Group 2: Christina Graziose Dave Lund Milan Nguyen
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Transcript of Group 2: Christina Graziose Dave Lund Milan Nguyen
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1Where Innovation Is TraditionWhere Innovation Is Tradition
Group 2:Christina GrazioseDave Lund Milan Nguyen
Determining the Efficacy of Modifications to T-AGS 60 Ships (DEMoTAGS)
Sponsor: Mr. Gregory Opas, Merrill-Dean Consulting
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Agenda• Background• Problem Statement and Scope• Assumptions• Bottom Line Up Front• System• Approach• Model Overview• Data Analysis• Identification of Modifications Effects• Recommendations• Conclusion
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Background• US Navy operates a fleet of 6 T-AGS Class Oceanographic Survey vessels
• Powered by 2 Z-drives: provide propulsion and directional control of the vessel• Recent ship modifications were made to enlarge the skeg• Towing tank and computational fluid dynamics analyses performed prior to mods
• Analyses suggested a level of fuel savings would occur• No comprehensive analysis of performance improvements done after the mods• T-AGS vessels operate in one of three modes:
• Underway (UW): vessel is moving and producing its own power• Not-underway (NUW): vessel is anchored and producing its own power• Cold iron: vessel is docked and receives power from outside generators
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• Problem• Determine if skeg mods improved fuel consumption• Develop mathematical model
• Calculate propulsion fuel consumption and determine skeg mod effects on fuel efficiency based on ship speed and sea state
• Scope• Only UW and NUW will be analyzed
• NUW data will identify the hotel load power requirements• Overall, determine how skeg mods affected ship fuel consumption when UW
Problem Statement and Scope
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Assumptions• When ship is not-underway, power generated solely supports
hotel load• Propulsion power can be sufficiently estimated by taking
underway power and subtracting not-underway power• Skeg mods do not affect the hotel load• No additional power is generated beyond what is needed to
support hotel load or propulsion power• Weight of diesel fuel is 7.2 lbs/gal• Weight of the vessel is constant• Ship speed and sea state are the primary variables that affect
fuel consumption
*All assumptions were approved by customer
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Bottom Line Up Front (BLUF)• Fuel Consumption
• All vessels had fuel reduction post skeg modification• Reduced average yearly fuel consumption by 17%• Average yearly savings of ~$4.8 million
• Other modifications• Provided additional reductions in fuel consumption
• ANOVA to test if fuel consumption amongst vessels are the sameµ fuel consumption 1 = µ fuel consumption 2= … = µ fuel consumption 6
• Evidence of a difference between each vessel’s fuel consumption• Mathematical Model
• Calculated average fuel consumption based on speed and sea state
Model accurately represents actual data Skeg mods resulted in yearly savings of ~$4.8 million
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• Multiple variables affect ship fuel consumption:• Ocean Current• Wind• Temperature• Speed• Sea State• Others
• Analyzed the effect of speed and sea state on the ship’s fuel consumption • Additive effect on the resistance acting on the ship
System
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Approach• The study was completed through three tasks
• Task 1: Data Collection and Literature Research• Task 2: Data Analysis and Model Development• Task 3: Findings and Conclusions
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Model Overview
• Goal of model to predict ship fuel consumption based on power consumption• Speed and sea state are major parameters used to calculate power
consumption• Hypothesis:
• Predicted fuel consumption will not be affected by skeg mods since it is computed from speed
• Actual fuel consumption will be affected by skeg mods• Predicted fuel consumption should start to deviate from actual fuel
consumption when skeg mods occurred
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Regression Model for Speed Power
Relationship
Calculate Hourly Power in kW and HP
(qry-103)
Calculate Hourly Fuel Consumption
(qry-103)
Compute Monthly Fuel Consumption
Residuals(qry-105, qry-106)
Calculate Sea State Factor(qry-101)
Plot Residuals to Identify Fuel
Consumption Trends
Outlier Analysis Outlier Analysis
Model Baseline
Aggregate Hourly into Monthly Fuel
Consumption(qry-104)
Speed Power Data Hourly Ship Log Data
Monthly Fuel Data
Calculate Monthly Fuel Consumption
(qry-102)
= Input
= Process
= Output
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Model Implementation
• Model was implemented using Microsoft Access• Three major data sets provided:
• Monthly Consumption and Op Hours• Ship Logs• Speed versus Power data
• Tables were created to store data • Queries were built to process the data
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Tables
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Queries
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ShipLog Table• Contains ship log entries - recorded every few hours
Largest data table containing over 42,000 records
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MonthlyConsumption Table• Stores monthly barrels of fuel consumed and hours of
operation while Underway and Not-underway
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• Outlier Analysis:• Anderson-Darling normality test• Histograms • Boxplots (with fences)
• MonthlyConsumption Outlier Results:• Underway Fuel Consumption: 5.97% of data• Not-underway Fuel Consumption: 19.95% of data
• Missing ShipLog Data:• Excluded months with less than 75% of daily data
Data Analysis
Site NameTotal
MonthsMonths with
No DataMonths With < 75% Data
Usable Months
Percent Unusable Months
USNS Bowditch 96 30 33 33 66%USNS Heezen 96 14 38 44 54%USNS Henson 96 41 45 10 90%USNS Mary Sears 96 4 46 46 52%USNS Pathfinder 96 42 34 20 79%USNS Sumner 96 3 48 45 53%
Majority of outliers due to missing data
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0
500000
1000000
1500000
2000000
2500000
3000000
65 75 85
The
Sum
of S
quar
es
Percentage of Monthly Data Required for Analysis
Data Variation - Sum of Squares for Recorded Propulsion Fuel Consumption
USNS Sumner
USNS Pathfinder
USNS Mary Sears
USNS Henson
USNS Heezen
USNS Bowditch
• Sensitivity analysis on monthly data• 65%, 75%, and 85% of monthly
data analyzed• Total variation (sum of squares)• Average variability (sample
variance)
Missing Ship Log Data Sensitivity
75% has low average variability
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
65 75 85
The
Sum
of S
quar
es
Percentage of Monthly Data Required for Analysis
Data Variation - Sample Variance for Recorded Propulsion Fuel Consumption
USNS Sumner
USNS Pathfinder
USNS Mary Sears
USNS Henson
USNS Heezen
USNS Bowditch
Sam
ple
Varia
nce
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Regression Model for Speed vs. Power• Relationship used for the mathematical model• R2 values used to determine correlation
• R2 value close to 1 indicates high correlation between curve and data points
Used polynomial equation in model implementation
0 2 4 6 8 10 12 14 16 18 200
100020003000400050006000700080009000
10000
f(x) = 2.88372411842636 x³ − 39.8892382137251 x² + 247.62591026939 x + 800R² = 0.994824444496485
f(x) = 728.856269704421 exp( 0.121870690566197 x )R² = 0.95954534829718
f(x) = 22.7902629099067 x^1.98739306345643R² = 0.962540655652399
Speed Power Curve
Power(kW)Polynomial (Power(kW))Exponential (Power(kW))Power (Power(kW))
Speed (kts)
Pow
er (k
W)
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• Following formula was used for the conversion:• Fuel Consumption = (Specific Fuel Consumption * HP) / Fuel Weight
• Specific Fuel Consumption = 0.36 lbs/hp/hr• Fuel Weight (Diesel) = 7.2 lbs/gal
• Solved for HP and converted to kW by multiplying by 0.746• Histograms were developed for hotel loads
• Most frequent hotel load: ~800 kW range
Estimating Hotel Load
Site Name Mean Median Std Dev Confidence IntervalUSNS Bowditch 801.85 773.45 286.85 [857.79, 745.91]USNS Heezen 880.39 879.24 344.77 [950.84, 809.94]USNS Henson 747.64 704.97 329.73 [810.11, 685.16]USNS Mary Sears 759.08 783.30 122.66 [783.87, 734.28]USNS Pathfinder 871.33 792.55 340.46 [937.08, 805.58]USNS Sumner 831.04 783.30 378.31 [907.93, 754.15]Overall 814.18 783.30 300.46
Estimate of 800 kW for hotel load is reasonable
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• Engine Fuel Consumption Estimate:• Caterpillar marine propulsion engine fuel consumption of 0.36 lb/hp-hr
• Engine HP is comparable to that of the T-AGS engines
Estimating Engine Fuel Consumption
Caterpillar C280-8 Marine Propulsion Engine (3,634 HP)Engine Speed
(rpm) Power (bhp)BSFC
(lbs/hp-hr)Fuel Rate (gal/hr)
500 386 0.39 21.5600 667 0.379 36630 773 0.376 41.4700 1,060 0.37 55.9750 1,303 0.364 67.7800 1,582 0.358 80.6850 1,897 0.352 95.1910 2,328 0.352 116.8950 2,649 0.355 133.9
1,000 3,090 0.351 154.8Average 0.36
BSFC: Brake Specific Fuel Consumption
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• Used World Meteorological Organization (WMO) sea state codes• Sea state did not have an appreciable effect on fuel consumption• Sea state resistance curves were used to estimate Sea State Factor• Sea states 0 to 4 had a minimal impact on propulsion power• Sea states 5 to 9 had considerable impact on propulsion power
Calculate Sea State Factor
Sea State Wave Height (m) Wave Height (ft) Sea State Factor Description0 0 0 1 Calm (glassy)1 0.1 0.33 1 Calm (rippled)2 0.5 1.64 1 Smooth (wavelets)3 1.25 4.1 1 Slight4 2.5 8.2 1.016 Moderate5 4 13.12 1.094 Rough6 6 19.69 1.165 Very rough7 9 29.53 1.224 High8 14 45.93 1.271 Very high9 20 65.62 1.306 Phenomenal
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Output Analysis (1 of 3)
Oct-200
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Sumner - Propulsion Fuel Consumption
Predicted Prop FCRecorded Prop FC
Skeg Mod &Other Mods
• Model calculations vs. recorded data
Model underestimated FC prior to mod and was more accurate post mod
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• Analysis of Mathematical Model Data• Analyzed ratio of the predicted to recorded fuel consumption
• 90% of the calculated UW data was within +/- 30% of the recorded UW data• ANOVA to test average fuel consumption amongst vessels
Output Analysis (2 of 3)
Model sufficiently represents real-life data
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• Skeg modification data identified dates of “other” modifications• Analyzed effect of other modifications on fuel consumption
• Between modifications• After all modifications
Output Analysis (3 of 3)
VesselAverage Fuel Consumption
Post- Skeg Mod
Average Fuel Consumption
Post- Other ModDifference Percent Savings
USNS Heezen 157.81 gal/hr 136.67 gal/hr 21.14 gal/hr 13.4%
Other modifications resulted in fuel consumption reductions
Other Mods: Gondola, Bubble Fence, and Bilge Keel Skeg Extension
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Site NameAvg Yearly FC Before
Mod (gal/hr)Avg Yearly FC After
Mod (gal/hr)Avg Yearly
Savings (gal/hr)Pct
SavingsUSNS Bowditch 157.12 129.59 27.53 17.5%USNS Heezen 150.54 147.67 2.87 1.9%USNS Henson 168.88 146.87 22.01 13.0%USNS Mary Sears 185.78 171.63 14.16 7.6%USNS Pathfinder 234.66 155.11 79.55 33.9%USNS Sumner 216.33 162.80 53.53 24.7%Overall 185.42 153.78 31.64 17.1%
Skeg Mod Effects on Fuel Consumption• Skeg mod effect on UW fuel consumption
Overall reduction in average fuel consumption
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Skeg Mod Effects on Cost• Cost savings
• Used diesel fuel costs of $3.86 (current cost as of 15 April)• Cost Savings based on recorded average UW fuel consumption
Total expected monetary savings per year of ~$4.8 million
Avg Yearly Fuel (gal)
Avg Yearly Fuel Cost
Avg Yearly Fuel (gal)
Avg Yearly Fuel Cost
Avg Yearly Fuel Savings
(gal)Avg Yearly
Cost SavingsUSNS Bowditch 834,636 3,221,695$ 664,677 2,565,654$ 169,959 656,040$ USNS Heezen 932,700 3,600,222$ 921,992 3,558,887$ 10,708 41,335$ USNS Henson 1,016,513 3,923,742$ 856,718 3,306,932$ 159,795 616,810$ USNS Mary Sears 1,105,907 4,268,799$ 1,024,580 3,954,879$ 81,327 313,920$ USNS Pathfinder 1,340,815 5,175,545$ 905,664 3,495,864$ 435,151 1,679,681$ USNS Sumner 1,316,621 5,082,159$ 937,870 3,620,176$ 378,752 1,461,982$ Total 6,547,192 25,272,162$ 5,311,501 20,502,393$ 1,235,691 4,769,769$
Site Name
Before Skeg Mod After Skeg Mod Savings
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Conclusions• Fuel Consumption
• All vessels had fuel reduction post skeg modification• Reduced average yearly fuel consumption by 17%• Average yearly savings of ~$4.8 million
• Other modifications• Provided additional reductions in fuel consumption
• ANOVA to test if fuel consumption amongst vessels are the sameµ fuel consumption 1 = µ fuel consumption 2= … = µ fuel consumption 6
• Evidence of a difference between each vessel’s fuel consumption• Mathematical Model
• Calculated average fuel consumption based on speed and sea state
Model accurately represents actual data Skeg mods resulted in yearly savings of ~$4.8 million
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Recommendations• Further analysis on sea state effects on fuel consumption
• Perform sensitivity analysis on sea state factors• Perform study to determine exact sea state factors for a T-AGS vessel
• Improve recorded data quality• Daily or weekly data validity checks to capture outliers• Research methods for automatic data recording
• Mathematical model improvements• Incorporate additional variables that affect fuel consumption
• Wind speed/direction• Water Temperature• Variable total fuel weight during mission
• Would require refueling information• Vary BSFC based on vessel speed
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29Where Innovation Is TraditionWhere Innovation Is Tradition
Questions?
https://sites.google.com/site/TAGSFuelStudy