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

To meet global green energy targets, the bottom founded offshore wind industry is looking for ways to economically expand markets to deeper waters. A reduction of the hydrodynamic load is necessary to achieve this. One option is to perforate the monopile around the splash zone. Here the related work of Q. Star is continued through the implementation of a 2D Navier-Stokes based LES CFD model with VMS closure in Gridap.jl. The numerical simulations are gathered to create a dataset on which a machine learning surrogate model is trained. The analysis does not include secondary design aspects such as manufacturing, installation, noise mitigation, scour, corrosion, acidification, or marine growth. Neither does it require secondary fluid phenomena such as 3D simulations, FSI, VIV, and fatigue.
Analysis of 2D perforated cylinders, built up parametrically using the wall thickness, angle of attack, diameter, inflow velocity, number of perforations and porosity as input parameters shows that the first two are of negligible influence, and number three and four can, at least for their mean values, be modelled accurately using Morison equations. A non-scaled diameter does prove important in reducing the random nature of frequency-based effects. The number of perforations and the porosity provide complex interactions both from a design-force mean and variability perspective, as well as for the frequencies and vibrations they generate. A 2D LES-VMS model gives the perfect trade-off between cost and accuracy, predicting a possible drag reduction of more than 50% compared to a closed cylinder with large diameter.
Different machine learning surrogate models are analysed with the goal of massively speeding up the analysis in the future. This is achieved by a factor of 350 thousand using Random Forests, Gaussian Processes and Neural Networks. Although the Gaussian Processes preliminary show the best accuracy, below 6% error for millisecond predictions, further work on Neural Networks could give them the advantage in future analyses.