Offshore wind farms are central to the global energy transition, especially as larger turbines and new substructures enable deployment at greater depths. While cost and energy yield are typically the main drivers of design, integrating environmental impacts into the design process is becoming increasingly important. This thesis develops a framework in which life cycle greenhouse gas (GHG) emissions are quantified and expressed through the global warming potential (GWP) in grams of CO₂-equivalent per kWh, thereby creating a metric that can be directly compared with the levelized cost of energy (LCOE). In doing so, GWP becomes a design-relevant parameter for optimizing offshore wind farms not only for economics, but also for sustainability.
The study investigates wind farms ranging from 400 MW to 1000 MW, with turbine ratings of 5 MW, 10 MW, 15 MW, and 22 MW. Three foundation types are considered: monopile (fixed-bottom), semi-submersible (floating), and spar buoy (floating). A bottom-up life cycle assessment (LCA) was applied, covering material production, manufacturing, transport, installation, operation, and decommissioning. The assessment incorporates a logistics model for marine operations and farm-level scaling to ensure realistic representation of installation demands and component quantities. Results were normalized by the annual energy production (AEP) of each configuration, enabling fair comparison across technologies and site conditions.
Findings show that increasing turbine size consistently reduces GWP, as fewer units are needed to achieve a given farm capacity, lowering both material requirements and installation activities. In a 400 MW farm, semi-submersible turbines achieve GWP reductions of 50.50%, 65.28%, and 73.96% when scaling from 5 MW to 10 MW, 15 MW, and 22 MW, respectively. Spar buoy turbines show similar reductions of 50.91%, 67.29%, and 75.93%. For monopile foundations, also considered at shallower depths, larger turbines likewise reduce environmental impacts per kWh. Expanding farm capacity from 400 MW to 1000 MW further enhances performance, as economies of scale amplify both environmental and economic benefits.
Economic analysis reveals parallel trends. LCOE decreases with turbine scaling for all foundation types, although monopiles remain most competitive in shallow waters, while spar buoys gain advantage in deeper sites. Semi-submersibles bridge the intermediate depths but also benefit significantly from turbine upscaling. The alignment between GWP and LCOE trends indicates that design decisions favoring cost reductions also contribute to environmental sustainability. However, the marginal reduction in GWP diminishes at very high turbine ratings, suggesting that beyond a threshold, other factors such as LCOE, reliability and system integration may dominate design trade-offs.
In conclusion, this thesis demonstrates that by quantifying environmental impacts into a comparable metric, sustainability can be incorporated into wind farm design. Across farm scales, turbine ratings, and foundation types, results consistently show that larger turbines on monopile, semi-submersible, and spar foundations deliver lower GWP and improved LCOE, confirming that technology scaling serves both economic and environmental objectives in offshore wind development.

Floating offshore wind turbines enable renewable energy expansion into deep-water regions with stronger, more consistent winds. However, they are subject to continuous platform motion from wind and waves, which complicates blade loading and wake aerodynamics. While surge and pitch effects have been extensively studied, the aerodynamic influence of roll motion remains underexplored. Roll motion uniquely induces a non-uniform tangential velocity field across the rotor, which would have implications on the blade loading, structure fatigue and the wake aerodynamics.

This thesis presents a baseline analysis of blade loading and near-wake aerodynamics for a FOWT under prescribed roll motions. A coupled high-fidelity GPU-based Large-Eddy Simulation code GRASP resolved turbulent wake dynamics, while OpenFAST calculated blade loads. Coupling was achieved via the Filtered Actuator Line Method and the AspFAST application programming interface. The IEA 15MW reference turbine was modeled under steady, uniform inflow, with variations in roll amplitude (5° and 10°), roll frequency (0.03Hz and 0.05Hz), and tip-speed ratio (TSR 7 and TSR 9) in a full factorial test set.

Results show that roll motion leads to asymmetric fluctuations across the rotor plane. Normal forces display strong vertical asymmetry, with greater variations in the upper side of the rotor due to its larger distance from the roll center. A kinematic analysis deriving the variation of tangential velocity both azimuthally and over time supported this finding. Tangential forces exhibit lateral variation, with a left-right asymmetry across the rotor plane, though the magnitude of variation is similar on both sides. Both roll amplitude and frequency increase the magnitude of loading fluctuations, while TSR influences their blade spanwise distribution.

Power Spectral Density analysis reveals that roll introduces spectral content at the roll frequency and at 1P sideband frequencies, the latter arising from modulation of the 1P frequency by roll. Higher roll amplitudes, frequencies, and TSRs amplify these load fluctuations. While the prominence of the 1P frequency is negligible at the blade, it is strong in the near wake along with the roll frequency, 3P frequency, and 3P sidebands.

The unsteady blade loads directly shape the near wake. The stable helical vortex system of a bottom-fixed turbine is replaced by an oscillatory corkscrew structure that breaks down earlier and more chaotically. Increased roll amplitude causes stronger lateral oscillations and fragmented vortices, while higher roll frequency produces shorter wavelength perturbations, accelerating vortex pairing. Higher TSR strengthens the initial vortices and accelerates breakdown. Standard deviation of velocity fields in the wake confirms larger fluctuations at the top of the rotor due to its greater distance from the roll axis.

This study establishes a direct link between roll-induced blade loads and near-wake dynamics, providing a foundation for improved FOWT design, control, and wake modeling. Future work should assess far-wake impacts, the persistence of lateral velocities induced by rotational motion, and inertial effects under highly unsteady roll conditions.

The growing global demand for green energy requires the development of innovative solutions. Floating offshore wind turbines (FOWTs) present significant potential by enabling the harvesting of wind in deep-sea waters. However, as this technology is still in its early stages, the impact of the added six degrees of freedom (6-DOF) motion on wake dynamics and power performance remains unclear. Understanding these effects is critical for the design of floating wind farms. In this work, large eddy simulations and an actuator line model are employed to investigate the wake behavior and power output of the IEA-15MW reference turbine mounted on several floating platform concepts. To explore wake interactions in wind farm scenarios, two tandem FOWTs are modelled. The simulations are carried out in AMR-Wind, coupled with OpenFAST for the platform and blades' motion.
Initial cases are conducted with a laminar inflow to isolate fundamental wake mechanisms. Then, a neutral atmospheric boundary layer (ABL) is recreated to perform investigations with realistic offshore conditions.
Platform motions are found to induce velocity fluctuations in the wake and promote vortex-pairing. High-frequency and large-amplitude motions particularly enhance wake recovery in the upstream wake, although this effect is less pronounced in the downstream wake. Among all 6-DOF, surge is identified as the main driver of wake dynamics.
FOWTs generate larger turbulence levels than their fixed counterparts during laminar inflow. Notably, when the downstream turbine moves in phase with the incoming wake, the resulting interaction leads to the superimposition of the wake structures, forming large, separated low-speed regions.
In contrast, under turbulent conditions, FOWTs generate lower turbulence in the near wake, but similar levels further downstream. In these cases, the differences in mean wake velocity between floating and fixed turbines decrease significantly with distance.
The tandem FOWTs are found to produce less total power when operating at rated speed during neutral ABL conditions. However, the downstream FOWTs exhibit power gains of up to 20\%, indicating that larger floating wind farms are likely to generate more electricity than traditional bottom-fixed offshore wind farms.

Despite a large number of numerical and experimental tests and, as of late, linear stability analyses, incomplete knowledge of the physics of manipulated wakes prevents the community from embedding dynamic induction control into analytical control-oriented models. This thesis addresses this gap. We first focused on the framework: we considered the need of modeling blade flexibility in the simulations, then evaluated the turbine model to be used [2]. The actuator line model has been the go-to approach for wind turbines and (small) wind farm simulations in academic contexts for more than a decade but no consensus has been reached over important issues, such as evaluating the free-stream velocity and choosing the width of the smearing function used to project volume forces into the computational domain. The thesis discusses how these issues are connected and proposes an alternative approach to velocity sampling [3].
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes.

In this thesis, the effect of a surging and pitching motion of a floating wind turbine on its wake as well as the effect of a surging, pitching and yawing motion on a stationary downstream turbine are analysed. Two experiments are conducted in order to address both points. The turbine models used are scale models of the DTU 10 MW turbine, and the upstream turbine is placed onto a kinematic robot which can move in all 6 degrees of freedom. The first objective is studied using Particle Tracking Velocimetry and the second objective using load cell measurements. It is found that pitch has a larger effect on the flow field in the wake than surge and that the effects of low frequency motion are more visible than those of high frequency motion. Moreover, it is found that the tracing particles are concentrated in specific areas, affecting the accuracy of the results. In future research, it should be examined why this is the case and how it can be improved. Additionally, low frequency pitch and surge had the greatest effect on the loads. A clear sinusoidal motion is visible and the mean thrust, torque, and power is increased. The effect of the high frequency motions is not visible in the results. In future studies, the wake behind a downstream turbine can also be examined using Particle Tracking Velocimetry. Moreover, the downstream turbine can also be moving rather than standing still and more movements of the turbine can be examined.

Experimental testing of floating offshore wind turbines (FOWT) is essential for understanding the various engineering challenges posed by their dynamic motion. For example, the pitch and surge motion of the FOWT results in unsteady aerodynamic effects caused by wake interaction. In this project, two FOWT models (DTU10MW with TripleSpar and IEA15MW with VolturnUS) were developed for testing with a hybrid hardware-in-loop (HIL) setup. This setup integrates physical wind loading in a wind tunnel with numerically simulated hydrodynamic loading.

The HIL setup comprises a scaled wind turbine model, a hexapod that can actuate the floating motion, an instrumentation system with sensors to measure forces and accelerations, and a numerical model that simulates FOWT dynamics in real-time based on measured data. This work explores the methodology of developing a numerical model that can simulate the dynamics of the scaled FOWT model in real-time with the applied wind loading and numerically simulated hydrodynamic loading. Furthermore, a methodology for correcting the measured forces to obtain external aerodynamic forces is introduced and validated.

The process of developing the numerical model is thoroughly detailed, including its tuning and validation. The challenges encountered during the validation process are discussed in depth, with an emphasis on their underlying causes. Finally, the HIL numerical model was used to test the scaled DTU10MW wind turbine with a simulated TripleSpar floater at the Open Jet Facility (OJF) wind tunnel at TU Delft. This study also presents intriguing results regarding the HIL model's performance and the impact of wind on FOWT dynamics observed during the test campaign.

Floating offshore wind farms are a promising technological development that can help provide large amounts of energy from sites previously not feasible for wind farm development. To suc- cessfully develop floating wind farms, knowledge about the involved aerodynamics are crucial. A first building block of this is the understanding of the wake of a single floating wind turbine un- dergoing floating motion in turbulent wind fields. Using an actuator line model within the Large Eddy Simulation framework YALES2 the wake of a floating wind turbine undergoing surge motion has been investigated at varying amplitudes and frequencies. Special attention was given to the inflow conditions, comparing laminar and low turbulent inflow conditions modeled using Mann turbulence boxes. Varying the surge amplitude at a surge frequency equivalent to St = 0.4, the investigations show that the influence of the motion on the wake is greatly reduced even when low turbulence levels are present. Already at a turbulence intensity of 2.5% the wake deficit and wake turbulence in- tensity were indistinguishable from a fixed turbine’s wake for lower surge amplitudes. Large surge amplitudes were required to significantly change these wake statistics, starting from an amplitude of A/D = 0.16 rotor diameters.Using proper orthogonal decomposition the dynamics in the wake have been analyzed. It was found that the effect of the rotor motion on the wake is lower when the inflow turbulence intensity is increased. Varying the motion frequency in the range of St ∈ [0.2, 0.8] at a amplitude of A/D = 0.04, it was shown that a surge motion at a frequency corresponding to St = 0.6 has the highest impact on the wake in terms of the reduction of modal time series correlations. However, even in that case the wake deficit and turbulence intensity in the wake are not changed significantly.

The field of floating offshore wind energy is rapidly advancing, offering a promising solution to
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.

However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.

Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.

This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.

A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms.

Accurate wind turbine modelling is essential for reliable aerodynamic performance predictions. The industry primarily uses the Blade Element Momentum (BEM) method with correction models, but BEM’s assumptions become less valid with larger rotors and in Floating Offshore Wind Turbines (FOWTs), where wave interactions and wake dynamics are more complex. The Lifting Line Free Vortex Wake (LLFVW) method offers higher modelling fidelity but is less computationally efficient.

This study compares a BEM and LLFVW model implemented in the software QBlade. The evaluated parameters include power, torque, thrust, root bending moment, tip deflection, and angle of attack using the floating 15 MW UMaine VolturnUS-S reference turbine under various wind and wave conditions taken from several Design Load Cases (DLCs). The aim is to identify any differences between the methods and the met-ocean conditions under which these are most pronounced.

The results show minimal differences in BEM and LLFVW outputs under varying wave conditions. However, wind conditions have a greater impact, particularly around rated speeds where discrepancies were observed, mainly due to different controller dynamics. Above-rated conditions showed similar power, torque, and thrust predictions, but notable differences in angle of attack. The maximum and standard deviation of the root bending moment and tip deflection were found to be consistently lower for LLFVW compared to BEM.

This thesis presents a comprehensive study on Floating Offshore Wind Turbine’s (FOWT’s) modeling and simulation, focusing on the IEA 15MW wind turbine coupled to the VolturnUS-S semi-submersible platform. The primary goal was to develop the building blocks of a high-fidelity Computational Fluid Dynamics (CFD) coupled model using OpenFOAM to simulate large-scale FOWT behavior, verify, and compare each component individually against the mid-fidelity tool OpenFAST. The project builds on the frameworks of previous studies by Pere Frontera Pericàs [81] and Scarlatti [98], expanding them to accommodate the complexities of a larger turbine and more intricate environmental conditions.

The model was implemented using OpenFOAM’s waves2Foam and Moody libraries for wave and mooring modeling, respectively, while aerodynamic simulations employed the turbinesFoam package with an actuator line approach. A spatial convergence study optimized mesh parameters for computational efficiency and accuracy, ensuring reliable and stable simulation results for motions up to 24 meters in surge. Comparisons between OpenFOAM and OpenFAST using a P-Q analysis highlighted significant differences, particularly in modeling damping. OpenFAST was shown to underestimate both linear and quadratic hydrodynamic damping due to its simplified representation of fluid dynamics. Discrepancies in the equilibrium positions were found between the decay tests using CFD; their origin still needs more investigation.

In aerodynamic simulations, steady-state and prescribed motion tests revealed critical differences in thrust and power predictions between OpenFOAM and OpenFAST. OpenFAST overpredicted the reduction in axial wind speed, leading to a reduced power output for steady turbine simulations. The CFD model provided a more accurate representation of rotor wake effects and dynamic responses, particularly in high-frequency surge conditions, where OpenFAST overestimated power fluctuations. These findings underscore the importance of using high-fidelity models like OpenFOAM to improve the accuracy of performance predictions in floating wind turbines.

The study concludes by offering recommendations for future research, including model validation using experimental data and the implementation of overset mesh techniques to improve simulation stability for large motions. The work establishes a reliable CFD framework for simulating this large FOWT, offering insights into enhancing mid-fidelity models and guiding future developments in floating wind turbine design and analysis. It also builds a fully coupled CFD model in OpenFOAM to investigate its behavior.

In recent years, the use of renewable energy sources has increased significantly. As a result, offshore wind energy has expanded due to its advantages compared to onshore, such as steadier winds, reduced visual impact and lower noise emissions. However, 80% of the global offshore wind resource potential is located in areas with water depths greater than 60 meters, making the installation of fixed-bottom wind turbines unfeasible and leading to an increase in floating wind turbines. Floating offshore wind turbines, due to their increased freedom of movement, experience more complex aerodynamic and hydrodynamic phenomena, resulting in different power generation compared to fixed-bottom wind turbines. The effect of six degrees of freedom on power production has not been extensively studied in full-scale operating wind turbines. Additionally, a better prediction of energy production could also reduce the investment risk of floating wind turbines, leading to a reduced levelised cost of energy and better integration of floating wind turbines into a fully renewable energy system.

The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.

For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.

Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.

In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy.

As wind turbines get bigger and bigger, the simulation of wind turbines becomes more complex. The increase in size brings about a multitude of intricate challenges that must be addressed in the simulation process. These challenges lie in the different aspects of the simulations such as aerodynamics, structural dynamics, power electronics, hydrodynamics, turbine control etc.

The central theme of this thesis is to explore the effect of one of the aspects- structural dynamics, on the wind turbine simulations. Moreover, the thesis also emphasises the development of a structural module and it’s integration in an OpenFOAM-based wind turbine simulation library called TurbinesFoam. TurbinesFoam is an actuator line method-based simulation tool, which enables the study of turbine performance as well as wake dynamics using Computational Fluid Dynamics. The key motivation is also to contribute towards the accurate simulation of wind turbines by performing a successful integration as said above.

To fulfil the goals, a structural module was developed in Matlab to simulate the
structural dynamics of wind turbines. The module is developed from the theory
of spinning elements assuming the blade and tower as Euler-Bernoulli beam. The developed module code is called while running the CFD simulations in OpenFOAM to add deflection and rotation of the blades and towers, using a MatLab pipe class.The accuracy of the developed code was measured using BModes and OpenFAST. The results exhibited satisfactory agreement between the outputs.

The NREL 5MW turbine was used to study the effects. The results revealed that,
at slightly above the rated condition, the turbine showed 1% decrease in power
production due to elasticity when compared with a rigid turbine. The thrust and
torque coefficients showed similar trends of reduction in value. Moreover, as the inflow wind velocity increased, the differences in performance got broader, due to increased deflection value. The wake region of the elastic turbine showed a mixed region of both increased and decreased wind velocity when compared with the rigid turbine.

As the urge to decarbonise the energy field becomes increasingly important, a growing interest is shown in wind turbine technologies. In particular, the past few years have seen the development of floating offshore wind turbines for applications in deep waters.

In addition to the harsher environment they are facing, these wind turbines are mounted on floaters and therefore experience motions in the six additional degrees of freedom. As a result, this greatly alters their aerodynamic behaviour. The flow surrounding the rotor gains in complexity, becoming highly unsteady and three-dimensional. Thus, its resolution by numerical means calls for high-fidelity methods such as Large-Eddy Simulations (LES).

The objective of this study is to numerically impose single and coupled motions in the pitch and surge directions on a scale rotor of the DTU 10MW. This scale model was used for an experimental campaign within TU Delft, and particular interest is given to the comparison of the loads obtained.
Additionally, numerical simulations permit access to further information, such as the radial distribution of the loads or the wake development.

For this purpose, the LES code YALES 2 is used, implemented with an actuator line approach capable of dealing with imposed motions. Both 1-DOF and 2-DOF harmonic motions are imposed in the pitch and surge directions. Different reduced velocities and frequencies are considered.

On single-imposed surging motion, the loads are found to be well in accordance with the quasisteady theory (QST), even at high frequency, where much larger fluctuations were encountered during the experiment. Particular phenomena are also captured in the wake, such as the formation of vortex rings when the frequency is sufficiently high. Similar comments are made for 1-DOF pitching. Finally, for combined pitch/surge motions, the loads are also in accordance with the QST predictions made from the 1-DOF results. The influence of each motion on the wake’s development is also discussed.

Keywords: Floating offshore wind turbine (FOWT), Large-eddy simulation (LES), Actuator line method (ALM), Coupled imposed motions, pitch, surge

In recent years, the interest in deep-water wind energy projects has drastically increased, driven by the considerable wind resource in deep-water locations. In such locations, floating wind turbines are more economically attractive than bottom-fixed wind turbines. Nevertheless, the floating wind industry faces numerous challenges. One major challenge is the high investment costs involved in the manufacturing of the substructure. Additionally, it is important to maintain the wind turbines’ power output within the required standards for grid connection. Therefore, the design and optimization of substructures for floating wind turbines has to account for both capital costs and power quality. To investigate the existing research on the optimization of floating wind turbines, a literature review was conducted. Evidently, there is limited research investigating both cost and power quality optimization. Furthermore, the majority of the optimization techniques presented are accompanied by a computationally expensive substructure analysis. Moreover, in the early stage of the substructure design, a variety of different alternative scenarios are tested, requiring an extensive number of optimizations. Thus, the computational resources required by the optimization become a major concern. One promising approach to address this issue is substituting a computationally expensive analysis with a surrogate model. This solution could significantly increase the computational efficiency of the optimization process.

This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.

The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach.

The purpose of this study has been to develop an aerodynamic load model for the energy harvesting (traction) phase of leading-edge inflatable (LEI) kites operating in an airborne wind energy (AWE) context. The load model stems from multivariate polynomial regression analyses expressing the airfoil lift, drag and moment coefficients as polynomial functions of the angle-of-attack and 2D non-dimensional (relative to the chord length) shape parameters: non-dimensional tube diameter, maximum camber magnitude and chordwise position of maximum camber. The regression analyses relied on numerical data attained from computational fluid dynamics (CFD) simulations of the 2D flow fields around parameterised LEI wing profiles. The RANS equations, closed by the k-ω SST turbulence model, have been used for this purpose. The parameterisation and subsequent geometric construction of LEI wing profiles has been a key aspect of this study. As such, the effects of the shape parameters on the flow field have been assessed.

Floating wind turbines despite the the potential to harness energy from deep offshore areas where higher average wind speeds face challenges in terms of competitiveness. One approach to raising the competitiveness of a wind farm is to mitigate efficiency losses resulting from the wake effect. This report focuses on the combination of three notable wake effect mitigation strategies: layout optimization, yaw-based wake redirection, and turbine repositioning.

A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency.

Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly.

Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position error across most points within the movable range Both the tension of the mooring lines and the static stiffness of the floater showed to be sensitive to the position of floater, the direction of the wind load and the yaw of the wind turbine, with percentual changes ranging from 0.5% up to 50%. It results that the orientation of the mooring lines with correspondence of the prevailing wind direction, as well as that restrictive constraints on the tension and stiffness may be taken should be taken into account when designing a mooring system for turbine repositioning.

Overall, combining , yaw-based wake redirection, and turbine repositioning allows for greater wind farm AEP, with gains contingent on turbine movable range and upcoming wind speeds. Designing position mooring systems must factor in the influence of yaw-based wake redirection and turbine repositioning on mooring system tension and stiffness. More advanced analyses, including dynamic assessments, are essential for comprehending the mooring lines system's response to position mooring for turbine repositioning, and yaw-based wake redirection.

Considering the goals set by the international community, the implementation of new energy sources has to increase considerably in the next seven years. In this thesis, the focus is on the acceleration and improvement of the application of offshore wind turbines. The power produced using this technology should become 3.6 times more before the end of this decade to comply with the set goals.

To achieve this target, new solutions have to be developed. To this aim, the implementation of model predictive control for wind turbines in the last years has been investigated. This would allow to optimize different parameters at the same time, such as maximization of energy production while minimizing the perceived loads. For this application, it is necessary to have forecasts of different features of the turbine, especially loads, with a higher frequency. As a result, the main research topic is defined as 'Can data-driven surrogate models be used for forecasting load time series on offshore wind turbines?'.

To answer this question, first environmental conditions are sampled within limits deducted from real data through Halton sequencing, and next simulations are run through OpenFAST to determine the resulting loads acting on the turbine. Within all the features resulting from the simulation, only five inputs and five target outputs are selected. This is the result of various considerations. Given the desire to develop a realistic methodology, the input variables are first filtered by assessing their availability from measurement devices. Next, the relationships between the variables are analyzed through cross-correlation to determine the degree of influence of each input on the output.

Using this data, a training database is created. It is used to train two different types of surrogate models, one linear and one non-linear, respectively ARIMAX and LSTM. These are implemented to generate a 30-second forecast of the moments acting at the root of the blade. To do so, the algorithms are trained using different variables as exogenous inputs to assess the models' performance in different cases. Given the wide range of target features, for LSTM two different behaviors are identified and the blade edgewise and flapwise moments are taken as examples. The hyper-parameters are tuned on the blade edgewise moment and lead to overfitting when applied to the blade flapwise and out-of-plane moment.

The obtained results show that the RMSE in ARIMAX is up to seven times larger than the one obtained from the application of LSTM. Within the non-linear models, the one resulting in the lowest percentage error for the blade edgewise, pitching, and in-plane moment considers the wind reference speed, the wind speed time series, and the corresponding tip deflection as exogenous inputs. Very low RMSE errors are obtained for all variables. Furthermore, it is concluded that while it is possible to implement LSTM in real-life, this is not achievable for ARIMAX.

Energy transition is imperative to effectively address the pressing issue of climate change resulting from global warming. In this transition, offshore wind power assumes a pivotal role as a crucial and indispensable source of clean and renewable energy. Offshore benefits become more pronounced as the offshore locations progress far offshore. In deep-water areas, where 80% of the worldwide offshore wind energy can be potentially harnessed, the utilization of floating foundations becomes essential instead of traditional bottom fixed ones. The present study seeks to investigate the disparities in power generation and energy production that arise from the replacement of bottom fixed wind turbines with floating counterparts. The former is represented by the monopile foundation, while the latter by the spar buoy. The power performance difference lies in the ability of floating structures to move, which can lead to suboptimal positioning of the rotor relative to the incoming wind inflow, mainly due to spar’s pitch and surge motions. The investigation is conducted using two distinct aerodynamic model of lower and higher fidelities, BEM and OLAF, respectively, to assess their effects on the outcomes.
In the field of offshore wind turbine design, engineers rely on aero-hydro-servo-elastic software codes to simulate the dynamic behavior of floating offshore wind turbine systems in offshore environments. OpenFAST, an open-source software, has been extensively developed and validated for conducting such investigations. In this study, OpenFAST is employed to develop both floating and bottom fixed wind turbine models. Specifically, a coupled aero-hydro-servo-elastic model of a floating spar wind turbine is created, and the simulated motion of the spar is compared with measurements obtained from an actual floating turbine deployed in the Hywind Scotland floating offshore wind farm. Metocean data, spar measurement data, and spar system descriptions are provided by Equinor to facilitate this benchmarking process. Additionally, an equivalent monopile wind turbine model is developed for energy yield comparison purposes.
The simulated spar motion results of the developed OpenFAST model exhibit reasonable realism when benchmarked with the measured data for all load cases and modal analysis, despite assumptions and uncertainties influencing the model's accuracy. Model discrepancies primarily stem from undisclosed wind turbine parameters and controller strategy as well as some modeling simplifications inherent in OpenFAST. Nevertheless, through statistical, time series and power spectral density comparisons against full-scale Hywind measurements encompassing various wind speeds (below-rated, rated, and above cut-out), the developed floating model is validated, thereby ensuring the reliability of its energy production outcomes.
It is observed that the spar’s pitch and surge mean offset is mostly affected by the current speeds while varying wave conditions (height and period) influence their oscillation amplitudes. Power is slightly affected by the ocean conditions, primarily the wave effects, while it is more strongly influenced by the spar nacelle’s velocity and direction relative to the incoming wind, especially when subjected to lower wind speed fields. Finally, for both BEM and OLAF simulations, monopile bottom fixed structures produce higher amounts of energy annually compared to the floating spar counterparts. The implementation of alternative controllers specifically designed for FOWT, with optimization objectives focused on either maximizing power performance or ensuring structural integrity and longevity, results in estimated AEP reductions when utilizing a spar floater instead of a monopile foundation. For the BEM model, the estimated AEP decrease ranges from 3.46% to 8.62%, depending on the specific controller optimization objective. On the other hand, when employing the VPM-OLAF model, the estimated AEP reduction for a spar floater compared to a monopile substructure ranges from 4.50% to 9.58%, under similar conditions as previously mentioned. By employing OLAF instead of BEM, the computed annual energy yield for both Bottom Fixed and Floating offshore wind turbines increases by approximately 25%.
In conclusion, it is recommended that future research prioritizes resolving identified discrepancies in the model setup, addressing phenomena not adequately captured by the current OpenFAST model, and conducting additional validation of the spar model using a wider range of measured parameters.

Floating Offshore Wind Energy represents an enormous potential for the future of wind energy and will play a pivotal role in the global energy transition. However, important technical challenges still need to be overcome by the industry regarding the standardization of processes like transportation, floating substructure concept, and supply chain optimization. To successfully achieve these milestones, FOWTs must be accurately modeled to minimize uncertainties to ultimately attract investors and capital in the industry. This thesis has the objective of developing and validating a high-fidelity model for the hydrodynamics of FOWTs with the ultimate goal of creating reliable results to be used as a benchmark for faster mid-fidelity software mostly used in the offshore industry.

The model expanded throughout this thesis is developed in OpenFOAM C++ framework. The whole numerical model is built upon three main pillars: Waves2Foam used for the generation of relaxation zones in the numerical wave tank for wave-current generation and absorption; MoorDyn/Moody numerical tools employed for the mooring lines spatial discretization for the station keeping of the floating platform; InterFoam applied as the main OpenFOAM solver for the multi-phase solution which, in combination with a mesh morphing technique and a rigid body solver, is capable of deforming the mesh to accommodate the 6DOF motions of the platform.

A sequential approach has been undertaken. The first step involves the simulation of second-order Stokes wave propagation in an infinite 2D wave tank, followed by a sensitivity study with an associated uncertainty quantification. The next step involves conducting 3D simulations of a floating box, comparing motion and forces under different mooring models: dynamic FEM, dynamic lumped mass, and quasi-static catenary. In the last step, the results and lessons learned from the previous studies are combined for coupled hydrodynamic/aerodynamic simulations of the OC4 DeepCwind semi-submersible floater with the 5MW NREL offshore wind turbine. Forces, displacements, and tensions are extrapolated and compared with experimental and numerical results, validating the model's accuracy.

Simulated across diverse wind and wave conditions, the OpenFOAM model demonstrated remarkable robustness and excellent ability to achieve convergence even under challenging environmental conditions, while maintaining a high level of result accuracy. The framework effectively reproduced the hydrodynamic behavior of a Floating Offshore Wind Turbine (FOWT), establishing itself as a dependable and reliable tool for conducting future high-fidelity FOWT simulations.