Determining the Efficacy of Modifications to
T-AGS 60 Ships (DEMoTAGS)
Group 2:
Christina Graziose
Dave Lund
Milan Nguyen
Sponsor:
Mr. Gregory Opas,
Merrill-Dean Consulting
Where Innovation Is Tradition
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Agenda
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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 Statement and Scope
• 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
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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
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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 same
µ fuel consumption 1 = µ fuel consumption 2= … = µ fuel consumption 6
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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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System
• Multiple variables affect ship fuel
consumption:
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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
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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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Model
Speed Power Data
Regression Model
for Speed Power
Relationship
Baseline
Hourly Ship Log
Data
Monthly Fuel Data
Outlier Analysis
Outlier Analysis
Calculate Hourly
Power in kW and HP
(qry-103)
Calculate Sea
State Factor
(qry-101)
Calculate Hourly
Fuel Consumption
(qry-103)
Aggregate Hourly
into Monthly Fuel
Consumption
(qry-104)
= Input
= Process
= Output
Calculate Monthly
Fuel Consumption
(qry-102)
Compute Monthly
Fuel Consumption
Residuals
(qry-105, qry-106)
Plot Residuals to
Identify Fuel
Consumption Trends
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Model Implementation
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Model was implemented using Microsoft Access
Three major data sets provided:
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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
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Contains ship log entries - recorded every few hours
Largest data table containing over 42,000 records
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MonthlyConsumption Table
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Stores monthly barrels of fuel consumed and hours of
operation while Underway and Not-underway
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Data Analysis
• 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
Site Name
USNS Bowditch
USNS Heezen
USNS Henson
USNS Mary Sears
USNS Pathfinder
USNS Sumner
Total
Months
96
96
96
96
96
96
Months with
No Data
30
14
41
4
42
3
Months With
< 75% Data
33
38
45
46
34
48
Usable
Months
33
44
10
46
20
45
Majority of outliers due to missing data
Percent Unusable
Months
66%
54%
90%
52%
79%
53%
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Missing Ship Log Data Sensitivity
Data Variation - Sum of Squares for Recorded
Propulsion Fuel Consumption
• Sensitivity analysis on monthly
data
3000000
• 65%, 75%, and 85% of monthly
data analyzed
• Total variation (sum of squares)
• Average variability (sample
variance)
2000000
USNS Sumner
USNS Pathfinder
1500000
USNS Mary Sears
USNS Henson
1000000
USNS Heezen
500000
USNS Bowditch
0
65
75
Data Variation - Sample Variance for Recorded
Propulsion Fuel Consumption
85
Percentage of Monthly Data Required for Analysis
100000
90000
80000
75% has low average variability
Sample
Squares
Sum ofVariance
The
The Sum of Squares
2500000
70000
USNS Sumner
60000
USNS Pathfinder
50000
USNS Mary Sears
40000
USNS Henson
30000
USNS Heezen
20000
USNS Bowditch
10000
0
65
75
85
Percentage of Monthly Data Required for Analysis
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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
Speed Power Curve
10000
9000
y = 2.8837x3 - 39.889x2 + 247.63x + 800
R² = 0.9837
8000
Power (kW)
7000
6000
5000
Power(kW)
4000
Poly. (Power(kW))
3000
y = 728.86e0.1219x
R² = 0.9595
2000
Expon. (Power(kW))
1000
0
0
5
10
15
20
Speed (kts)
Used polynomial equation in model implementation
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Estimating Hotel Load
• 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
Site Name
USNS Bowditch
USNS Heezen
USNS Henson
USNS Mary Sears
USNS Pathfinder
USNS Sumner
Overall
Mean
801.85
880.39
747.64
759.08
871.33
831.04
814.18
Median
773.45
879.24
704.97
783.30
792.55
783.30
783.30
Std Dev Confidence Interval
286.85
[857.79, 745.91]
344.77
[950.84, 809.94]
329.73
[810.11, 685.16]
122.66
[783.87, 734.28]
340.46
[937.08, 805.58]
378.31
[907.93, 754.15]
300.46
Estimate of 800 kW for hotel load is reasonable
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Estimating Engine Fuel Consumption
• 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
Caterpillar C280-8 Marine Propulsion Engine (3,634 HP)
Engine Speed
BSFC
Fuel Rate
(rpm)
Power (bhp) (lbs/hp-hr)
(gal/hr)
500
386
0.39
21.5
600
667
0.379
36
630
773
0.376
41.4
700
1,060
0.37
55.9
750
1,303
0.364
67.7
800
1,582
0.358
80.6
850
1,897
0.352
95.1
910
2,328
0.352
116.8
950
2,649
0.355
133.9
1,000
3,090
0.351
154.8
Average
0.36
BSFC: Brake Specific Fuel Consumption
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Calculate Sea State Factor
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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
Sea State
Wave Height (m)
Wave Height (ft)
Sea State Factor
Description
0
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
Slight
4
2.5
8.2
1.016
Moderate
5
4
13.12
1.094
Rough
6
6
19.69
1.165
Very rough
7
9
29.53
1.224
High
8
14
45.93
1.271
Very high
9
20
65.62
1.306
Phenomenal
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Oct-2012
Jun-2012
Feb-2012
Oct-2011
Jun-2011
Feb-2011
Oct-2010
Jun-2010
Feb-2010
Oct-2009
Jun-2009
Feb-2009
Oct-2008
Jun-2008
Feb-2008
Oct-2007
Jun-2007
Feb-2007
Oct-2006
Jun-2006
Feb-2006
Oct-2005
Jun-2005
Feb-2005
Oct-2004
Jun-2004
Feb-2004
Oct-2003
Jun-2003
Feb-2003
Oct-2002
Output Analysis (1 of 3)
• Model calculations vs. recorded data
Sumner - Propulsion Fuel Consumption
300
Skeg Mod &
Other Mods
250
200
150
Predicted Prop FC
100
Recorded Prop FC
50
0
Model underestimated FC prior to mod and was more accurate post mod
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Output Analysis (2 of 3)
• 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
Model sufficiently represents real-life data
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Output Analysis (3 of 3)
• Skeg modification data identified dates of “other” modifications
• Analyzed effect of other modifications on fuel consumption
• Between modifications
• After all modifications
Vessel
USNS Heezen
Average Fuel
Consumption
Post- Skeg Mod
157.81 gal/hr
Average Fuel
Consumption
Post- Other Mod
Difference
Percent Savings
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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Skeg Mod Effects on Fuel Consumption
• Skeg mod effect on UW fuel consumption
Avg Yearly FC Before
Site Name
Mod (gal/hr)
USNS Bowditch
157.12
USNS Heezen
150.54
USNS Henson
168.88
USNS Mary Sears
185.78
USNS Pathfinder
234.66
USNS Sumner
216.33
Overall
185.42
Avg Yearly FC After
Avg Yearly
Pct
Mod (gal/hr)
Savings (gal/hr) Savings
129.59
27.53
17.5%
147.67
2.87
1.9%
146.87
22.01
13.0%
171.63
14.16
7.6%
155.11
79.55
33.9%
162.80
53.53
24.7%
153.78
31.64
17.1%
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
Before Skeg Mod
Avg Yearly
Site Name
Fuel (gal)
USNS Bowditch
834,636
USNS Heezen
932,700
USNS Henson
1,016,513
USNS Mary Sears
1,105,907
USNS Pathfinder
1,340,815
USNS Sumner
1,316,621
Total
6,547,192
After Skeg Mod
Avg Yearly Avg Yearly
Fuel Cost
Fuel (gal)
$ 3,221,695
664,677
$ 3,600,222
921,992
$ 3,923,742
856,718
$ 4,268,799
1,024,580
$ 5,175,545
905,664
$ 5,082,159
937,870
$ 25,272,162
5,311,501
Avg Yearly
Fuel Cost
$ 2,565,654
$ 3,558,887
$ 3,306,932
$ 3,954,879
$ 3,495,864
$ 3,620,176
$ 20,502,393
Savings
Avg Yearly
Fuel Savings
Avg Yearly
(gal)
Cost Savings
169,959 $
656,040
10,708 $
41,335
159,795 $
616,810
81,327 $
313,920
435,151 $ 1,679,681
378,752 $ 1,461,982
1,235,691 $ 4,769,769
Total expected monetary savings per year of ~$4.8 million
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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 same
µ 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
27
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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Questions?
https://sites.google.com/site/TAGSFuelStudy
Where Innovation Is Tradition
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