TEAMER: Experimental Characterization of a Laboratory-Scaled Oscillating Surge Wave Energy Converter
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This data is a result of an experimental campaign to characterize the hydrodynamics and performance of a laboratory-scale oscillating surge wave energy converter (OSWEC). The device was 85 cm wide, 1.4 meters tall, and 14 cm thick and was tested in the Sea Wave Environmental Lab (SWEL) wave tank at the National Renewable Energy Laboratory which is 2.5 meters wide with a water depth of 1.3 meters. The device included fifteen pressure sensors on the flap face, two 6-axis load cells at the hinge, an encoder to measure flap position, and a motor to emulate a PTO and absorb power. We provide a full summary of the device and experiments in the TEAMER Post-Access Report titled "Optimal control of an oscillating surge wave energy converter". This DropBox directory contains data from four types of experiments: 1. Buoyancy Tests - We measure the torque required to hold the flap at different angles to characterize buoyancy torque as a function of position. 2. Locked Flap (Excitation) Tests - We measure the torque on a locked flap subject to different wave parameters to extract the excitation torque coefficient. 3. Forced Oscillation (Radiation) Tests - We force the flap to oscillate at different periods and amplitudes to extract added inertia and radiation damping coefficients. 4. Control Tests - We subject the flap to different waves and use a linear damping controller to emulate a PTO and extract absorbed power and capture width ratio (CWR) as a function of wave and control parameters. This data set includes raw and processed time series data from the encoder and load cells, as well as calculated hydrodynamic and performance parameters from the tests. We include a README document as well as a spreadsheet with individual test details as a reference. Funding for this experimental campaign was provided by the TEAMER Program under RFTS 10 and was a collaboration between the University of Washington and the National Renewable Energy Laboratory.
Evaluation of a Wave Powered Water Pump Performance by Ocean Field Testing and WEC-Sim Modeling
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This submission from AMEC (the Atlantic Marine Energy Center) includes data from an ocean field deployment of a wave powered water pump in March 2023. The wave pump is an upweller device, designed to enhance macroalgal aquaculture. The wave pump device was deployed off the coast of Isles of Shoals Appledore Island in Maine, USA. The data were collected using a custom-built DAQ module comprised of Arduino Unos. GPS time stamp accompanies the data. The data are volumetric flow rate from the wave pump, and relative motion of the device between float and spar buoys. Flow rate is measured by flow meter, and relative motion is measured by lidar. Calibration data for the lidar and flow meter sensors are included. This data set also includes synchronous Sofar Spotter buoy data from a mooring approximately 300 feet away from the wave pump mooring. Video data from the deployment are included from both on-board the device sporadically throughout the deployment, and from a webcam for a short duration of the deployment. Hydrophone data were also taken co-currently, and are available by contacting Martin Wosnik at the University of New Hampshire. The Matlab code used to process the field data is incorporated. A biological assessment is included which aided the NEPA consultation process, prior to conducting the field deployment. A WEC-Sim numerical model of the wave pump, and a re-design effort are part of this work. Code used to validate the WEC-Sim model from the field data are also included.
Evaluation of a Wave Powered Water Pump Performance by Ocean Field Testing and WEC-Sim Modeling
공공데이터포털
This submission from AMEC (the Atlantic Marine Energy Center) includes data from an ocean field deployment of a wave powered water pump in March 2023. The wave pump is an upweller device, designed to enhance macroalgal aquaculture. The wave pump device was deployed off the coast of Isles of Shoals Appledore Island in Maine, USA. The data were collected using a custom-built DAQ module comprised of Arduino Unos. GPS time stamp accompanies the data. The data are volumetric flow rate from the wave pump, and relative motion of the device between float and spar buoys. Flow rate is measured by flow meter, and relative motion is measured by lidar. Calibration data for the lidar and flow meter sensors are included. This data set also includes synchronous Sofar Spotter buoy data from a mooring approximately 300 feet away from the wave pump mooring. Video data from the deployment are included from both on-board the device sporadically throughout the deployment, and from a webcam for a short duration of the deployment. Hydrophone data were also taken co-currently, and are available by contacting Martin Wosnik at the University of New Hampshire. The Matlab code used to process the field data is incorporated. A biological assessment is included which aided the NEPA consultation process, prior to conducting the field deployment. A WEC-Sim numerical model of the wave pump, and a re-design effort are part of this work. Code used to validate the WEC-Sim model from the field data are also included.
Evaluation of 1/100th Scale Mooring Systems for Wave Energy Converters
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This study evaluates the feasibility, accuracy, and limitations of using 1/100th scale physical mooring systems to represent full-scale mooring behavior for wave energy converters (WECs) during small-scale tank testing. The work focuses on an RM3-style point absorber deployed in conditions representative of the PacWave South test site and examines whether small-scale physical testing can reliably inform numerical modeling, design decisions, and future prototype development. Testing occurred in the National Lab of the Rockies' 13,000 gal wave tank. The data channels captured were: Wave height, mooring line tension, 6DOF orientation and position of two WEC bodies. The tests conducted were: three wave cases, free decay, forced displacement, tank characterization, and mooring line spring constant characterization For more information on the project and the dataset please see the "Dataset Description" resource below.