Research into advanced wind turbine design has shown that load alleviation strategies like bend-twist coupled blades and coned rotors could reduce costs. However these strategies are based on nonlinear aero-structural dynamics providing additional benefits to components beyond the blades. These innovations will require Multi-disciplinary Design Optimization (MDO) to realize the full benefits. This research expands the MDO capabilities of Horizontal Axis Wind Turbines. The early research explored the numerical stability properties of Blade Element Momentum (BEM) models. Then developed a provincial scale wind farm siting models to help engineers determine the optimal design parameters. The main focus of this research was to incorporate advanced analysis tools into an aero-elastic optimization framework. To adequately explore advanced designs with optimization, a new set of medium fidelity analysis tools is required. These tools need to resolve more of the physics than conventional tools like (BEM) models and linear beams, while being faster than high fidelity techniques like grid based computational fluid dynamics and shell and brick based finite element models. Nonlinear beam models based on Geometrically Exact Beam Theory (GEBT) and Variational Asymptotic Beam Section Analysis (VABS) can resolve the effects of flexible structures with anisotropic material properties. Lagrangian Vortex Dynamics (LVD) can resolve the aerodynamic effects of novel blade curvature. Initially this research focused on the structural optimization capabilities. First, it developed adjoint-based gradients for the coupled GEBT and VABS analysis. Second, it developed a composite lay-up parameterization scheme based on manufacturing processes. The most significant challenge was obtaining aero-elastic optimization solutions in the presence of erroneous gradients. The errors are due to poor convergence properties of conventional LVD. This thesis presents a new LVD formulation based on the Finite Element Method (FEM) that defines an objective convergence metric and analytic gradients. By adopting the same formulation used in structural models, this aerodynamic model can be solved simultaneously in aero-structural simulations. The FEM-based LVD model is affected by singularities, but there are strategies to overcome these problems. This research successfully demonstrates the FEM-based LVD model in aero-elastic design optimization. / Graduate / 0548 / pilot.mm@gmail.com

This study aims to explore the ability of a multi-criteria decision making with analytical hierarchy process (MCDM-AHP) model to emulate the results of a cost benefit analysis (CBA) model in the context of offshore wind farm siting within the Swedish exclusive economic zone (EEZ). The research question addressed is whether the MCDM-AHP analysis produces similar results to the CBA analysis. In addition to this, the strengths and weaknesses of each model is explored. The MCDM-AHP model employs the spatial criteria in a more basic manner compared to the CBA model, simplifying the evaluation process while still explaining 89.5% of the variation in the CBA model and defining similar areas as suitable. Thus, it can be concluded that the MCDM-AHP model adequately emulates the CBA model within the context of offshore wind farm siting within the Swedish EEZ. However, it is crucial to note that the two models produce outputs on different scales. While the CBA model provides levelized cost of energy (LCOE) values that can be thresholded for investment viability comparisons, the suitability score generated by the MCDM-AHP model remains a relative and arbitrary score within the model. Both models entail uncertainties, limiting their usage beyond making general assumptions or identifying areas of interest. The findings reveal that the CBA model demonstrates greater robustness when confronted with changes in spatial input parameters compared to the MCDM-AHP model. This discrepancy is attributed to the iterative computation process and consideration of flat cost inputs in the CBA model, whereas the MCDM-AHP model represents a linear combination of various spatial parameters. However, the calculated LCOE values in the CBA model are highly sensitive to changes in modeling assumptions regarding external parameters, resulting in significant linear variations. The LCOE values obtained from the CBA model baseline case fall within a range of 52.1 - 98.9 EUR/MWh, which aligns with similar studies, validating the CBA model. Nonetheless, caution should be exercised when considering these results as an accurate representation of the real world due to inherent uncertainties in cost inputs and the LCOE measure. The strengths of the MCDM-AHP model lie in its robustness when the order of relative importance remains stable for key spatial evaluators. It is sensitive to significant changes in water depth and wind speed, which heavily influence its output. The model's simplicity allows for a quick overview of the problem, but it requires assumptions that introduce uncertainties. Validation of the MCDM-AHP model using existing and planned offshore wind farms within the Swedish EEZ was possible but limited by the arbitrary scale and limited validation areas. The comparison between the two models could be enhanced with more comprehensive spatial and economic data for an in-depth CBA model, which could serve as a ground truth for the MCDM-AHP model. Nevertheless, the comparison made in this study considers the CBA model to be closer to the truth, acknowledging the underlying assumptions that should be considered during evaluation. In conclusion, within the context of offshore wind farm siting, the MCDM-AHP model produces outputs that are similar to the CBA model.