Traceability in complex ship design involves keeping track of the evolving relations between need, requirements and design. Such traceability is not fully developed in the current design process at DMO due to divisions in the design process and between design tools. This thesis proposes to use a single information model to facilitate traceability by connecting the need, requirements and design in a single database. Besides enabling traceability, such an information model can also be used to support modern modelling design tools. In the early design stage of naval vessels – the preliminary design phase – the capability, technical feasibility and affordability of a new ship are defined. Interactions between these three aspects are opaque and rely on the need for a new naval vessel and supporting requirements and preliminary design(s). Determining the need, requirements and design is in this thesis considered to be a ‘wicked problem’. In such problems, information gained by developing solutions helps define the problem itself. However, these solutions can only be determined with a certain interpretation of the initial problem. This results in many (evolving) interactions between the need, requirements and design in the ship design process as one is needed to define the other. With the help of Systems Engineering and Requirements Engineering information and relations in a ship design process can be determined and structured. These engineering fields are widely applied, and current technology enabled the development of computer aided approaches incorporating design theory, including computer aided traceability. One of the current developments in design approaches is the introduction of model-based approaches, which creates the possibility to actively manage more relations and interactions in the design process. Model based approaches such as the Design Structure Matrix are used to visualise relations between information, and Axiomatic Design enables the decomposition and creation of complex relations. A different approach to modelling in design processes is the use of Knowledge Based Engineering. Knowledge Based Engineering field aims to capture not only the relations and interactions between information in design processes, but also ‘knowledge’ about the ‘how’ and ‘why’ in these processes. Each of these model-based developments relies on, or enables, traceability of information. However, having a program capable of supporting an information storage to support these approaches remains difficult to achieve. For this purpose, Shipbuilder software was created to support transparent storage and management of information. In this thesis, theories for modelling approaches, Systems and Requirements Engineering and the DMO design process have been combined. This resulted in one information structure, applied in the Shipbuilder application, which enables traceability. To show that the proposed information model can model a ship and supports traceability, a case study has been performed. A small model of both an S- and L-frigate has been created to show how traceability can be performed in this information model.

This thesis research focusses on winning at sea. "Winning at sea" requires presence of relevant capability at the right time and for the required time. A winning fleet is a fleet that has the capability to achieve this. The goal of this research is to define a winning fleet and to create a method that can provide insight in how such a fleet can be obtained and maintained. One of the main challenges when operating a naval fleet is to match the fleet capabilities with the capabilities required from the missions that occur over time. The first part of this problem lies in the fact that it is never known what the future will bring. Especially when looking at a period of 30 years which is the general lifetime of a naval vessel. In practice this is partly countered by introducing a major update at the halfway point in the ship's life cycle. The other part of the problem is that the fleet capabilities need to be distributed over the different vessels and vessel types. Because having an infinite fleet size would be far too expensive, certain compromises need to be made. The main of which is that having a finite fleet will result in lower costs but also means that when a vessel is occupied all capabilities of that specific vessel are unavailable for other activities. For this reason it is important that the fleet composition is constructed in such a way that at all times, or at least within an acceptable error, the requirements set by the missions can be met. The method that is created during this research acts like a proof of concept of whether such a method could be useful in the future. This research should be able to help with the iterative process of balancing the operational need and design requirements with the feasibility and affordability that occurs within the early design stages of naval fleet design. The main stakeholders in this process are Commando Zeestrijdkrachten (CZSK), the department of planning (DPLAN), and the Defence Materiel Organisation (DMO). These three parties can respectively be categorized as the user, military planner, and the supplier. In order to reach the research goal a model has been created that simulates naval fleet behaviour. This Fleet Behaviour Model (FBM) describes a fleet as a collection of capabilities which are distributed over different vessel types. In order for this fleet to be operational it needs to comply with certain Life Support Activities (LSA) such as maintenance and the training of the crews. The operational need of this fleet is simulated by generating mission scenarios which require certain capabilities from this fleet for a specific time and at a specific location. Next to requiring mission specific capabilities from the fleet the missions also have the possibility of enemy presence. When this happens naval combat is simulated which introduces attrition into the model. The inclusion of attrition into a fleet behaviour model is what makes this research unique. Using this model the performance of multiple fleets can be tested against one or more scenarios. After the Fleet Behaviour Model was finished and tested a Genetic Algorithm (GA) has been constructed in order to systematically find an optimal solution. In this case an "optimal solution" is the fleet of which the capabilities and the distribution of these capabilities best fit a given mission scenario. It also takes into account the difference in design priorities that can change over time. One or more of three performance parameters: mission successfulness, attrition, and fleet size can be prioritized. Depending on factors like risk and budget these priorities can change which in turn can have a drastic impact on the resulting fleet compositions. In order to test the models' capability to provide insight into what makes an effective fleet, three test cases have been constructed. These test cases test the model on how well it can generate performance optimized fleet compositions as well as testing the performance of one fleet against a range of different scenarios. After these simulations the resulting data has been analysed in an attempt to find clues on what makes one fleet perform better than another. This insight was than translated to the broader spectrum of general early stage naval fleet design. In conclusion, the data gathered from the test cases has proven that the model is capable of providing insight into the early stage design requirements of naval fleet design. As a proof of concept this Fleet Behaviour Model seems promising as a useful tool for naval fleet design in the near future.

Five naval support vessels are being replaced. For this replacement two focus points are important. The first one concerns creating more unity within the fleet, achieved by using the hull geometry as a monolithic part. The second one concerns the aim to reduce harmful emissions by reducing the effects of the energy transition by minimising the resistance of the vessels. The current design approach at DMO has deficiencies which limit the ability to vary hull forms and to analyse a large set of design options. Also propulsion power is predicted by regression lines only, which makes it difficult to evaluate novel hull forms. Motion predictions are only used in a later design phase. These deficiencies need to be resolved. The technological opportunities which will be used to address these deficiencies are incorporating CAESES and RAPID in the concept exploration phase. CAESES is a CAD method with integrated optimisation algorithms which makes it useful to make hull form variations by using parametric optimisation. RAPID is a potential flow solver which can be used to predict the wave resistance on the geometry. The total resistance will be predicted by using Holtrop and Mennen for the viscous resistance components. The seakeeping capabilities are predicted by a simplified version of the linear strip theory. The main objective can be formulated as: How can the unity and the energy transition be analysed in the concept exploration phase for the new generation naval support vessels with a hull geometry point of view using RAPID/Holtrop/seakeeping integrated with CAESES Four validation studies have been performed. The test case studies are analysed on the differences in effective power and vertical displacement. The first conclusion is that the effective power will increase for a family design strategy in comparison to a single ship design strategy. The second conclusion is that the vertical displacement can be decreased when using a family design in comparison to the single design strategy. But only when the Logistic Support vessel is left out of the family of vessels, because of its restricted length. The design speed can be used as a design variable, but only when a more sophisticated power usage objective is used. The new approach can be very useful in the concept exploration phase at DMO. The new approach uses a more advanced resistance objective in combination with hull variation. It can also predict the seakeeping capabilities in the concept exploration phase already. The integration of a potential flow solver and prediction on vertical displacement at the bow can be done within one optimisation and a study is possible within reasonable time (under six hours). This makes the new approach practical in the concept exploration phase.