The objective of this thesis is to explore the possibilities of utilising machine learning to predict the axial velocities in a nominal wake field of a single-propeller vessel, given a database with only basic parameters describing hull geometries, such as length, beam, and block coefficient. The nominal wake field is of primary importance for the propeller design process, as it represents the influence of the hull on the flow field that a propeller encounters. Furthermore, propeller-hull interactions can be derived from it.

Five different machine learning models have been developed, trained and evaluated. Visual inspection of predictions made by all five models revealed that none of the models has successfully captured the underlying physical phenomena that drive the wake field: all predictions show highly generalised wake fields. This finding was supported by a feature
importance study, which showed that the features that supposedly contribute the most to understanding the hydrodynamic phenomena that drive the wake field do not have the highest relative importance.

The limited performance of the developed models is primarily attributed to two factors: the limited dataset size and a lack of feature informativeness, meaning that the used features contain insufficient information about the problem to let a model effectively capture the underlying physical phenomena. To a lesser extent, computational limitations also play a role.

The ongoing pursuit of efficiency in the shipping industry is driven by both economic and environmental objectives. While historically focused on reducing fuel consumption to enhance market competitiveness, recent regulations such as the International Maritime Organisation’s Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Index (EEXI) have emphasized the need to minimize harmful emissions. To achieve these goals, Simulation-Based Design (SBD) and Computational Fluid Dynamics (CFD) have become essential tools for optimizing ship design. This research aims to develop and implement an optimization strategy to minimize the propeller power required for a vessel to maintain a constant speed by refining the aftbody shape while accounting for propeller-hull interaction effects.

The study addresses a critical gap in current methodologies, where many existing optimization strategies neglect propeller effects and studies including propeller effects use too computationally intensive methods to be used in an optimisation strategy. The research explores strategies to reduce computational load, ensuring a balance between accuracy and efficiency. Key questions include determining the optimal geometrical parameters for aftbody design, refining CFD procedures to be computationally light yet accurate, incorporating propeller effects efficiently, and identifying the most effective optimization algorithm.

The research findings reveal that by varying specific geometrical parameters, such as the aft arc angle and transom angle, an approximately 10\% reduction in required propeller power can be achieved. The study also evaluates different strategies for reducing computational load, including a grid refinement study and the exclusion of free surface effects, while noting the trade-offs in accuracy. The use of the virtual disk method to model propeller effects is identified as the most practical approach given computational constraints. Among the optimization algorithms tested, the NSGA-III algorithm is found to be the most effective, offering significant improvements with fewer computational resources compared to alternatives.

Overall, the research demonstrates that the proposed optimization setup can lead to a significant reduction in propeller power, contributing to the development of more efficient ship designs. However, further research is needed to refine the exclusion and estimation of free surface effects to ensure broader applicability across different vessel designs.

The TANGO mission represents a significant leap forward in the global monitoring of greenhouse gas emission plumes, particularly CO2 and NO2. Comprising two satellites, TANGO-Carbon and TANGO-Nitro, the mission is designed to operate from 2027 to 2031, offering unprecedented capabilities through stereo (temporally separated) and high spatial res- olution emission plume images. These capabilities create a unique framework that facilitates the development of novel estimation methods for emission rates of greenhouse gas emitters. Conventional methods for estimating emission rates of CO2 and NO2 rely on combining gas concentration measurements with wind velocity fields derived from meteorological data, often introducing substantial uncertainties due to the inherent inaccuracies in wind velocity estimation. To address this challenge, this research explores alternative methods enabled by TANGO’s innovative framework, which allows for the direct measurement of emission plume velocity from concentration data. By eliminating the dependency on uncertain me- teorological inputs, these methods promise to reduce the uncertainty in emission rate estimates.
Through detailed simulations and analyses, this research demonstrates that the TANGO mission can effectively establish a framework for directly measuring emission plume velocities. By simulating the data products of the TANGO satellites using Large-Eddy Simulations and applying advanced methods such as traditional Correlation Image Velocimetry (CIV) and Computer Vision Correlation Image Velocimetry (CVision-CIV), wind velocity fields were successfully estimated from plume imagery. These estimates were found to be of promising precision across a range of conditions, including varying wind velocities, emission rates of greenhouse gasses, and levels of measurement noise in the simulations. Results revealed that the CVision-CIV method outperforms the traditional CIV method, especially in scenarios with low signal-to-noise ratios. Wind velocity fields directly estimated from plume imagery were implemented in the Cross-sectional Flux Method to estimate CO2 emission rates. The emission rate estimates indicate that direct plume velocity measure- ments provide a more accurate estimate of emission source rates than conventional methods, which rely on indirect wind velocity estimates from meteorological data. The use of wind velocity fields obtained through the CVision-CIV method resulted in CO2 emission rate estimates with ±20% accuracy in most scenarios, particularly under optimal SNR conditions. Additionally, the study highlights the impact of mission parameters such as image resolu- tion and measurement noise on the accuracy of wind velocity estimations. It was found that higher image resolution and longer time intervals between measurements enhance the precision of wind velocity field estimates by reducing the relative effects of measurement noise.
In summary, this research demonstrates that direct estimation of wind velocities from emission plume imagery, as enabled by the TANGO mission’s advanced capabilities, can accurately be performed and significantly enhance the accuracy of emission rate estimates. The improved wind velocity estimation methods proposed in this thesis offer a promising advancement in remote sensing techniques for greenhouse gas monitoring.

The increased focus on the reduction of emissions in the maritime sector has been the driving force behind many technological advancements related to marine engineering. Among those is the invention of the gate rudder system, which, while still at an early stage of development, promises improvements in propulsion efficiency and ship manoeuvrability. This thesis investigates the concept from the perspective of time domain simulation in order to pave the way towards identification of the working principle of the gate rudder power savings.

First, an extensive literature review is presented. A number of subjects related to the project was studied, starting from the principles of ship manoeuvrability, which comprises of ship hull hydrodynamics, hydrodynamics of control surfaces and regulations on manoeuvrability of ships. Afterwards, the current state of research into the gate rudder concept is investigated. Then, techniques of modelling of ship motion are laid out, with a focus on mathematical modelling of ship motion and rudders. Machine learning is identified as a potentially useful tool in the research, therefore a dedicated section consists of a brief introduction of the concept, some example regression algorithms and practical techniques utilized in conjunction with their use. Finally, the concept of time domain simulation in the context of maritime engineering is investigated and the options of simulator software available for the project are reviewed.

Initial decisions made for the execution phase of the project are described, including the choice of Manwav as the preferred time domain simulation software and S175 container ship as the benchmark vessel to carry out model tests on. An effort to tune the ship's model in Manwav to better match its performance found in literature is also described at the end of the chapter.

A physics-based gate rudder model is adapted into Manwav from a scientific paper. It is based on a mathematical model of a rudder derived from the MMG methodology, and the goal of its implementation was to pave the way in terms of code structure in Manwav and to serve as a reference point to the focus of the project, which is the data-driven model.

The data-driven gate rudder model uses a machine learning regression algorithm to build a set of rudder force coefficient predictors using a CFD dataset containing the values of force coefficients within a range of rudder and drift angles of a vessel equipped with a gate rudder system. A well-behaving model is successfully built using support vector regression and deployed in Manwav.

Both gate rudder models are put to test in the case study, where three ships with different rudder models are seen performing a set of benchmark manoeuvres, comprising of turning circles and zig-zag manoeuvres at different vessel speeds and rudder deflection settings. While the physics based model was found to be generating abnormal vessel behaviour, the data-driven model performed as expected, providing some insight into a potential impact a gate rudder system might have on a real ship's manoeuvrability and power consumption.

The thesis is concluded with a discussion on the data driven model's applications, along with limitations that prevent it from reaching its true potential. What follows is an extensive list of recommendations that go into detail on how the project could be improved in the future. The overall conclusion from the project is that a relatively limited amount of CFD data was successfully used to produce a rudder model that provides simulation possibilities otherwise beyond the reach of high fidelity methods due to scenario complexity and computational cost associated with them. With not a lot of additional effort, the model will make it possible to test whether or not gate rudder power savings reported in literature come from sailing in realistic weather conditions.

The development of biofouling on ship hulls is known to be deeply damaging for the overall performance of the vessel. The real effects of soft fouling, especially due to biofilms and diatoms, are however not yet understood and precisely defined. The current work numerically assesses the influence of an increase in surface roughness due to soft biofouling over the hydrodynamic performance of a containership. The simulations are carried out through MARIN’s in-house software ReFRESCO. Unsteady free-surface computations are used to evaluate both the resulting frictional drag, and the wave profile modification for different input surface roughness values. The results show that frictional resistance at 14 knots is increased of up to 45% for a limit equivalent sand-grain roughness height ks = 300 μm, correspondent to biofilms. Residuary resistance at the same speed suffers decreases of maximum 2.5% in the same range. These consist in hull pressure variations in the aft and transom areas of the vessel, and in small height differences in the wave pattern. A dataset obtained from monitoring of the containership for five years is used to study the added frictional resistance evolution due to roughness. Holtrop & Mennen
procedure integrated with environmental variables show a maximum increase of ΔCF = 1.4 · 10−3. The methodology proves useful to identify potential drydocking periods, and to provide an overview
of the added resistance trend. It should however be implemented with Machine Learning models to improve accuracy, in case the precise influence needs to be assessed. A novel comprehensive soft
fouling growth model is created to assess the surface roughness ks and ΔCF time evolution. This constitutes one of the few attempts in the literature to create a model that simultaneously considers the
effect of multiple environmental and operational variables based on the GPS position. Light intensity, sea surface temperature, draft and concentration of nutrients. Increases of up to 100 kN in resistance
and of ΔCF = 0.001 are observed if only diatoms are considered are integrated. Temperature and nutrients result to be more relevant for growth, in comparison to light intensity and draft. A sensitivity
study is also carried out to prove the small dependence on the choice of the analytical approach. A comparison of the growth model + CFD results with data processing show that the analytical procedure
provides a good indication on the growth trend, while added resistance intensity is underestimated in the order of ΔCF = 4.65 · 10−4.

The offshore wind industry is increasingly constructing wind turbines farther from the coast, in deeper water, and under more extreme conditions. This requires larger (monopile) foundations and necessitates new installation methods. An important factor affecting workability is the dynamic behavior of the monopile during installation.
The objective of this thesis is to develop a method for determining the hydrodynamic loads caused by internal sloshing in an open-ended monopile (MP) as it transitions from a horizontal to a vertical position in the splash zone.
First, the resonance frequencies of the internal water column are predicted with analytical approximations based on linear theory. Distinction is made between piston mode and sloshing. Two numerical methods, linear potential flow (LPF), and computational fluid dynamics (CFD) are used to verify the resonance frequencies. Since the CFD analysis is done in 2D, a 2D representation of the open-ended monopile is considered. Due to the presence of viscous effects in CFD, the resonance observed with CFD consistently occurs at a lower frequency than for the analytical and LPF methods. Also it is found that an decrease in inclination angle of the monopile with respect to the horizontal, while maintaining the same submerged length, results in lower resonance frequency for both piston mode and sloshing in both LPF and CFD.
To assess the accuracy of LPF in describing the motion of the internal water column, it is compared to the CFD model. Input excitation in the CFD model is low enough to avoid non-linear sloshing modes and other non-linear behaviour of the free-surface. The ComFLOW 2D CFD model has been validated against various works from the literature for the accurate representation of gap resonance frequencies.
For both the piston mode and sloshing resonance, discrepancies between the two numerical methods are found, which can be attributed to viscous effects. At resonance viscous effects are nonnegligible, therefore the LPF method over-predicts the severity of the piston mode and sloshing. The influence of both the submergence of the monopile and its inclination angle with respect to the horizontal is considered.
The hydrodynamic coefficients for added mass and damping are found with forced oscillation for both upright and inclined geometries. While good agreement is found between the LPF and CFD results away from resonance, the CFD results in the vicinity of the resonance frequency are used to tune the LPF model, by way of additional linear damping, to achieve more accurate results.
It can be concluded from the present work that the resonance of the internal water column near the first sloshing mode significantly affects the overall hydrodynamic force and must be taken into account. At the peak hydrodynamic force observed during the first sloshing mode, the sloshing induces forces 5.59 times higher (submergence of 5 meters), 3.62 times higher (submergence of 10 meters), and 2.33 times higher (submergence of 15 meters) compared to cases where sloshing is not considered.
Looking forward, it is strongly advised to conduct forced oscillation tests with larger amplitudes, as this explores the effect of non-linear chaotic sloshing. Additionally, expanding the CFD analyses to 3D, where more non-linear sloshing effects are expected, such as swirling, is recommended. Furthermore, given the differences in results between LPF and CFD, it is valuable to validate the findings through model experiments.

The increased consciousness of the negative impact of human-induced climate change has resulted in rapid developments within the offshore wind industry. With Wind Turbine Generators (WTGs) becoming increasingly larger and available shallow water sites becoming scarcer, new offshore wind solutions must be developed to accomplish the goals set by today’s policymakers. The new deep water sites available for offshore wind are not applicable for the current benchmark of monopiles and jack-up vessels. New state-of-the-art applications, such as Floating Offshore Wind (FOW), come into play in the global offshore wind market. To meet this demand, Heerema is developing installation methodologies for floating to floating installation applications. One of these methodologies is the RNA+ method (Rotor Nacelle Assembly + Tower), lifting a fully assembled WTG module in one lift from the vessel deck on top of its foundation to limit the number of critical lifts. Operability studies are required to examine whether such operation is feasible. As the FOW industry is the new kid on the block, little is known about FOW’s configuration and other influences. Therefore, this research aims to determine the effect of design parameters of the three key components of an offshore floating to floating installation on its operability. These key components are the WTG, floater, and installation vessel. For this research, the IEA 15 MW reference turbine with an altered tower, a Tension Leg Platform (TLP) provided by Intecsea, and Heerema’s Semi-Submersible Crane Vessel (SSCV) Sleipnir were used as WTG, floater, and installation vessel respectively.

A numerical base case model containing these key parameters was built to address the influence of the design parameters. This base case models the free-hanging stage of a complete WTG module suspended in the SSCV’s crane 3 m above the TLP in a water depth of 150 m. The numerical model was analyzed in the Frequency Domain (FD), considering only first-order effects. With respect to this base case, all parameter variations were compared. A mean JONSWAP spectrum was used as wave spectrum.

The results show that this installation method is sensitive to long wave periods ( > 8 s). The clearance between
the nacelle and crane-boom is deemed the most governing limiting criterion. The relative vertical Z-tip motion between the tower bottom and TLP top and the side-lead angle of the crane hoist wire are the secondary governing limits. Design parameters that influence the static clearance between the nacelle and crane-boom have the most impact on the total operability. With the current design parameters world’s largest SSCV has a limited operability for installing the modified version of the IEA 15 MW reference turbine with a single crane lift. Alterations to increase its crane boom reach and clearance are needed to perform this single lift installation. The hub height and nacelle casing size of the WTG limit the operability significantly. Furthermore, due to its relatively small size, stability and stiffness in heave direction, the TLP hardly affected the operability.

The offshore wind market is growing rapidly, and new offshore wind projects are being launched more than ever. To keep up with demand, parts for these wind turbines are being produced all over the world. To get all the parts needed to their destination on time, they must be transported on heavy transport vessels (HTVs).

Today, business cases focused on maximizing profits dictate the design of these vessels. Despite the upcoming energy transition, the environmental impact of these vessels is usually neglected. Therefore, there is a need for a method to evaluate the economic and environmental performance of HTVs.

One basis for such a method is Blended Design. This method is able to cope with market uncertainties in the early stages of ship design by expanding knowledge when design freedom is still high by combining market forecasts for offshore wind farms with multiple vessel designs. An uncertainty model then evaluates the financial performance of the vessels in the market, allowing designers to explore the performance on basic main dimensions of an offshore wind installation vessel. The main limitations of this method are that it is not able to optimize on environmental performance and evaluate the financial impact of alternative fuels.

Due to growing concerns about climate change, Ulstein has seen an increase in requests for the use of alternative fuels in ship designs. For this research, methanol, ammonia and liquid hydrogen are being investigated as these alternative fuels are considered future-proof.

The use of alternative fuels in ship design involves some adjustments to the
Blended Design method. Due to the different gravimetric and volumetric densities of these alternative fuels, this has implications for the endurance of the ship design and the amount of cargo the ship can carry. Changing system and installation requirements and varying fuel costs also drastically alter the financial performance of alternative fuels on ship designs.

The proposed methodology accounts for these changes and quantifies alternative fuel emissions by converting them to CO2-equivalent emissions. In this way, other greenhouse gases such as CH4 and N2O are also included in this metric. The environmental performance is then expressed in the EEXI-equivalent: the amount of CO2-e emitted by the ship design per tonmile. The EEXI-e index makes it possible to compare the various alternative fuels in terms of their environmental performance on the same basis.

In this work the EEXI-e method is combined with the original Blended Design method and the method is adapted to include alternative fuels. This makes it possible to evaluate the environmental performance and associated financial impacts of different alternative fuels.

Because financial and environmental performance are now linked, it is possible to examine the impact of different ranges of carbon taxes. The case study results presented in this thesis show that the carbon tax has a large impact on the financial performance of the ship when it is applied. When enforced, the choice of an alternative fuel system becomes more attractive.

The proposed method has provided a guide to ship designers to make better decisions about the main dimensions of a ship at an early stage of ship design. In addition, the method can provide much-needed insight in the selection of alternative fuels by evaluating the financial and environmental performance of alternative fuel systems.