The point absorber is one of the most popular types of ocean wave energy converter (WEC) that harvests energy from the ocean. Often such a WEC is deployed in an ocean location with tidal currents or ocean streams, or serves as a mobile platform to power the blue economy. The shape of the floating body, or buoy, of the point absorber type WEC is important for the wave energy capture ratio and for the current drag force. In this work, a new approach to optimize the shape of the point absorber buoy is developed to reduce the ocean current drag force on the buoy while capturing more energy from ocean waves. A specific parametric modeling is constructed to define the shape of the buoy with 12 parameters. The implementation of neural networks significantly reduces the computational time compared to solving hydrodynamics equations for each iteration. And the optimal shape of the buoy is solved using a genetic algorithm with multiple self-defined functions. The final optimal shape of the buoy in a case study reduces 68.7% of current drag force compared to a cylinder-shaped buoy, while maintaining the same level of energy capture ratio from ocean waves. The method presented in this work has the capability to define and optimize a complex buoy shape, and solve for a multi-objective optimization problem. / Master of Science / The marine kinetic energy includes ocean waves power, tidal power, ocean current power, ocean thermal power and river power. The total potential marine kinetic energy in 2021 is 2300 TWh/year, where 1400 TWh/year is from the ocean wave power. To discover and harvest the huge potential power from the marine, researchers have been developed for different types of WECs for several decades. One of the most successful concepts is the point absorber typed WEC, which can extract waver energy from the heaving vibration motion of a floating body and convert the kinetic energy into electrical energy. This thesis presents an optimization strategy to optimize the shape of the floating body to improve power extraction and reduce the installation cost by implementing the machine learning tool and genetic algorithm. Compared with the state-of-the-art optimization strategies, the proposed optimization method allows the floating body to have more parameters in shape changes and reduces the computational cost from minutes to milliseconds. The final optimized floating body shape performs extraordinarily compared to the other two state-of-the-art floating body shapes.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/113294 |
| Date | 19 January 2023 |
| Creators | Lin, Weihan |
| Contributors | Mechanical Engineering, Zuo, Lei, Tafti, Danesh K., Acar, Pinar |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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