1 / 29

Group 2: Christina Graziose Dave Lund Milan Nguyen

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. Agenda. Background Problem Statement and Scope Assumptions Bottom Line Up Front System Approach

osma
Télécharger la présentation

Group 2: Christina Graziose Dave Lund Milan Nguyen

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

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

  3. 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

  4. Problem Statement and Scope • Problem • Determine if skeg mods improved fuel consumption • Develop mathematical model • Calculate propulsion fuel consumption and determine skegmod 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

  5. Assumptions • When ship is not-underway, power generated solely supports hotel load • Propulsion power can be sufficiently estimatedby 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) • Fuel Consumption • All vessels had fuel reduction post skegmodification • 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 • µfuel consumption 1= µfuel 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. System • 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

  8. Approach • The studywas 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 skegmods • Predicted fuel consumption should start to deviate from actual fuel consumption when skegmods occurred

  10. Model Baseline Monthly Fuel Data Speed Power Data Hourly Ship Log Data Outlier Analysis Outlier Analysis Regression Model for Speed Power Relationship 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) Calculate Monthly Fuel Consumption (qry-102) Compute Monthly Fuel Consumption Residuals (qry-105, qry-106) = Input = Process Plot Residuals to Identify Fuel Consumption Trends = Output

  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

  12. Tables

  13. Queries

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

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

  16. 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 Majority of outliers due to missing data

  17. Missing Ship Log Data Sensitivity • Sensitivity analysis on monthly data • 65%, 75%, and 85% of monthly data analyzed • Total variation (sum of squares) • Average variability (sample variance) Sample Variance 75% has low average variability

  18. 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

  19. 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 Estimate of 800 kW for hotel load is reasonable

  20. 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 BSFC: Brake Specific Fuel Consumption

  21. Calculate Sea State Factor • 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

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

  23. 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

  24. 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 Other modifications resulted in fuel consumption reductions Other Mods: Gondola, Bubble Fence, and Bilge Keel Skeg Extension

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

  26. 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

  27. Conclusions • Fuel Consumption • All vessels had fuel reduction post skegmodification • 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 • µfuel consumption 1= µfuel 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 • 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. Questions? https://sites.google.com/site/TAGSFuelStudy

More Related