MULTI-DISCIPLINARY DESIGN ANALYSIS AND OPTIMIZATION (MDAO) OF WIND TURBINES

Abstract

The purpose of this thesis is to enhance the progress of an open-source platform called DAFoam, focused on Multidisciplinary Design Optimization (MDO). More precisely, the aim is to tailor DAFoam for MDO in wind turbines, serving the wind energy community. Wind energy is becoming increasingly important as a renewable energy source due to its environmental and economic benefits. The wind turbine blades play a crucial role as they directly engage with the wind, exerting a substantial influence on the overall performance of the system. Consequently, optimizing the design of these blades is vital to improve efficiency and minimize costs in wind turbine operations. Low-fidelity simulation and optimization, as well as high-fidelity optimization, are concepts often used in engineering and scientific research, particularly in the field of wind turbine design and analysis. They are interconnected approaches that help engineers and researchers refine and enhance the performance of wind turbines while managing computational complexity and resource requirements. Low-fidelity simulations involve using simplified or coarse models to represent the behavior of a system. In the context of wind turbine design, low-fidelity simulations might use simplified fluid dynamics models or simplified structural models to predict the performance of the turbine. These simulations are quicker to run and require fewer computational resources compared to high-fidelity simulations. However, they sacrifice accuracy and detail for speed. Low-fidelity optimization refers to the process of tuning or improving the design of a wind turbine using the results obtained from low-fidelity simulations. Engineers use various optimization techniques to iteratively adjust design parameters, such as blade shape, tower height, or generator size, with the goal of improving the overall performance of the turbine. Since low-fidelity simulations are computationally cheaper, they allow for a larger number of design iterations to be explored within a given time frame. Conversely, high-fidelity simulations entail utilizing intricate, precise models that accurately depict the behavior of the wind turbine, encompassing intricate details and complexities. These simulations capture more intricate physical phenomena and provide more accurate predictions of the turbine's performance. However, high-fidelity simulations are computationally intensive and may require significant computational resources and time to run. Between low-fidelity and high-fidelity approaches in wind turbine optimization, there is a trade-off between accuracy and computational cost. Low-fidelity simulations are often used as initial screening tools to quickly explore a wide range of design possibilities and identify promising configurations. Once a set of potential designs is identified, high-fidelity simulations are employed to validate and refine the design further. High-fidelity simulations provide more accurate insights into the complex flow patterns, structural dynamics, and other critical factors affecting turbine performance. However, due to their computational intensity, they are usually limited in the number of design iterations that can be explored within a reasonable time frame. Low-fidelity simulations and optimization serve as a way to guide the design process and narrow down the search space before committing significant computational resources to high-fidelity simulations. This two-tiered approach allows engineers to strike a balance between accuracy and efficiency, ultimately leading to the development of better-performing wind turbine designs. This thesis explores the application of low-fidelity optimization using QBlade as a preliminary step toward achieving high-fidelity wind turbine optimization. The initial part of the thesis focuses on the implementation of low-fidelity optimization techniques with QBlade, an open-source software widely used for aerodynamic simulations of horizontal-axis wind turbines. The low-fidelity approach serves as a cost-effective and rapid exploration of the design space, enabling the identification of promising design configurations. After establishing the groundwork through low-fidelity optimization, the primary objective of this thesis is to delve into high-fidelity wind turbine optimization. High-fidelity optimization aims to achieve a more accurate representation of the wind turbine's performance characteristics by considering additional complexities and factors such as structural integrity, aeroelasticity, and dynamic behavior. Unlike low-fidelity optimization, which often relies on simplified models and approximations, high-fidelity optimization takes into account finer details and complexities of the wind turbine design. To attain the high-fidelity optimization, concurrent aero-structural multidisciplinary design optimization (MDO) approach for wind turbine blades is implemented, which considers the interaction between the aerodynamic and structural aspects of the blade and optimizes them simultaneously. The optimization aims to maximize the torque generated by the blade while minimizing its mass. The proposed approach uses DAFoam software for CFD simulation, TACS for FEM simulation, and Mphys under the OpenMDAO framework for fluid-structure interaction between the CFD and FEM. The optimization of wind turbine blade design is undertaken using high-fidelity concurrent multi-disciplinary aerodynamic design optimization, employing gradient-based adjoint solvers. This approach is applied through five distinct schemes utilizing DAFoam.