**7. Application Case: Results**

Four different solutions were analyzed in this paper: the traditional diesel configuration (S0), the PTO solution (S1), the PTO of higher power size, and the fully electric one (S3). Table 9 provides the results of the total costs obtained with a complete LCPA analysis: we mainly focused on BLD, OPEX, and M&R values to compare the solutions. EEDI, Sox, and NOx are approximated values, but they can give an idea of the solution quality. Speaking in terms of BLD, the reference ship layout (S0) is the less expensive, while the fully electric configuration, as expected, is the most expensive; solution S1 and S2 are very similar, S2 is penalized by the third gen-sets installed for a redundancy aspect but not needed in terms of power generation (two gen-sets covered all scenarios). In terms of OPEX, the best solution is S2, while the reference configuration (S0) is the worse. It is important to remind that OPEX value includes fuel consumption, so, this value could also be read as, S0 has a higher fuel consumption than S2 and S3. For each configuration, fuel consumption has been calculated, combining the utilization factors and the complete engines diagrams. Referring to data reported in Tables 5–8, the results obtained are truthful.

The M&R are lower in S2 and higher in S1: as expected, S2 is the configuration that has been designed to ensure engines' better working point and this is underlined in OPEX and M&R values that are the best. S1 that has a worse working point, as shown in Table 8, also has higher M&R costs: a bad engine utilization reduces the MTBM and increases the maintenance times over ship life cycle.


**Table 9.** Results of the layout comparison.

The EEDI value points out that all alternative configurations have better efficiency and lower emissions than the reference one. S1 is the solution with the lower EEDI value, but we should underline that value in S2 is affected by the third gen-set installed as a stand-by unit. The EEDI value is a function of installed power.

It is important to note that despite S1 has worse engines working point, and therefore higher M&R is the best solution. Speaking in terms of NPV, the total value not differs so much from the other ones.

In Figure 10, it has been reported the LCC values maintenance over time for the four layouts S0, S1, S2, S3. As just said, solution S2 is the best in terms of operational maintenance; indeed, it has been designed to minimize maintenance costs. An unexpected result is given by solution S0: it is a cheaper solution than S1 even though the main engines are used less efficiently than in solution S1 (characterized by the introduction of the PTO).

From these results, it is also possible to see that the fully electric, besides being the most expensive solution, will not guarantee a significant advantage during the years. From this analysis, we can also say that, at least for our application case, it is not true that the most expensive solution is always the best in terms of maintenance costs. Analyzing the last years of the ship life in the graph (25–30 years), it seems that solution S2 would bring a good saving compared to the original solution.

**Figure 10.** Integration over 30 years of Operational Maintenance costs.

Table 10 provides the results in terms of dimensionless index ready for the LCPA calculations: the coefficient is always defined between zero (worst) and one (best). S1 is the best, and S2 is the second-best solution, even if it is affected by the power surplus installed. Not purchasing the third gen-set, decision subjected to owner agreement, implies less BLD and M&R. The S3 is the worst layout, but it is important to stress that LCPA tool does not take into account noise and vibration problems, new possible weights distribution onboard, engine room locations, and off-limits routes.

The last column in Table 10 is part of the LCPA tool structure: for each KPI, a weight has been assigned. The assignation is arbitrary and given by the designer or the owner or the shipbuilder. In this case, we selected BLD and M&R as most interesting KPIs, followed by OPEX. In this perspective, despite NPV value is one of the most popular economic indicators, it has not been deeply discussed and analyzed during this work focused on maintenance actions and costs: the weight assigned in Table 10 is equal to 0 for this reason. The NPV value does not present significant changes in S0, S1, and S2 because the technology used in the propulsion system is the same (i.e., diesel engines), while the S3 configuration employs large electric motors as main propulsion item.


**Table 10.** Results of the layout comparison.

The same results are shown in Figure 11 below.

**Figure 11.** KPIs coefficients in spider graph.

#### **8. Conclusions**

For a research vessel, all the necessary data have been provided to start developing and assess a maintenance prediction model based on a real maintenance plan and not on a parametric formulation. The methodology described in Section 4 is a result of multiple interactions: it has been developed for the reference vessel only and then readapted to a general application for various vessel types. Figure 3 provides a simplified scheme of the maintenance tool and calculation structure.

During the tool development, a corrective Formula (4) for diesel engines MTBM has been defined to take into account design mistakeS on the maintenance costs. Application cases have been carried out on a research vessel, as exposed in Sections 5 and 6. Different propulsion solutions have been identified, and the tool can characterize them in terms of different performances; but, at the same time, it not so simple to identify the best solution because of the final evaluation that is strictly related to the stakeholder point of view.

Further improvements could derive from the following activity:


**Author Contributions:** For conceptualization, methodology, validation, formal analysis and investigation P.G., G.F., M.M. and G.M. gave their contribution. For software and data curation, G.F., M.M. and G.M. gave their contribution. The writing—original draft preparation was made by G.M. and the writing—review and editing has

been carried out by P.G. and G.F. Resources have been provided by M.M. Supervision has been carried out by P.G., G.F. and M.M. Project administration and funding acquisition was in charge of P.G.

**Funding:** The reported research is still under development in the frame of the HOLISHIP (HOLIstic optimization of SHIP design and operation for life cycle) project (2016–2020), which has been funded by the European Commission in the HORIZON 2020 Transport Program, Grant Agreement nr 689074.

**Acknowledgments:** The Authors would like to express their gratitude to Alberto Dachà (Fincantieri s.p.a) for the valuable contribution he gave in terms of constructive discussion and advise during the development of the research activity.

**Conflicts of Interest:** The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
