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Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation
The work aims to achieve optimal tidal energy conversion through a comprehensive approach of modeling, optimization, and control hardware-in-the-loop (CHIL) validation. By developing accurate models and employing optimization techniques, it seeks to identify efficient system confi...
Luo, F. et al Stony Brook University
Jul 17, 2023
6 Resources
0 Stars
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6 Resources
0 Stars
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BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset
The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...
Tripp, C. et al National Renewable Energy Laboratory
Dec 30, 2022
9 Resources
1 Stars
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9 Resources
1 Stars
Publicly accessible
Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging Software and Data
Software and testing data from the OH Hinsdale Wave lab for DOE-funded project on Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging. This project will perform foundational research and testing to accelerate the sector-wide development a...
Hollinger, G. et al Oregon State University
Oct 26, 2022
11 Resources
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11 Resources
0 Stars
Publicly accessible