loh-ch4-8-2012

Modelling and Simulation of Marine Power and Propulsion Systems

Melvin Loh (113807) Page 46

4.0 SOFTWARE DEVELOPMENT

This chapter discusses about the code development of the software. Initial research and planning were required in order to obtain the project objective. In this software development, the following steps were taken:

1. Identification of the required input and output data; 2. Mathematical modelling of the whole software; 3. Familiarisation with the LabVIEW software development suite; 4. Preliminary design of the front panel in LabVIEW (Software Interface); 5. Code development in the block diagram of LabVIEW (Formula Node); 6. Ability to select required generator power load with the generators in the market; and 7. Ability to extract data into Microsoft Excel to plot data.

LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) is a platform and development environment for a visual programming language from National Instruments. The functions of every components in the software developed are provided and this chapter explains how they are used to calculate the resistance and power required in each components of the DEP system. A brief description of the methodology of each method is given to reinforce the information developed by LabVIEW. A basic mathematical model is also provided to help the user understand the flow of how the program was developed. The block diagram developed for individual VI is displayed in APPENDIX B and the user manual on how to fully utilise the software is attached in APPENDIX C.



4.1 Software Rationale

A LabVIEW Project is a tool for organizing project files, NI hardware, configuration data, and build specifications as shown in Figure 4-1. Project information is stored in an .lvproj file. The "Project Explorer" window, shown below, is where you interact with LabVIEW projects:

Figure 4-1: Project file in LabVIEW

The software was developed to size the diesel-electrical power required in the vessel using the methods and equations outlined in Chapter 2 and 3. The basic structure of the software was formed based on four different virtual instruments (.vi) for analysis.

The software was designed so that the user could easily understand the operation of the program and be able to use it with ease. This was achieved by designing an intuitive VI with inputs accompanied by clear labels. A screenshot of the software VI are provided from Figure 4-2 to Figure 4-6.



Figure 4-2: Limitations check for regression based methods VI



Figure 4-3: Holtrop Resistance Prediction Algorithm



Figure 4-4: Lahtiharju (Hard Chine) Resistance Prediction Algorithm VI

Figure 4-5: Generators Selection VI



Figure 4-6: Plots VI

For future use, a future expansion of the software would be required. The database of the diesel generators and electrical motors would need to be expanded and the software needs to include water-jet propulsion, propeller selection and also propeller design analysis using off-design methods.

4.1.1 Limitations Check for Regression Based Methods VI

In the limitation check VI, the ship operating profile is check with Holtrop and Lahtiharju limitations to ensure the validity of the particular regression based method. The Boolean will indicate the validity of the regression based method. When the Boolean is green, it indicates that the parameters are within the range of the limitations and when the Boolean is red, this shows that the parameters are not suitable for the particular regression based method.

4.1.2 Holtrop Resistance Prediction Algorithm VI

In the Holtrop VI, the resistance will be predicted based on ship operating profile. The ship operating profile or ship coefficients are very important parameters in determining the Holtrop and Mennen resistance prediction algorithm. Once the input parameters are inputted into the software, run the VI and the software will calculate the total resistance and the required power for the diesel generators and electrical motors.

4.1.3 Lahtiharju Resistance Prediction Algorithm VI In the Lahtiharju VI, the same approach as Holtrop was used. Resistance of the vessel will be predicted based on the ship operating profile. The only difference is that the algorithm used will be different.



4.1.4 Generators Selection VI

In the generators selection VI, the user can go through a list of diesel generators in the market. For this project, a total of 48 diesel generators from Caterpillar, Cummins and Wartsila are used for the generators selection database. In the future, users can include more diesel generators in the market to ensure a better selection or sizing for the diesel-electric propulsion.

4.1.5 Plots VI

The following plots will be presented based on the selected generator: 1) Resistance Curve; 2) Effective power vs Ship speed; 3) Range vs Ship speed; 4) Endurance vs Ship Speed; and 5) Fuel consumption per mile vs Ship speed.

4.2 Structures

Structures contain sections of graphical code and control how and when the code inside is run. The most common execution structures are While Loops, For Loops and Case Structures which you can use to run the same section of code multiple times or to execute a different section of code based on some condition.

4.2.1 Formula Node

The Formula Node in the LabVIEW software is a convenient, text-based node you can use to perform complicated mathematical operations on a block diagram using the C++ syntax structure. It is most useful for equations that have many variables or are otherwise complicated. The text-based code simplifies the block diagram and increases its readability. Furthermore, you can copy and paste existing code directly into the Formula Node rather than recreating it graphically.

In addition to text-based equation expressions, the Formula Node can accept text-based versions of if statements, while loops, for loops, and do loops, which are familiar to C programmers. These programming elements are similar but not identical to those you find in C programming.

The MathScript Node implements similar functions but with the additional functionality of a full .m file compiler, making it useful as a textual language for signal processing, analysis, and math. LabVIEW MathScript is generally compatible with .m file script syntax, which is widely used by alternative technical computing software. For LabVIEW 2009 and later, the LabVIEW MathScript features are released separately in the LabVIEW MathScript RT Module.



Figure 4-7: Formula Node

The Formula Node is a window in the block diagram where you can write textual program code following the C-syntax. Using a Formula Node structure for mathematical expressions is often more convenient than building the expression using elementary blocks in the ordinary way in LabVIEW since it is easier to write and maintain textual mathematical expressions than drawing equivalent block diagram code.

4.2.2 While Loop

The While Loop executes the sub-diagram until the conditional terminal, an input terminal, receives a specific Boolean value. The conditional terminal in a While Loop behaves the same as in a For Loop with a conditional terminal. However, because the For Loop also includes a set iteration count, it does not run infinitely if the condition never occurs. The While Loop does not include a set iteration count and runs infinitely if the condition never occurs.

If a conditional terminal is Stop if true, you place the terminal of a Boolean control outside a While Loop, and the control is FALSE when the loop starts, an infinite loop is caused, as shown in the following example. An infinite loop will also be caused if the conditional terminal is Continue if True and the control outside the loop are set to TRUE.

4.2.3 Case Structure

A Case Structure is a LabVIEW primitive that dynamically selects which parts of code should execute. For this project, the case structure was used together with the Tab Control so that at the selected tab, the selected VI can be executed successfully.



4.3 Graph Indicators

Figure 4-8: Build XY Graph in LabVIEW

The most common graph indicators in this project are the XY graph. Figure 4-8 shows an example of building XY graph and this requires two inputs function to enable the plots

4.4 Write to Spreadsheet VI

Figure 4-9: Write to spreadsheet.vi in block diagram

LabVIEW provide the function of write to spreadsheet which enables users to extract data out of the software. This enables users to do further analysis using excel. This function will provide more flexibility for first time LabVIEW users.



4.5 Software Development

Figure 4-10: Software development flow diagram

The software development was developed into three stages: Research stage, Software Development stage and Final Software as shown in Figure 4-10.

In the research stage, investigation studies were carried out on regression based methods, diesel-electric propulsion, and the diesel generators and electrical motors in the market. To fully understand the regression based methods, numerical examples of Holtrop and Lahtiharju were calculated from the examples of the Holtrop and Lahtiharju papers. For diesel-electric propulsion, papers were collected although diesel-electric is widely used, but there were not many papers on diesel-electric propulsion. Lastly, sourcing for diesel generators and electrical motors were straight-forward and it will be by going through list of companys website and obtaining their product specifications in order to build up the database.

In the software development stage, it will be precisely translating the knowledge gained in the research stage to LabVIEW programming. For the development of the regression based methods, first it will be the development of limitations check for Holtrop and Lahtiharju and the development of Holtrop and Lahtiharju resistance prediction algorithm based on ship operating profile or ship coefficients. For the development of diesel-electric propulsion VI, the parameters will be entirely the calculated resistance and power from the Holtrop and Lahtiharju resistance prediction VI and the



electrical efficiencies for each electrical component in DEP. Lastly, once the database was built up in excel format from the research stage, it will be just reading the excel file and presenting it in LabVIEW. After calculating results for the required power for diesel generators and electrical motors in the diesel-electric propulsion VI, it will only involve the selection of the diesel generators or electrical motors from the database.

The final software will be just building individual VI file into the project file. After which, it will be presenting the data into plots by building XY graph in LabVIEW.



5.0 TESTING AND ANALYSIS OF SOFTWARE

In this chapter, testing, analysis and verification works will be carried out on existing DEP vessels, namely R/V G.O. Sars and SV290 to validate the ship resistance, number of generators and electrical motors onboard the research vessel. Besides that, the generator, electrical motors and electrical power load will be checked with the existing ship specification. This verification will test the programs reliability and accuracy. Both vessels specification are attached in APPENDIX A. R/V G.O. Sars has a full displacement hull powered by 3 Wartsila 2700kW diesel generators and is run by Wartsila/acbLIPS 5 bladed fixed pitch propeller with a speed of up to 17.5 knots. SV290 is powered by 4 Cummins QSK60-DM engines driving Tewac Marathon 744 generators, producing 1825kW at 1800RPM and the propulsion is provided by 2 Schottel combi-drives; model SCD-2020, driven by 2500kW electrical motors integrated to the units.

5.1 Simulation Study 1: R/V G.O. Sars

5.1.1 Limitations Check

A limitation check is carried out as shown in Figure 5-1. The result shows that for this particular vessel (R/V G.O. Sars), Holtrop resistance prediction algorithm is seen to be the only available regression based method.

Figure 5-1: Limitations check (R/V G.O. Sars)



5.1.2 Resistance Curve

Figure 5-2: Data inputs in Holtrop resistance prediction algorithm

Figure 5-3: R/V G.O Sars Resistance Curve drawn with Excel

The ship resistance is calculated based on the ship operating profile or ship coefficients as shown in Figure 5-2. In general, all ship resistance are proportional to the square of the speed, but for high speeds, the wave resistance increased much faster, thus contributing to a higher part of the total resistance. A further increase in the propulsion power may only result in negligible ship speed increases as most of the extra power will be converted into wave energy hence increasing the fuel consumption with a slight increase in ship speed.

Referring to Figure 5-2, it can be observed that frictional resistance contributes most of the resistance.

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As an initial test of the accuracy of the program, the hull form data of R/V G.O. Sars was entered and the resistance as determined by the software was compared with Hullspeed and the software package developed.

Figure 5-4: Resistance comparison (R/V G.O. Sars)

Figure 5-4 shows the validation of the softwares resistance prediction by Hullspeed with only 6% error.

5.1.3 Prediction of Required Power for DG and EM

Figure 5-5: R/V G.O. Sars Diesel-Electric Propulsion VI

From Figure 5-5, we can obtain required power for the diesel generators and electrical motors based on the calculated resistance from the Holtrop VI. As compared with the ship specification, it gives similar results for the required power for the diesel generators and electrical motors. This shows that the program has successfully predicted the required power for diesel generators and electrical motors.

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Table 5-1: Comparison of required power (R/V G.O. Sars)

Power (kW) Software Predicted Power (kW) 3 x Wartsila 6L32 Diesel Generators 8100 8164 1 x Wartsila 6L32 Diesel Generator 2700 2721 2 x Teco Westinghouse DC Motors 6000 7140 1 x Teco Westinghouse DC Motor 3000 3570

According to Table 5-1, the predicted results of required power are 8164kW for the diesel generators, and 7140kW for the electrical motors. The predicted power for the software gives a pretty close result with a 0.7% error. This provides sufficient information for the naval architects and marine engineers with sufficient information to carry on with the designing. Although the software predicted a much higher power required for the electrical motors, this might be due to insufficient data of the electrical efficiencies in the switchboard, transformers, frequency converters and the electrical motors itself. As such, the analysis was done based on a rough estimation. The efficiencies of the electrical components might be much lower compared to the typical efficiency as shown in Table 3-1.



5.1.4 Generators Selection

Figure 5-6: Generator selection from database

From Figure 5-6, it shows that the matching generator will be Wartsila 6L32 as it produces 2880kW of power. This matches with the ship specification as the vessel is using three Wartsila 6L32 diesel generators. The selection shows that the software has successfully predicted the required power for the diesel generators.



5.1.5 Range

With the range plots generated in Figure 5-7, the user can predict the cruising speed of the vessel and plan the route of the vessel in order to achieve a better fuel consumption for the vessel. As the ship speed increases, the specific fuel consumption will increases dramatically which in turns affects the range to be shorter referring to Figure 5-9.

Figure 5-7: Range of R/V G.O. Sars in LabVIEW



5.1.6 Endurance

Figure 5-8 gives the endurance plots of R/V G.O. Sars. With the endurance plots, user can identify how many days the vessel can last in the sea without bunkering. If the vessel is travelling at a higher speed, the endurance will be lower as a higher speed will require a higher specific fuel consumption.

Figure 5-8: Endurance of R/V G.O. Sars in LabVIEW



5.1.7 Specific Fuel Consumption

Based on the selected generator, Wartsila 6L32, the specific fuel consumption of the fuel consumption was plotted in Figure 5-9. The fuel consumption increases as the ship speed increases therefore in order to obtain a better fuel efficiency; it is recommended to plan the route based on the fuel available and the fuel consumption. This enable users to know the cruising speed and at which speed, the vessel will travel at the highest fuel efficiency.

Figure 5-9: Wartsila 6L32 specific fuel consumption in LabVIEW



5.2 Simulation Study 2: SV290

5.2.1 Limitations Check

A limitation check is carried out as shown in Figure 5-10. The result shows that for this particular vessel (SV290), the only regression based method is Holtrop resistance prediction algorithm.

Figure 5-10: Limitations check (SV290)

5.2.2 Resistance Curve

Referring to Figure 5-11, the resistance results of the model testing provided from the design company, STX Marine is compared against the resistance generated by the software. The resistance comparison shows that there are not many differences between the model test results and the software results. This again validates the resistance prediction algorithm developed in the software.



Figure 5-11: Resistance comparison (SV290)

5.2.3 Prediction of Required Power for DG and EM

Figure 5-12: SV290 Diesel-Electric Propulsion VI

From Figure 5-12, we can obtain the required power the diesel generators and electrical motors based on the calculated resistance from the Holtrop VI. The results generated from the software gives similar results to the ship specification. This shows that the program has reached its objective in predicting the required power for diesel generators and electrical motors.

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Table 5-2: Comparison of required power (SV290)

Power (kW) Software Predicted Power (kW) 4 x Tewac Marathon 744 Generators 7300 7308 1 x Tewac Marathon 744 Generator 1825 1827

2 x Schottel combi-drives 5000 6390 1 x Schottel combi-drive 2500 3195

According to Table 5-2, the predicted results of required power are 7308kW for the diesel generators, and 6390kW for the electrical motors. The predicted power for the software gives a relatively close result with a 0.1% error. This provides sufficient information for the naval architects and marine engineers with sufficient information to carry on with the designing. The same occurrence of predicted power of electrical motor happens in this simulation study. The software over-predicted the power required for the electrical motors.

5.2.4 Range

With the range plots generated, the user can predict the cruising speed of the vessel and plan the route of the vessel in order to achieve a better fuel consumption for the vessel as shown in Figure 5-13. Two generators namely, Tewac Marathon 744 generators and Caterpillar 3516B were compared in this simulation study.

Figure 5-13: Range of SV290 in LabVIEW



5.2.5 Endurance

Figure 5-14 presents the endurance plots of SV290. As previously mentioned, this simulation study has looked into two generators comparison to demonstrate the comparison function developed by the software in LabVIEW.

Figure 5-14: Endurance of SV290 in LabVIEW



5.2.6 Specific Fuel Consumption

The comparison between the Tewac Marathon 744 generator and the Caterpillar 3516B proves that the Tewac Marathon 744 generator has a better fuel consumption at the service or cruising speed as refer to Figure 5-15. This comparison function in LabVIEW will provide more options for the system designer during the selection of diesel generators.

Figure 5-15: Tewac Marathon and Caterpillar 3516B fuel consumption comparison



6.0 DISCUSSION

6.1 Modelling and Simulation of Marine Power and Propulsion Systems

In Chapter 3.0, a model of a complete marine power system is presented. The overall model consists of diesel generators, switchboard, transformers, frequency converters, electrical motors either synchronous or induction and propeller shaft.

The main objective of this project is to determine a solution of marine power options that is best match to the design ship operating profile. The ship resistance was predicted through either Holtrop or Lahtiharju based on the ship operating profile.

The main concern for choosing an ideally sized diesel generators and electrical motors is economical consideration. An oversized diesel generators and electrical motors will cost the ship owner much more. Important economic factors are fuel consumption, cost of the diesel generators or electrical motors and maintenance. Other than the economic factors, the most important factor will be the budget.

With this program, before building any new vessel, the naval architects and marine engineers can analyse the fuel consumption of the selected diesel generators and determine its cruising speed in order to attain the best fuel consumption efficiency. At the same time, the software is able to compare fuel consumption, range and endurance of two diesel generators. When the vessel is in operation, ship master and crew can monitor the specific fuel consumption, range and endurance based on the selected diesel generators. This can be iterated during the sea trials.

6.2 Accuracy of Regression Based Methods

The prediction accuracy of equation (2.91) has been compared with resistance calculation methods by Compton (1986), Holtrop (1984), Mercier and Savitsky (Savitsky & Brown, 1976), Ortmerssen (1971), Radojcic (1984), Savitsky (1964) and Tang (Ping-zhong et al., 1980) in Figure 6-1. Each method has been used within its own limits of applicability. Figure 6-1 shows the mean value of the ratio of the prediction to the experiment and the standard deviation at design speed, at hump speed, before the planing regime and at planing speed. The number of models used in the correlation is also given. The models include the NOVA models and also other models, the resistance data of which has been used in developing the equation (2.91). Thus the comparisons of prediction accuracies with the other methods are not totally fair.



Figure 6-1: The percentage difference between resistance prediction by equation (2.91) and model test for three hard chine vessels

As expected, the new methods give more reliable results than the older methods. The Mercier-Savitsky method gives the most reliable results for round bilge vessels in the speed range Fn =1.0 to 2.0 where the method is applicable. The maximum difference between prediction and experiment is from 6 to 7%. The Savitsky method gives the best resistance predictions for hard chine vessels at the planing regime. At lower speeds the predictions by Mercier-Savitsky method are quite reliable.

6.3 Software

A software package was developed which consist of resistance prediction, Holtrop and Lahtiharju and power required for diesel generators and electrical motors.

This software uses mathematical models created in Chapter 2.0 and 3.0 for the development of the package. With the use of modelling software package, the algorithms were developed from the structures in LabVIEW. Formula Node, While Loop and Case Structures were used in the progress. In LabVIEW simulation program developed, users can change inputs and save resulted data to Microsoft Excel for further data analysis. Graph indicators were used in plotting the necessary data required in this software. The development of the software is highlighted in Chapter 4.0.

The program is specially designed for diesel-electric propulsion vessels. The program will be able to predict the resistance of the vessel and the power required for the diesel generators and electrical motors.

6.4 Limitations

However, when predicting the resistance through regression based methods, only Holtrop and Lahtiharju had been looked into. The limitations of the resistance prediction for Holtrop and Lahtiharju are highlighted in Section 2.4.12 and 2.5.2.

The implementation of propulsor is also inappropriate for the program as there are too many variables and equations which have not been looked into. Therefore in this project, the propeller efficiency is assumed to be 55%.



6.5 Verifications

In order to validate the software developed, verification works are shown in Chapter 5.0.

The main reason for the verification is to determine the accuracy of the software developed. The Holtrop resistance prediction algorithm and DEP models were simulated and compared with ship specifications from R/V G.O. Sars and SV290. The resistance generated from the software was compared with results from Hullspeed.

The simulated result of the required power for the diesel generators has shown a pretty close result. Although the simulated result of the required power for the electrical motors is differs from the actual power required this is probably due to insufficient information of electrical efficiency in the switchboard, frequency converters, transformers and electrical motors. Since the predicted power required for the electrical motors is much higher than the actual required power, this shows that the assumed efficiencies of the switchboard, frequency converters, transformers and electrical motors are too high. Therefore, electrical efficiencies in the components of DEP determine an important role in predicting the output power. However, the number of propellers is equally important; a twin-screw propeller shaft will have much lower propeller efficiency as compare to a single screw propeller shaft.

Overall, the software shows that the resistance prediction algorithm gives a good estimate of resistance and DEP provides a good estimate of required power for the diesel generators and electrical motor. The analysis of fuel consumption, range and endurance are provides a good estimation when in operation.



7.0 CONCLUSION

The software developed has successfully reached the aim of the project. The ship operating profile or ship coefficients are the inputs for the use of regression based methods and the coefficients will determine the final predicted resistance. Electrical efficiencies are equally as important as the ship operating profile as it plays an important role in predicting of power for the diesel generators and electrical motors. Typical efficiencies may be used for the design stage and when in operation, the known electrical efficiencies of the DEP systems should be used in order to obtain a more accurate ship fuel consumption, endurance and range. The numbers of propellers are as important as the electrical efficiencies because a twin-screw propeller shaft will result in much lower propeller efficiency.

The key features of the software are: resistance prediction through Holtrop and Lahtiharju methods based on the hull forms; prediction of required power for diesel generators and electrical motors in diesel-electric propulsion; fuel consumption, range and endurance analysis based on the selected diesel generators. The benefits for the users especially the naval architects and marine engineers have been shown and this software can be used widely by them during the design stages. Instead of going through multiple model-testing to improve on the design, the users can just modify the hull form in the software to analysis the results. Based on the available components in the diesel-electric propulsion, the user can select the best components from the database. If there are no suitable diesel-electric propulsion components, the users most probably need to modify the hull form to obtain the best sized diesel generators and electrical motors.

The advantages of the software are: user friendly as compared to commercial software for resistance prediction; can be further developed for future analysis works; ability to extract key data into Excel for further analysis and can be used as a tool for design stage without going through model testing for a few times. The only disadvantage is that the propeller efficiency is assumed for this project and it might affect the final result slightly and it will be further highlighted in the conclusion.

Holtrop method estimates the resistance of displacement ships. It is a statistical regression of model tests and results from ship trials and may be used to access qualitatively for the resistance of a ship design. The improved formulation from Holtrop, 1984 has been published in Holtrop, 1988. The new formulation has form factor depending on ship speed, revised formulas for the wave resistance and separate relations for the air resistance. Other improvements include added resistance due to incoming waves, added resistance from head wind and shallow water corrections. Therefore, Holtrop, 1988 provide reasonable degree of accuracy not only for the initial stages of ship design but also for more rigorous analysis in the later stages.

Regression equations of Lahtiharju method for resistance prediction have been developed on the basis of extensive systematic resistance tests. The equation for hard chine vessels can be used before the planing regime. The new equations, together with the Mercier-Savitsky method and the Savitsky method, seem to give a reliable basis for the resistance prediction of high-speed hull forms at the design phase over a very wide speed range. However, more correlations with resistance data of



models not used in developing the new equations are required before the new equations are fully proven.

Overall, the accuracy of Holtrop and Lahtiharju resistance prediction algorithms have shown similar results as compared with Hullspeed as shown in Figure 5-4. Therefore, the software has successfully achieved in resistance prediction based on ship operating profile or ship coefficients. The software should not be seen as a replacement for model testing, but rather as a tool to reduce design iterations and ensure that the hull form when undergoing model testing is as close to the final hull as possible. Typically the software could be used to qualitatively compare with the merits of a series of hull forms. Lastly, the validation of the required power for the diesel generators and electrical motors has been proven in Table 5-1.

In every regression based methods, there is a limit of applicability or limitations to the method. It is important for users to understand the background information of the regression based method and most importantly, the limits of applicability for the method. For the software developed, it is secured as before inputting in the ship operating profile, the software will run the limitations check for Holtrop or Lahtiharju. Both validated DEP vessels can only use Holtrop resistance prediction algorithm.



8.0 FUTURE WORK

As explained in Chapter 2.0, Holtrop and Lahtiharju resistance prediction algorithms are limited to full displacement hulls and planing boats respectively. It is highly recommended to include more resistance prediction algorithms. As mentioned in Section 6.2, the Savitsky method gives the best resistance predictions for hard chine vessels at the planing regime. At lower speeds the predictions by Mercier-Savitsky method are quite reliable. Therefore, it will be wise to include Savitsky and Mercier-Savitsky resistance prediction algorithm to enhance the applicability of the software.

For the generators selection part in Chapter 4.0, maintenance and initial cost may be added to build a cost function. In order to do this a more detailed study to investigate the maintenance and initial costs must be done. Also starting and stopping costs should be investigated and incorporated into the selection algorithm in order to optimize the total fuel consumption for a given period of time with a varying load demand.

In order to attain a better selection for diesel generators and electrical motors, the database needs to be updated on a regular basis. Without a strong database, it is unable to provide the users with sufficient information for comparison.

This project can also look into exhaust emissions analysis. Ship engine exhaust emissions are known to have adverse impacts in areas of high shipping activities in the world. SOx and NOx are the most common air pollutants that affect the environment. When the air atmosphere has a sufficient concentration of SOx and NOx, it affects the air quality and produces acid rain. With the analysis of exhaust emissions, the software will be able to perform calculations on amount of exhaust emissions.

In the future, the software can be incorporated into propeller selection and look into propeller series like Wageningen B-Series, Au-series, Gawn-series, KCA-series, Ma-series, Newton-Rader series, KCD-series and Meridian series. Table 8-1 and Table 8-2 summarize the fixed pitch, non-ducted propeller series referenced here to enable rapid selection of the appropriate series for a particular set of circumstances and the extent of the series in terms of a blade number versus blade area ratio matrix.



Table 8-1: Fixed pitch, non-ducted propeller series summary (Carlton, 2007)

Table 8-2: Extent of Wageningen B-screw series (Carlton, 2007)

There has also been a significant increase in the choice of marine propulsion system configurations available to the naval architects and marine engineers. Therefore, the expansion of the project should also look into waterjet propulsion and different propulsor options like controllable pitch propellers, ducted propellers and Voith Schneider propellers. Other than that, gas and steam turbine should not be neglected. Gas turbines are used widely by naval vessels and steam turbines are used by LNG carriers and nuclear submarines.

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