Floating offshore wind farms enable wind energy deployment in deeper waters, where bottom-fixed turbines are not viable. However, integrating mooring systems and dynamic cables introduces additional challenges in cable routing, increasing design constraints and costs. While research on cable optimization for bottom-fixed wind farms is well-developed, studies focused on floating wind farms remain limited.
This thesis presents an optimization framework that minimizes inter-array cable length while ensuring compliance with mooring system constraints and loop topology requirements. A Mixed-Integer Linear Programming model is used to enforce spatial and technical constraints, structured into three phases: preprocessing, optimization, and postprocessing. Preprocessing defines feasible cable connections while preventing crossings with mooring lines. To manage complexity, turbines are grouped into clusters with defined boundaries to separate routing areas. The optimization phase determines the best cable layout while ensuring loop connectivity and balanced electrical loads. Postprocessing checks compliance with industry standards, identifying clearance violations and refining layouts for feasibility.
A case study of the London Array wind farm demonstrates the model’s effectiveness, achieving a 1.1% reduction in total cable length while maintaining the original layout structure. When adapted for floating wind, spatial constraints from mooring systems cause clearance conflicts, which are mostly resolved by scaling the layout. Minor turbine adjustments eliminate remaining issues. The study highlights that increasing the number of loops and reducing loop size creates more routing conflicts, particularly near the offshore substation and at cluster boundaries. It also finds that centrally positioned substations significantly reduce clearance violations compared to those at the field boundary.
This research provides practical insights into the spatial constraints of floating wind farms and offers a structured, computationally feasible approach to optimizing inter-array cable routing for large-scale farms.

Goal-oriented reduced-order models (GOROM) have the potential to represent complex dynamics in an interpretable way. In the GOROM procedure, modes are found which when used in a Galerkin projection of the governing equations, return the desired goal function with minimum error. Determining such models leads to a high-dimensional optimisation problem, which may have multiple extrema, making purely local optimisation inapplicable.
In this thesis, a global optimisation algorithm was derived for GOROM. The optimisation response surface was approximated with a surrogate model, built using the Kriging technique. In each iteration, the surrogate model was improved by including the objective function of the three points with the highest expected improvements. This iterative process was stopped when the expected improvement fell below a threshold.
First, the performance of the algorithm was confirmed for a simple non-GOROM optimisation. Then the algorithm was applied to GOROM optimisation for the forced Burgers equation, initially using a complex forcing function giving multiple minima and later to a forcing function derived from a DNS of a turbulent channel flow. The initial forcing required on the order of tens of degrees of freedom to be optimised. The corresponding response surface proved similar to that of the simple problems, showing these optimisations to be tractable.
For the DNS forcing, hundreds of coefficients needed to be optimised. Using the developed algorithm a better optimal was found than with the local algorithm, although substantially more computational resources would be required to confirm global optimality.