The rapid expansion of offshore wind energy, especially into intermediate depths and more non-linear wave environments, has increased the need for accurate prediction of wave-structure interactions, particularly wave run-up and the associated hydrodynamic forces on monopile foundations. These predictions are essential for ensuring the structural integrity, safety, and cost-efficiency of offshore wind turbines. Existing semi-empirical formulas are limited in their applicability, often overestimating run-up heights and failing to capture the complex non-linear interactions that occur under steep (So > 0.03) or near-breaking wave conditions typical of intermediate water depths.

This thesis presents a numerical model based on Computational Fluid Dynamics (CFD), developed using OpenFOAM and the waves2Foam toolbox, to predict wave run-up and the associated hydrodynamic forces on monopiles under varying hydrodynamic conditions. The model was validated against experimental data from de Vos et al. (2007), achieving excellent agreement with less than 5% error in peak wave run-up predictions. Following validation, the model was applied to assess the influence of scale effects, turbulence, monopile geometry, and key wave characteristics on run-up behavior and force magnitude.

The results confirm that scale effects on surface elevation and wave run-up are limited, while hydrodynamic forces show some sensitivity, particularly viscous forces in the vertical direction. Steep, high-energy waves near the breaking thresholds (So ≈ 0.04 to 0.05) produce significantly larger run-up heights and slamming forces, underlining their importance in design considerations. The geometry of the monopile also impacts localized flow patterns and wave wrapping, while turbulence contributes to increased lateral and viscous forces. However, its effect on the critical run-up at the front of the structure remains minimal.

By integrating turbulence modeling, scaling, and mesh optimization, this study establishes a robust, scalable, and accurate numerical framework for simulating complex wave-structure interactions. The model demonstrates significant improvements over existing empirical approaches, especially in intermediate water depths where non-linearity, shoaling, and breaking are present.

While the model shows excellent predictive capabilities, areas for further improvement remain. These include local mesh refinement around the monopile, higher-order discretization schemes to better capture breaking waves, and more extensive validation with recent experimental datasets. However, these enhancements would come at significant computational cost. Given the already high level of accuracy (>95%) in capturing peak wave run-up, the current model offers a practical balance between precision and efficiency. Future efforts should focus more on applying the model to irregular wave conditions and additional hydrodynamic parameters rather than fine-tuning. Researchers with greater computational resources may explore these improvements but should weigh the trade-off between added accuracy and computational expense.

The model configurations are publicly available to support future research and industry adoption: https://github.com/hiddded/MSc-thesis-wave-run-up-on-monopiles

Offshore floating photovoltaics (OFPVs) show great potential for future renewable energy generation by complementing existing wind energy. To maximize power output, it is essential to accurately predict the response of these structures under wave-induced forces.

Although finite-element fluid-structure interaction (FE-FSI) models can simulate the response of very large floating structures (VLFSs) with high accuracy, they are computationally expensive and time-intensive. To address this challenge, this thesis develops and optimizes three surrogate deep learning models: a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a hybrid CNN-LSTM model. An iterative trial-and-error approach was employed to fine-tune the hyperparameters and improve model performance.

The CNN model accurately captured spatial features but struggled with generalization across varying sea states. The LSTM model captured temporal patterns well, but had lower accuracy and longer training times. The hybrid CNN-LSTM model combined the strengths of both approaches, achieving a low weighted absolute percentage error (WAPE) of 0.98 %. All models performed well under typical wave conditions but exhibited reduced accuracy in extreme scenarios, particularly for low-amplitude tilts. A sensitivity analysis indicated that these errors were likely caused by insufficient representation of extreme sea states in the training data.

The hybrid CNN-LSTM model was selected as the model that most accurately predicts the response of VLFSs due to its balanced performance, minimal prediction time, and generalization capability across typical operating conditions. While the model shows high reliability for typical sea states, its performance declines for extreme conditions, such as extremely large waves or very small tilts. Despite reduced reliability in extreme conditions, the model provides a promising tool for real-time OFPV response prediction and preliminary studies.

The offshore wind industry has grown significantly since the installation of the first offshore wind farm in Vindeby, Denmark, in 1991. In recent years, the need to expand offshore wind capacity has led to an increased interest in floating offshore wind turbines (FOWTs), especially for deeper waters where bottom-fixed structures are impractical. This research explores the feasibility and behavior of daisy-chaining FOWTs to reduce mooring costs and anchor points, which currently account for a significant portion of installation expenses and are challenging to install.

The study focuses on spar-type FOWTs, investigating how daisy-chaining impacts the motion, stability, and mooring line forces in different wave and wind conditions. By conducting dynamic simulations with OrcaFlex software, two configurations, triangular and hexagonal, were analyzed to assess the effects of connecting multiple FOWTs. Results indicate that the daisy-chaining doesn’t increase the amplitude of roll and pitch motions compared to a single FOWT, but the system is susceptible to resonance in high sea states, causing excessive yaw motions around the z-axis. These resonances occur due to certain modes in the system being excited by peak wave frequencies, leading to amplified loads.

Despite these challenges, the study demonstrates that daisy-chaining can be feasible if the mooring and connection line design parameters are optimized to avoid resonance issues. The forces experienced in the mooring lines were found to be well within the available safe working load of industry-standard steel wire ropes, but careful design considerations are necessary to mitigate fatigue and ensure stability. This research contributes to the limited literature on shared mooring systems for FOWTs and offers insights into potential cost saving and design strategies for future offshore wind farm projects.

In this study, we establish a foundational framework for controlling flexible floating structures by integrating state-of-the-art Model Predictive Control (MPC) with a high-fidelity Finite Element model representing these structures. Our specific objective is to enhance the stability of floating structures in offshore engineering applications. We commence with a comprehensive literature review to identify potential challenges, which informs the development of a well-structured research plan that addresses these challenges comprehensively.

From the insights gained during the literature review, we formulate a theoretical framework that serves as the basis for our methodology. We subsequently examine two distinct control scenarios—regular and irregular wave conditions—serving as a Proof of Concept, and we discuss the significant observations derived from these experiments. In conclusion, we summarize our findings and provide recommendations for future research directions.

We utilize a two-way monolithic Finite Element formulation of the flexible floating structure as a representation of the real-world system we aim to control. For our control strategy, we implement Model Predictive Control, which facilitates the incorporation of advanced functionalities, including the application of constraints. To enhance computational efficiency, we employ a Reduced Order Model (ROM) through Dynamic Mode Decomposition with Control (DMDc), trained using open-loop data derived from the Finite Element model. Additionally, we implement a Kalman filter to reconstruct the system's state from sparse and noisy measurements obtained from the floating structure.

We design two Reduced Order Models specifically for controlling the floating structure under the aforementioned conditions. Prior to executing the control strategies in these distinct scenarios, we conduct an in-depth investigation of DMDc, exploring its relationship with Koopman theory. We generate open-loop data free from pollution using the Finite Element model, which is subsequently utilized to derive the Reduced Order Model. A convergence study is performed by analyzing the eigenvalues and amplitudes of the DMDc, following methodologies established in prior research. We validate the DMDc models against validation datasets, allowing us to select the model exhibiting the smallest validation error. Furthermore, we ensure that the training datasets inherently encompass the wet modes by employing Proper Orthogonal Decomposition.

Our initial control scenario involves managing the floating structure under regular wave conditions. This case is pivotal for acquiring fundamental insights into the control mechanisms. We observe that the frequency of the control input aligns precisely with the excitation frequency. Subsequently, we extend our study to encompass the control of the floating structure in irregular wave conditions, characterized by a sea state defined by the JONSWAP spectrum. Consistent with our findings from the regular wave scenario, we discover that the control mechanism exploits the natural frequencies of the floating structure, which closely correspond to the energy-dense region of the sea state's spectrum. This is achieved by amplifying the reflected wave, thereby counteracting the incoming wave and reducing energy input into the system.

In conclusion, we advocate for further research to bolster the proposed method by examining various structural properties and wave environments, thereby providing robust evidence to validate the approach presented in this study. Our findings indicate that the integration of advanced control strategies, such as MPC and DMDc, holds significant promise for optimizing the performance and stability of flexible floating structures in dynamic offshore conditions.