1. Where Innovation Is Tradition Group 2: Christina Graziose Dave Lund Milan Nguyen 1 Determining the Efficacy of Modifications to T-AGS 60 Ships (DEMoTAGS) Sponsor: Mr. Gregory Opas, Merrill-Dean Consulting

2. Agenda Background Problem Statement and Scope Assumptions Bottom Line Up Front System Approach Model Overview Data Analysis Identification of Modifications Effects Recommendations Conclusion 2

3. Background 3 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

4. 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 4

5. Assumptions 5 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

6. Bottom Line Up Front (BLUF) 6 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 fuel consumption 1 fuel consumption 2 fuel consumption 6 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

7. 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 7

8. Approach 8 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

9. 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 9

10. Regression Model for Speed Power Relationship Calculate Hourly Power in kW and HP (qry-103) Calculate Hourly Power in kW and HP (qry-103) Calculate Hourly Fuel Consumption (qry-103) Calculate Hourly Fuel Consumption (qry-103) Compute Monthly Fuel Consumption Residuals (qry-105, qry-106) Compute Monthly Fuel Consumption Residuals (qry-105, qry-106) Calculate Sea State Factor (qry-101) Calculate Sea State Factor (qry-101) Plot Residuals to Identify Fuel Consumption Trends Outlier Analysis ModelBaseline Aggregate Hourly into Monthly Fuel Consumption (qry-104) Aggregate Hourly into Monthly Fuel Consumption (qry-104) Speed Power Data Hourly Ship Log Data Monthly Fuel Data Calculate Monthly Fuel Consumption (qry-102) Calculate Monthly Fuel Consumption (qry-102) = Input = Process = Output 10

11. 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 11

12. Tables 12

13. Queries 13

14. ShipLog Table Contains ship log entries - recorded every few hours 14 Largest data table containing over 42,000 records

15. MonthlyConsumption Table Stores monthly barrels of fuel consumed and hours of operation while Underway and Not-underway 15

16. 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 16 Site Name Total Months Months with No Data Months With < 75% Data Usable Months Percent Unusable Months USNS Bowditch963033 66% USNS Heezen9614384454% USNS Henson9641451090% USNS Mary Sears96446 52% USNS Pathfinder9642342079% USNS Sumner963484553% Majority of outliers due to missing data

17. 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 17 75% has low average variability Sample Variance

18. Regression Model for Speed vs. Power 18 Relationship used for the mathematical model R 2 values used to determine correlation R 2 value close to 1 indicates high correlation between curve and data points Used polynomial equation in model implementation

19. 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 19 Site NameMeanMedianStd DevConfidence Interval USNS Bowditch801.85773.45286.85[857.79, 745.91] USNS Heezen880.39879.24344.77[950.84, 809.94] USNS Henson747.64704.97329.73[810.11, 685.16] USNS Mary Sears759.08783.30122.66[783.87, 734.28] USNS Pathfinder871.33792.55340.46[937.08, 805.58] USNS Sumner831.04783.30378.31[907.93, 754.15] Overall814.18783.30300.46 Estimate of 800 kW for hotel load is reasonable

20. 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 20 Caterpillar C280-8 Marine Propulsion Engine (3,634 HP) Engine Speed (rpm)Power (bhp) BSFC (lbs/hp-hr) Fuel Rate (gal/hr) 5003860.3921.5 6006670.37936 6307730.37641.4 7001,0600.3755.9 7501,3030.36467.7 8001,5820.35880.6 8501,8970.35295.1 9102,3280.352116.8 9502,6490.355133.9 1,0003,0900.351154.8 Average0.36 BSFC: Brake Specific Fuel Consumption

21. 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 21 Sea StateWave Height (m)Wave Height (ft)Sea State FactorDescription 0001Calm (glassy) 10.10.331Calm (rippled) 20.51.641Smooth (wavelets) 31.254.11Slight 42.58.21.016Moderate 5413.121.094Rough 6619.691.165Very rough 7929.531.224High 81445.931.271Very high 92065.621.306Phenomenal

22. Output Analysis (1 of 3) 22 Skeg Mod & Other Mods Model calculations vs. recorded data Model underestimated FC prior to mod and was more accurate post mod

23. 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) 23 Model sufficiently represents real-life data

24. 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) 24 Vessel Average Fuel Consumption Post- Skeg Mod Average Fuel Consumption Post- Other Mod DifferencePercent Savings USNS Heezen157.81 gal/hr136.67 gal/hr21.14 gal/hr13.4% Other modifications resulted in fuel consumption reductions Other Mods: Gondola, Bubble Fence, and Bilge Keel Skeg Extension

25. Skeg Mod Effects on Fuel Consumption 25 Skeg mod effect on UW fuel consumption Overall reduction in average fuel consumption

26. Skeg Mod Effects on Cost 26 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

27. 27 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 fuel consumption 1 fuel consumption 2 fuel consumption 6 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

28. Recommendations 28 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

29. Where Innovation Is Tradition Questions? 29 https://sites.google.com/site/TAGSFuelStudy