc. Of primary benefit to the ORNL research team has been the experience and lessons learned from the pilot test conducted in 2005 through the completion of the FOT in 2008. This strong experiential base linked with the positive results of the HTDC project suggests future directions in the area of truck-based duty cycles and performance data. Although Class-8 long-haul operations is responsible for most of the fuel consumed in Class-8 applications, Class-8 short-haul applications in urban and metropolitan areas represent a significantly different set of performance criteria. A future direction of research involves collecting, archiving and analyzing data and information on non-long-haul Class-8 operations. Class-6 trucks represent the second largest class of fuel consumers in heavy/medium truck spectrum. These trucks have duty cycles that are vocationally dependent, log significantly fewer miles per vehicle year than Class-8 trucks, and often utilize the truck’s engine as a source of power for the operation of, for example, the arm of a bucket truck. As a result, a significant portion of the fuel consumed is done by the Power Take-Off (PTO) functions. A future direction of research involves collecting, archiving and analyzing data and information on Class-6 operations in selected vocations. Hybrid technologies have been demonstrated successfully in smaller vehicles that are less than Class- 6. However, many of the truck OEMs are considering hybrid drive applications for heavy- and medium-sized trucks. Although the stop-and-go nature of duty cycles is understood as making the best use of hybrid technologies, and the theory of hybrid applications is well understood, real-world applications may prove to be different. A future direction of research involves the vocational aspects and duty cycles of the various truck classes (3 through 8) to determine the vocations and specific applications that would benefit most from a hybrid platform. Furthermore, for these duty cycles, and for selected vocations, a cost-benefit study will be conducted that clearly illustrates the hybrid technology benefits to various vocations. This study would allow end-users who are considering hybrids to determine if their operation is a good candidate for hybrid technologies and also allow them to determine when the return-on-investment achieves pay-back. A simple “calculator” could be developed with input such as: Vehicle Class (GVWR), Combination/Straight Truck, Total Miles Driven, Average Weight, Total Time Idling, Power Take-Off Time, Driving Environment, etc. The calculator could give a quantitative score (e.g., 1-to-10 or 1-to-100) as to the “fit” for a particular application to go hybrid, and could give an estimate of annual fuel savings. EPA’s SmartWay Program is focused on increasing the energy efficiency and reducing greenhouse gases and air pollution for heavy and medium-sized trucks operating on our highways. SmartWay certification of a fleet assures the public that the owners and operators of the fleet are energy efficiency and environmentally minded. A challenge for the Program is how to certify various classes and vocational application of heavy- and medium-sized trucks. Discussions to date have included a reference truck configuration and representative sets of duty cycles per application/vocation which real-world fleets can compare themselves to. What is lacking is the existence of real-world-based duty cycles for comparison purposes, especially those involving the emissions from these vehicles operating on these duty cycles. Dyno and test track testing offer the opportunity to collect emissions 111
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Class-8 Heavy Truck Duty Cycle Proj
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Vehicle Systems Program ORNL/TM-200
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2.7 LAUNCH OF THE FOT (OCTOBER 23,
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40 Connection and Support Structure
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LIST OF TABLES Table Page 1 Pilot T
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ACKNOWLEDGEMENTS The Oak Ridge Nati
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Sixty channels of data were collect
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xviii
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The data analyses performed in Phas
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Fig. 2. HHDDT Transient Mode In add
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vehicle; 3) a transit bus; 4) a nei
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payload and accessory load would al
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Data # Performance Measure Benefit
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Data # Performance Measure Benefit
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Fig. 10. NGK NOx/O2 Sensors Fig. 11
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1.3.4 De-instrumentation: Following
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Fig. 16. Pilot Test North-South Rou
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GPS information collected with the
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2.1 REVIEW OF PERFORMANCE MEASURE R
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2.3.2 Performance Measures Fig. 18.
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Signal Sensor 53 Road Grade Calcula
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2.3.6 Tractor and Trailer Details F
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determined that the particular CANo
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Cable runs to and from sensors were
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2.8 CONDUCTION OF FOT The FOT laste
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3. INTERNET DATA ACCESS TOOL The HT
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Fig. 30. Search Results The file se
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When the button “Extract Data”
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Data Fig. 33. Segment Merging In ad
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Fig. 36 shows the synthetic cycle g
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Fig. 39. A Synthetic Duty Cycle wit
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tractors) as well as the maximum to
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vehicle was carrying a heavy load o
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Vehicle Speed [mph] Vehicle Speed [
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5.2 REAL-WORLD DUTY CYCLES Informat
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Vehicle Speed [mph] 70 60 50 40 30
Page 80 and 81: Given the size and complexity of th
Page 82 and 83: data or out-of-range data. Therefor
Page 84 and 85: 6.1.1 Spatial Information Using the
Page 86 and 87: For those cases in which an instrum
Page 88 and 89: Fig. 56. DCGenT Prototype Search Cr
Page 90 and 91: at constant speed. Parameters m, b,
Page 92 and 93: weight. Notice that, as expected, m
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Page 98 and 99: q q ( − ) ( feb'− feb) 100 '
Page 100 and 101: Table 15. General Statistics: Total
Page 102 and 103: 70% 60% 50% 40% 30% 20% 10% 0% Idli
Page 104 and 105: Fig. 68. All Trips with Dual Tires
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Page 108 and 109: Frequency 300 250 200 150 100 50 0
Page 110 and 111: Speed [mph] 80 70 60 50 40 30 20 10
Page 112 and 113: vehicle is hauling light or heavy l
Page 114 and 115: Frequency 350 300 250 200 150 100 5
Page 116 and 117: A summary of the statistics describ
Page 118 and 119: analysis controlling for the type o
Page 120 and 121: 6.3.2.4 Summary of Fuel Efficiency
Page 122 and 123: Lesson Learned: A technology progre
Page 124 and 125: Lesson Learned: Adding external sen
Page 126 and 127: know if the vehicle was “filled u
Page 129: 8. SUMMARY OF RESULTS AND CONCLUSIO
Page 133: Appendix A PILOT TEST LETTER REPORT
Page 136 and 137: 1.0 BACKGROUND 2.0 PROJECT OVERVIEW
Page 138 and 139: ACKNOWLEDGEMENTS The authors wish t
Page 140 and 141: evaluate the data collection system
Page 142 and 143: performance of their next generatio
Page 144 and 145: March and provided to the ORNL Prog
Page 146 and 147: Between January, 2005 and May, 2005
Page 148 and 149: Figure 4: NGK Sensor Figure 5: Omeg
Page 150 and 151: o Date and Time o Wind Speed o Wind
Page 152 and 153: Figures 28 through 31 show the ball
Page 154 and 155: Figure 22: Airweigh System Figure 2
Page 156 and 157: DVD. Figures 35 and 36 show some of
Page 158 and 159: 3.3.7.1 Pre-Analysis Efforts: As th
Page 160 and 161: Task 1: Identification of Fleet(s)
Page 162 and 163: Data # Performance Measure Performa
Page 164 and 165: Sensor or eDaq Performance Performa
Page 166 and 167: 45 46 47 48 49 50 51 52 53 Measure
Page 168 and 169: Data # 62 63 64 65-67 68-69 70-71 7
Page 170 and 171: Data # Performance Measure 87 Headi
Page 172 and 173: Appendix A-B Final Pilot Test Plan
Page 174 and 175: • DAS/sensor suite/cabling operab
Page 176 and 177: 9 10 11 12 13 14 15 Lateral Velocit
Page 178 and 179: 34 Rain Intensity 35 36 37 38 39 40
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59 60 61 62 63 64 65-67 68-69 70-71
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85 Longitude 86 Velocity 87 Heading
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3.2 Sensors The PMs listed in Table
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vehicles will be dedicated to the p
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DATE TEST ENGINEER: DRIVER: DEPARTI
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Jim Ridge, Dana Product Engineer, w
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Table 5.1 October Route and Schedul
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Table 5.2 December Route and Schedu
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Appendix A-C Field Test Signals 64
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9 Performance Measure Performance M
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Performance Measure 25 Rain Duratio
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Letter Report SUMMARY OF RELEVANT H
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Sensor or Performance Data Sensor o
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GAWR 44 lb AirWeigh 5800 Road Surfa
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Appendix B SCHRADER MEMORANDUM OF U
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MOU-UTB-2007001 Schrader Trucking C
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MOU-UTB-2007001 Schrader Trucking C
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Appendix C FIELD OPERATIONAL TEST P
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Collection, Analysis, and Archiving
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4.0 APPROACH Phases 2 and 3 of the
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Signal Sensor 60 Tractor-Trailer Ma
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Schrader Trucking will provide six
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Sensitive Information] [Partner Sen
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4.6 Sensor and DAS Integration to T
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5.5 Vehicle Deployment Methodology
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Start address: 2360 Cherahala Blvd
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Figure 6.2: Schrader Shop, Far Left
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Figure 6.7: eDAQ Lite with two Vehi
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6.5 Safety Information For Student
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Appendix D SYSTEM DESIGN AND OPERAT
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TABLE OF CONTENTS Collection, Analy
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9.1.2. Shutdown Procedure 9.1.3. SI
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Signal Sensor 26 Cruise Control Acc
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Figure 4.1: Air-Weigh System Compon
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4.5. EDAQ The eDAQ Lite is connecte
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the equipment inside from overheati
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5.4. Weather Station Connections Fi
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Figure 5.5.3: VBOX Cable Table 5.5.
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Figure 6.3: Weather Station Locatio
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6.6 Power Supply Use a second cable
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can also be changed from this windo
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on and the yellow light should begi
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Figure 7.1.3-1: Weather Station and
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Speed unit: m/s Sampling frequency:
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iii) Click on Browse to find and op
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c) Power Controller (Power) - no co
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1) Select the Transducer and Messag
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Figure 7.2.4-2: Adding Transducer C
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Figure 7.2.4-3: Editing DIO Vehicle
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A. Changing Units - Converting tran
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C. Coordinates - Assign correct sig
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switch) Table 7.2.5: Contents of Ti
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Trigger Delay Setup: Check Enable d
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Figure 7.2.5-6: Time Base Shifter S
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Units: Full Scale Estimate - Min: 0
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Figure 7.2.6-2: Burst History Data
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Press Ctrl+3 (or click Run in the T
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8.2. Procedures for Heavy Truck Dut
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d) Turn on the ignition. e) Turn on
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4) Change the IP address field to t
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5) Check to see that the computer c
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e) When all SIC files have been sav
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4) To verify the time setting, go t
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e) Alter the id, mask, value, etc.
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Longitudinal CAN 0x87FF 0x0304 Long
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ULSB 0x98EAFFFBC1FE00 MaxVehSpLm CA
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Figure 9.1.6-2: Performing a ColdSt
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Table 9.2.2-1: Descriptions and Uni
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Channel ID Sensor Format Integer? P
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Channel ID eDAQ Invalid Data Value
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Channel ID Resolution Accuracy Note
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9.3. Appendix C: Additional DAS Not
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e replicated and saved as five iden
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2) Type in “cmd” 3) Type “ipc
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Producing multiple setup files 1) G
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9.4. Appendix D: Checklists and For
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D-96
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9.5. Appendix E: References and Web
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E-3
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E-5
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E-7
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E-9
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E-11
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E-13
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E-15
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E-17
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E-19
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E-21
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E-23
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E-25
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E-27
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E-29
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E-31
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E-33
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E-35
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E-37
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E-39
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E-41
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