Browsing by Subject "Genetic Algorithm"
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Item Multi-domain multi-objective optimization of mechanisms: a general method with two case studies(2013-10) Sullivan, Thomas AdamWhile the design of mechanisms is a well-studied field, current optimization techniques generally focus on the kinematics and dynamics and relegate other aspects of the analysis to separate stages of the overall design process, resulting in a loss of optimality when the entire multi-domain system is considered. This thesis presents a general method by which a mechanism optimization problem may be efficiently formulated and solved, considering multiple competing design objectives across multiple analysis domains. Two case studies illustrate the practical application of this general method. The first is the kinematic-structural optimization of a hydraulic rescue spreader ("jaws of life"). The second is the kinematic-dynamic-thermodynamic optimization of a novel six-bar linkage for an internal combustion engine. A variety of powerful general-purpose multi-objective algorithms are available from the literature. In particular, genetic algorithms are well-suited to multi-objective problems, and the NSGA-II algorithm from this category is employed here. Three strategies are presented to formulate multi-domain mechanism optimizations in a way that can be solved efficiently by a multi-objective genetic algorithm and is free of explicit constraint functions even for complex problems. First, it is shown that the use of non-traditional design variables, such as angles and adaptive interpolations, can result in smaller design spaces to be searched and can guarantee that all optima lie within the selected range of a given design variable. It is also shown that traditional precision-position synthesis techniques can in some cases be employed in a preliminary analysis to reduce the dimension of the design space. Finally, a nested optimization structure is proposed in which kinematic design variables and objectives are optimized in an outer loop, with the non-kinematic problem being optimized in an inner loop at every outer loop iteration, improving the efficiency and stability of the optimization process. These techniques were applied to the hydraulic rescue spreader problem in order to design a six-bar mechanism that could exert a 10,000 pound force through a pair of jaws over a 24 inch spreading distance while maintaining performance-critical kinematic behavior and remaining light and compact enough to be a handheld tool. The structural stresses in each part of the linkage were modeled, using a combination of analytical methods and finite element analysis. The final optimization result was superior to a similar commercially available model with respect to all four kinematic and structural objectives. Having successfully optimized a low-speed mechanism with a structural motivation, the method was also applied to a high-speed mechanism with a thermodynamic motivation. A Stephenson-III six-bar linkage was developed in order to optimize the motion of the piston in an internal combustion engine and achieve a cylinder volume as a function of time most conducive to efficient combustion. A number of mechanical objectives relating to balancing and mechanism size were used in order to find a solution capable of practical implementation. A slight increase in thermal efficiency over a purely sinusoidal piston motion was obtained, along with satisfactory values of the mechanical objectives.Item A Study and Optimization of a Radial Ball Piston Pump for High-Speed Applications(2021-01) Bohach, GarrettEver increasing concern and regulations related to climate change has prompted hydraulic research to focus on improving energy efficiency and reducing emissions, with electrifying hydraulic systems considered a promising solution. This thesis studies the radial ball piston hydraulic architecture, and its capability to efficiently and compactly be integrated with a high-speed electric motor. A combination of analytical, numerical, and empirical models are utilized to create a detailed model of a radial ball piston unit and its losses. The model is experimentally validated using an off-the-shelf radial ball piston pump. After updating the model parameters, the model predicted the efficiency within 3-10 percent across the range of operating conditions. The model is then implementing within a genetic algorithm optimization framework that predicts performance across multiple operating conditions. The preferred embodiment discovered by the optimization reached 12,500 rpm while maintaining 80 – 90 percent efficiency across four quadrants of operation and packaged a 20kW machine within approximately 100mm outside diameter. The work within this thesis represents the first computationally fast and accurate model of a radial ball piston unit that was utilized within an optimization framework for design purposes. The results of this optimization demonstrate that the radial ball piston is an acceptable hydraulic architecture for four quadrant operation and integration with a high-speed electrical motor. However, a challenge with the architecture is that the ball piston kinematics generate an uneven clearance around the ball that results in a nearly constant torque loss independent of piston-cylinder clearance, creating an upper bound on the efficiency.