Ocean wave energy has the potential to play a crucial role in the shift to renewable energy. In order to improve wave energy conversion techniques, a recognition of the sub-optimal nature of traditional sequential design processes due to the interconnectedness of subsystems such as the geometry, power take-off, and controls is necessary. A codesign optimization in this paper seeks to include effects of all subsystems within one optimization loop in order to reach a fully optimal design for an oscillating-surge wave energy converter. A width and height sweep serves as a brute force geometry optimization while optimizing the power take-off components and controls using a pseudo-spectral method for each geometry. An investigation of electrical power and mechanical power maximization also outlines the contrasting nature of the two objectives to illustrate electrical power maximization's importance for identifying optimality. The codesign optimization leads to an optimal design with a width of 12 m and a height of 10 m. The power take-off and controls systems are also examined more in depth to identify important areas for increased focus during detailed design. Ultimately, the codesign optimization leads to a 61.4% increase in the objective function over the optimal design from a sequential design process while also requiring about half the power take-off torque. / Master of Science / Ocean wave energy has the potential to play a crucial role in the shift to renewable energy sources. The Earth's vast oceans have immense energy potentials throughout the world, which often follow the seasonal trends of electricity demand in temperate climates. Wave energy harvesting is a technology which has been studied significantly, but has not yet experienced commercial success, partially due to the lack of convergence on a type of wave energy converter. In order to improve wave energy conversion techniques and support the convergence on a particular type, a recognition of the sub-optimal nature of traditional sequential design processes due to the interconnectedness of subsystems is necessary. A codesign optimization in this paper seeks to include effects of all subsystems within one optimization loop in order to reach a fully optimal design for an oscillating-surge wave energy converter. A width and height sweep serves as a brute force geometry optimization while optimizing the power take-off and control components for each geometry. The codesign optimization leads to an optimal design with a width of 12 m and a height of 10 m. Ultimately, the codesign optimization leads to a 62% increase in performance over the result from a sequential design process.

No description available.

No description available.

Over the last few decades, many factors, including increased electrification, have led to a critical need for fast and efficient transient cooling. Thermal management systems (TMSs) are typically designed using steady-state assumptions and to accommodate the most extreme operating conditions that could be encountered, such as maximum expected heat loads. Unfortunately, by designing systems in this manner, closed-loop transient performance is neglected and often constrained. If not constrained, conventional design approaches result in oversized systems that are less efficient under nominal operation. Therefore, it is imperative that \emph{transient} component modeling and subsystem interactions be considered at the design stage to avoid costly future redesigns. Simply put, as technological advances create the need for rapid transient cooling, a new design paradigm is needed to realize next generation systems to meet these demands. <br><br>In this thesis, I develop a new design approach for TMSs called hierarchical control co-design (HCCD). More specifically, I develop a HCCD algorithm aimed at optimizing high-fidelity design and control for a TMS across a system hierarchy. This is accomplished in part by integrating system level (SL) CCD with detailed component level (CL) design optimization. The lower-fidelity SL CCD algorithm incorporates feedback control into the design of a TMS to ensure controllability and robust transient response to exogenous disturbances, and the higher-fidelity CL design optimization algorithms provide a way of designing detailed components to achieve the desired performance needed at the SL. Key specifications are passed back and forth between levels of the hierarchy at each iteration to converge on an optimal design that is responsive to desired objectives at each level. The resulting HCCD algorithm permits the design and control of a TMS that is not only optimized for steady-state efficiency, but that can be designed for robustness to transient disturbances while achieving said disturbance rejection with minimal compromise to system efficiency. Several case studies are used to demonstrate the utility of the algorithm in designing systems with different objectives. Additionally, high-fidelity thermal modeling software is used to validate a solution to the proposed model-based design process. <br>