For many years, ’het Loodswezen’ has relied on its tenders to transfer pilots to and from cargo vessels, with the primary focus on ensuring safety of both crew and pilots. In response to evolving emissions regulations and industry developments, the organisation launched the ’Tender of the future’ project. This initiative also provided an opportunity to enhance safety and comfort by redesigning the hulls of their tenders to address slamming, a significant issue observed in the latest tenders, the M-class. This challenge shaped the objective of this Master’s Thesis: "Delivering a new hull design for the tenders of ’het Loodswezen’ which will reduce the vertical accelerations caused by waves compared to the currently used L- and M-class tenders."

The first sub-question towards achieving the main objective is: "Which width is the optimal trade-off between a safe and efficient mode of operation and a high L/B-ratio?" This question was explored from two perspectives. From a safety standpoint, it involved examining the expertise of ’het Loodswezen’, relevant regulations, literature and consultations with Damen to identify the narrowest width that still ensures safety. From a practical perspective, simulations were conducted using Fastship (a tool designed for evaluating vertical accelerations of vessels by analysing cross-sections as falling wedges in the early design stages). These simulations were used to assess the impact of varying widths, achieved by scaling the Stan Pilot 2205, on
vertical accelerations. The combined results of these analyses identified the optimal breadth for a tender of ’het Loodswezen’ as 6.46 metres, a design that reduces the vertical accelerations with 10% compared to current tenders while maintaining safe and efficient operations.

In the subsequent phase, the bow and overall hull design were iteratively refined from a standardised planing hull towards the optimal design concept for a pilot tender of ’het Loodswezen’. Many iterations were performed and all simulated in Fastship in order to analyse its peak vertical accelerations. These peak values were used to define comfort, as the crew will base their actions on the highest impacts rather than on average vertical accelerations. The main focus of the iterations was on:
• Maintaining the required initial stability.
• Increasing the deadrise angle to 25 degrees.
• Introducing a twist by increasing the deadrise angle toward the bow.
• Implementing a spray rail to enhance hydrodynamic performance by ensuring that the water is separated from the hull.
• Raising the deck at the bow to increase the reserve buoyancy to prevent bow diving.
• Widening the deck at the bow to ensure that the same operational procedures can be applied.
These iterations culminated in the final bow and hull design, optimised for the tenders of ’het Loodswezen’. This design reduces peak vertical accelerations in the bow while maintaining resistance and operational efficiency, achieving a balance between performance and comfort for safe and effective pilot transfers.

Reducing the bow flare and narrowing the bow have proven to positively affect vertical accelerations but introduce potential challenges maintaining efficient operation with the current working methods. To address this issue, the next sub-question was posed: "Can the deck be shaped in such a way that the drawbacks to operational efficiency are minimised while the flare in the bow is significantly reduced?" Through several iterations, a deck design was developed that enhances operational performance without substantially increasing vertical accelerations. This was achieved by incorporating overhangs in strategic locations, enabling the tenders to operate effectively at an angle against cargo vessels.

A final design was developed that reduces vertical accelerations with 27.8% compared to a standardised planing hull while maintaining operational efficiency and considering resistance. However, the results should be interpreted with caution, as they are based on simulations using a basic version of Fastship with a limited number of input parameters. These simplifications enabled rapid evaluations but inherently affect the precision of the results.
It is important to note that this design represents a conceptual model. Further research and development are required to obtain reliable results for the actual design. For instance, the LCG plays a crucial role in performance but was, for simplicity, aligned with the LCB in this conceptual phase. In subsequent stages, the LCG should be carefully established to positively influence the results. Additionally, the hull fairing remains to be completed, a necessary step for generating a hull shape suitable for resistance calculations in simulations.
With these advancements, a comprehensive simulation can be conducted by Marin, for which budget has already been allocated. This simulation will provide a more accurate comparison of the new design with the existing L- and M-class tenders of ’het Loodswezen’, ensuring a robust evaluation of its performance.

This thesis investigates the yaw stability of wind-driven vessels using wingsails, addressing the urgent need for decarbonising global shipping. An analytical method is developed to assess directional stability based on aerodynamic and hydrodynamic characteristics, yielding a universal stability criterion validated with SEAMAN data. The study explores practical stability assessment approaches and evaluates the impact of varying hydrodynamic parameters, aerodynamic models, and surge coupling effects.

Results show that while stability diagnostics are invariant under different hydrodynamic models, significant variations occur in destabilised hulls. Including a boundary layer in the wind model has negligible effects on stability but increases computational effort. The closed-loop analysis assesses proportional and derivative feedback control strategies, demonstrating satisfactory stability for both human and autopilot control across all points of sail in the original hull configuration.

Time-domain responses to step rudder inputs indicate small steady errors and oscillatory components under human control, with autopilot control yielding even more robust outcomes. The study highlights the importance of surge coupling in stability analysis, often overlooked in previous models. The thesis concludes that wind-driven vessels can achieve directional stability under active control schemes, providing a foundation for future research on sustainable maritime transportation.

A significant push toward sustainability has emphasized the need to make all vessels as energy efficient as possible. Close attention must be paid to the performance evaluation of the vessel’s main energy consumer, particularly the engine. Moreover, prioritizing the efficient operation of the engine is essential for maintaining the vessel’s operational safety. Engine performance evaluation has been conducted using various methodologies, based on either field testing data or physical and data-driven models. This research focuses on developing a condition monitoring model that is effective even with limited data availability, highlighting the correlation between subsystem degradation and its impact on the overall underperformance of the engine. The literature review examines potential methods for condition monitoring of marine diesel engines, with a focus on their modeling purposes. These methods are classified based on their theoretical foundation: White-box models, which are based on first principles, and Black-box models, which rely on the interrelationships between input and output parameters using available mathematical tools. Additionally, the literature discusses Grey-box models, which combine physics-based principles with data-driven approaches. The modeling purpose of the methods analyzed is identified as either parameter estimation or fault detection and isolation, depending on the available data and the chosen methodologies. To achieve the objective of this thesis, a structured model development process is formulated. This process begins with defining the available parameters and determining which are inputs and which are outputs. The dataset then undergoes pre-processing steps before being divided into training, validation, and testing datasets. The modeling procedure begins by developing a basic model using shop test data (manufacturer data) to benchmark target parameters and determine whether a data-driven or hybrid model is necessary for engine condition monitoring. A neural network is then employed to capture data variance and leverage its ability to identify non-linear relationships. Building on the baseline neural network model, the loss function is customized to incorporate physics-based information into the data-driven model. This integration is achieved using two methods: either by approximating shop test data to represent engine behavior or by incorporating physical equations derived from mean value engine models. These physicsbased elements are introduced as new loss terms within the loss function evaluation. Each of the five developed model variations is then validated for their predictive capacity, with evaluation metrics compared to identify the most promising approach. Additionally, the models are assessed for their ability to generalize to unseen data. After concluding the modeling process, a case study was developed to examine the practical application of the best model in creating a condition monitoring tool. This tool relies on monitoring the residual trends of target parameters and correlating these trends with potential fault types in engine subsystems. By analyzing these residual trends, the model can effectively link subsystem degradation to overall engine underperformance, providing valuable insights for fault detection. Transitioning to the conclusions, it becomes clear that incorporating physics-based information, particularly from mean value models, yields superior results in capturing the variance of actual measurements and accurately predicting different operational conditions. This highlights the critical importance of developing hybrid approaches that combine the strengths of data-driven models while addressing their inconsistencies, thereby producing results closer to the ground truth. Further recommendations include integrating recurrent neural networks and establishing a decision support system to help identify the root causes of degradation and suggest necessary actions. Overall, developing a condition monitoring model proves beneficial for identifying underperformance and ensuring the correct operation of the vessel.