Findings and Conclusions

The purpose of the study was to develop a method and determine whether the modifications made to the vesselís skeg had an effect on the fuel consumption, and if so, did the fuel consumption increase, decrease, or remain the same.  The Team created a mathematical model in Microsoft Access that predicts the vesselís fuel consumption based on its speed and sea state level encountered.

The model development involved analyzing the speed vs. power consumption, estimating the sea state effect on fuel consumption, and estimating the vesselís engine fuel consumption.  The finished model closely resembles the real world data.  The Team performed t-tests to determine if the model calculations were statistically equivalent to the real world recorded data.  With 95 percent confidence, the predicted and recorded data were statistically equivalent for four of the six vessels. 

The second part of the problem was to determine if the skeg modifications resulted in fuel consumption reductions.  Overall, all vessels (with the exception of Mary Sears) experienced a reduction in fuel consumption post-skeg modifications.  Based on the fuel consumption savings, the Team determined the expected monetary savings.  Using the cost of diesel fuel of $3.86 (current cost as of 15 April), all vessels, with the exception of Mary Sears, had a cost savings with the Pathfinder having the largest savings of over $2 per gallon.


The Team identified recommendations for possible future analysis.  The first recommendation is to analyze the effects sea stats has on the fuel consumption of the T-AGS vessel.  This recommendation is twofold, the first would be to address the fact that this subject is not widely studied and the Team experienced difficulties in determining the exact factor to associate to each sea state.  In order to completely determine the sea state affect, it is anticipated that modeling, to possibly include, simulation would be needed.  Not only would this analysis improve the quality of the model developed during this project, but it would serve was a benchmark for all other vessels interested in determining the effect of sea state.  The second aspect of this recommendation would be to perform a sensitivity analysis on the sea state effect.  Based on the factors the Team associated with each sea state during this project, the sea state did not have a large impact on the fuel consumption.  If the first part of the recommendation cannot be accomplished, the second part would provide insight into the effect sea state could have on the fuel consumption.

The second recommendation would be to improve the quality of the data recorded from the T-AGS vessel.  As discussed in the outlier analysis, a large amount of data (especially monthly outlier analysis) was removed due to outliers or lack of data.  Possible suggestions for data quality improvements are to research possible methods of automating the data collection and/or performing daily or weekly data analysis to capture outliers early.  If outliers are captured early, there is a potential that the data could be corrected.

The final recommendation would be to continuously update and improve the mathematical model developed during the project.  The model developed only incorporated sea state and speed in the calculation of fuel consumption.  Other possible variables that would be of interest to determine their effect on fuel consumption would be the wind speed/direction, water temperature, and vary the weight of the vessel during the mission.  In order to analyze the effect of the weight of the vessel on fuel consumption, data on when the vessel was refueled and the amount of fuel would need to be known.  Also, the model does not vary the BSFC factor in respects to the vessels speed.  Updating the model to incorporate (possibly) different BSFC factor for each vessel speed could improve the predicted fuel consumption.  Most likely, the best approach for determining the BSFC factor for each speed would be to develop a regression analysis (similar to the speed vs. power curve) so that a BSFC factor can be calculated regardless of vessel speed.


The Team provided the customer with a final report documenting the results of the study, the Microsoft Access file used during the mathematical modeling of the problem, and a model cheat sheet that provides a brief overview of all tables and queries contained in the model (included in the final report as Appendix D).  These deliverables were provided to the customer electronically on 6 May 2013. 



The subsequent sections discuss how the Team will allocate resources and the studyís proposed schedule.

Resource Allocation

The Team was composed of two operations research analysts and one systems engineer.  Each team member was assigned specific roles and responsibilities that they are accountable for completing to ensure that the study is competed in a timely manner and is of Customerís quality standards.  The roles and responsibilities of each team member are identified in the table below.

Table 1.  Roles and Responsibilities




Study Lead

Manage schedule

Assign tasks

Primary contact with customer

Tina Graziose

Operations Research Analysts

Data analysis

Dave Lund

Milan Nguyen

Systems Engineer

Data analysis

Tina Graziose

Web Developer

Design webpage

Dave Lund



The following figure illustrates the schedule for the study.

Figure 26.  Study Schedule