Masters Thesis Presentation: Design, Testing, And Neural Network Modeling Of A Low-Friction Wave Energy Testbed

To achieve net-zero emissions by 2050 amidst rising energy demands, a shift toward renewable energy is essential. Wave Energy Converters (WECs) are a promising but underdeveloped technology that could outperform solar and wind. However, high costs and friction challenges in prototypes hinder their viability. This thesis presentation details the design and testing of a Low Friction Testbed (LFT) using air bearings and a voice coil actuator to improve WEC efficiency and reduce costs, enabling more effective testing and validation of advanced control strategies.

Learn more from his Thesis Defense: https://youtu.be/RUmbTqUUH_8?si=f7ZDYWDvML7RU09U 

For a deeper dive check the full thesis here: https://doi.org/10.37099/mtu.dc.etdr/1767