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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Turbulence Measurements from Compliant Moorings. Part II: Motion Correction

Acoustic Doppler velocimeters (ADVs) are a valuable tool for making highprecision measurements of turbulence, and moorings are a convenient and ubiquitous platform for making many kinds of measurements in the ocean. However—because of concerns that mooring motion can contaminate turbulence measurements and acoustic Doppler profilers are relatively easy to deploy—ADVs are not frequently deployed from moorings. This work details a method for measuring turbulence using moored ADVs that corrects for mooring motion using measurements from inertial motion sensors. Three distinct mooring platforms were deployed in a tidal channel with inertial motion-sensor-equipped ADVs. In each case, the motion correction based on the inertial measurements dramatically reduced contamination from mooring motion. The spectra from these measurements have a shape that is consistent with other measurements in tidal channels, and have a f^(5/3) slope at high frequencies—consistent with Kolmogorov’s theory of isotropic turbulence. Motion correction also improves estimates of cross-spectra and Reynold’s stresses. Comparison of turbulence dissipation with flow speed and turbulence production indicates a bottom boundary layer production-dissipation balance during ebb and flood that is consistent with the strong tidal forcing at the site. These results indicate that inertial-motion-sensor-equipped ADVs are a valuable new tool for measuring turbulence from moorings.

Kilcher, Levi F.↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Observations of marine animal interactions with a small tidal turbine

The risk of collisions between animals and operating tidal turbines remains a concern in the scientific and regulatory communities. A sensor package including optical cameras was deployed to monitor animal interactions with a small-scale (1 m 2 ) cross-flow tidal turbine. The turbine was deployed in Washington State, USA for 141 days at a site with peak flow speeds of 2.5 m/s. We analyze optical camera imagery spanning 109 days of turbine operation. The analyzed images contain 1044 observations of fish, fish schools, seabirds, or seals in the vicinity of the turbine. No instances of collision with seabirds or seals were observed. Seabirds were only observed during daylight hours and while the turbine was stationary. Both seals and fish were observed during both day and night and while the turbine was stationary and rotating. Four fish were observed colliding with the moving turbine and in all but one case the animals swam away following the collision. Over the same period of time, over fifty times more fish (224 individual fish and 5 fish schools) were observed passing the moving turbine without collision. Fish encounters were likely under counted due to the difficulty in discerning small fish from plant matter in the water column. These observations represent the first optical camera imagery showing fish, bird, and marine mammal interactions with a tidal turbine in North America. In addition to quantitative and qualitative discussion of the implications of our observations for collision risk, we discuss lessons learned on sampling schemes and deployment of machine learning for detection of animals to inform future data collection strategies in future monitoring campaigns.

16 TIDAL AND WAVE POWER↗
DOE OSTI2018

Sniffle : a step forward to measure in situ CO 2 fluxes with the floating chamber technique

Understanding how the ocean absorbs anthropogenic CO 2 is critical for predicting climate change. We designed Sniffle, a new autonomous drifting buoy with a floating chamber, to measure gas transfer velocities and air-sea CO 2 fluxes with high spatiotemporal resolution. Currently, insufficient in situ data exist to verify gas transfer parameterizations at low wind speeds (<4 m s -1 ), which leads to underestimation of gas transfer velocities and, therefore, of air-sea CO 2 fluxes. The Sniffle is equipped with a sensor to consecutively measure aqueous and atmospheric pCO 2 and to monitor increases or decreases of CO 2 inside the chamber. During autonomous operation, a complete cycle lasts 40 minutes, with a new cycle initiated after flushing the chamber. The Sniffle can be deployed for up to 15 hours at wind speeds up to 10 m s -1 . Floating chambers often overestimate fluxes because they create additional turbulence at the water surface. We correct fluxes by measuring turbulence with two acoustic Doppler velocimeters, one positioned directly under the floating chamber and the other positioned sideways, to compare artificial disturbance caused by the chamber and natural turbulence. The first results of deployment in the North Sea during the summer of 2016 demonstrate that the new drifting buoy is a useful tool that can improve our understanding of gas transfer velocity with in situ measurements. At low and moderate wind speeds and different conditions, the results obtained indicate that the observed tidal basin was acting as a source of atmospheric CO 2 . Wind speed and turbulence alone could not fully explain the variance in gas transfer velocity. We suggest therefore, that other factors like surfactants, rain or tidal current will have an impact on gas transfer parameterizations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Admiralty Inlet Advanced Turbulence Measurements: June 2014

This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in June of 2014. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on Tidal Turbulence Mooring's (TTMs). The TTM positions the ADV head above the seafloor to make mid-depth turbulence measurements. The inertial measurements from the IMU allows for removal of mooring motion in post processing. The mooring motion has been removed from the stream-wise and vertical velocity signals (u, w). The lateral (v) velocity has some 'persistent motion contamination' due to mooring sway. Each ttm was deployed with two ADVs. The 'top' ADV head was positioned 0.5m above the 'bottom' ADV head. The TTMs were placed in 58m of water. The position of the TTMs were: ttm01 : (48.1525, -122.6867) ttm01b : (48.15256666, -122.68678333) ttm02b : (48.152783333, -122.686316666) Deployments TTM01b and TTM02b occurred simultaneously and were spaced approximately 50m apart in the cross-stream direction. Units ----- - Velocity data (_u, urot, uacc) is in m/s. - Acceleration (Accel) data is in m/s^2. - Angular rate (AngRt) data is in rad/s. - The components of all vectors are in 'ENU' orientation. That is, the first index is True East, the second is True North, and the third is Up (vertical). - All other quantities are in the units defined in the Nortek Manual. Motion correction and rotation into the ENU earth reference frame was performed using the Python-based open source DOLfYN library (http://lkilcher.github.io/dolfyn/). Details on motion correction can be found there. Additional details on TTM measurements at this site can be found in the included Marine Energy Technology Symposium paper.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Admiralty Inlet Advanced Turbulence Measurements: May 2015

This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in May of 2015. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on a 'StableMoor' (Manufacturer: DeepWater Buoyancy) buoy and a Tidal Turbulence Mooring (TTM). These platforms position ADV heads above the seafloor to make mid-depth turbulence measurements. The inertial measurements from the IMU allows for removal of mooring motion in post processing. The mooring and buoy motion has been removed from the stream-wise and vertical velocity signals (u, w). The lateral (v) velocity has some 'persistent motion contamination' due to mooring sway. The TTM was deployed with one ADV, it's position was: 48 09.145', -122 41.209' The StableMoor was deployed twice, the first time it was deployed in 'wing-mode' with two ADVs ('Port' and 'Star') at: 48 09.166', -122 41.173' The second StableMoor deployment was in 'Nose' mode with one ADV at: 48 09.166', -122 41.174' Units ----- - Velocity data (_u, urot, uacc) is in m/s. - Acceleration (Accel) data is in m/s^2. - Angular rate (AngRt) data is in rad/s. - The components of all vectors are in 'ENU' orientation. That is, the first index is True East, the second is True North, and the third is Up (vertical). - All other quantities are in the units defined in the Nortek Manual. Motion correction and rotation into the ENU earth reference frame was performed using the Python-based open source DOLfYN library (http://lkilcher.github.io/dolfyn/). Details on motion correction can be found there. Additional details on TTM measurements at this site can be found in the included Marine Energy Technology Symposium paper.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Hydrokinetic Canal Measurements: Inflow Velocity, Wake Flow Velocity, and Turbulence

The dataset consist of acoustic Doppler current profiler (ADCP) velocity measurements in the wake of a 3-meter diameter vertical-axis hydrokinetic turbine deployed in Roza Canal, Yakima, WA, USA. A normalized hub-centerline wake velocity profile and two cross-section velocity contours, 10 meters and 20 meters downstream of the turbine, are presented. Mean velocities and turbulence data, measured using acoustic Doppler velocimeter (ADV) at 50 meters upstream of the turbine, are also presented. Canal dimensions and hydraulic properties, and turbine-related information are also included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

MOIS CAD Models

SolidWorks models of the Modular Ocean Instrumentation System (MOIS) data acquisition system components in it's subsea enclosure. The zip file contains all the components necessary for the assembly.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

MOIS LabVIEW Software

Software developed in LabVIEW for the Modular Ocean Instrumentation System (MOIS) is provided. Two documents: MOIS User's Guide and MOIS Software Developer's Guide are included in the submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Aquantis 2.5 MW Ocean Current Generation Device - Scaled Tank Test Design and Results

Aquantis 2.5 MW Ocean Current Generation Device, Tow Tank Dynamic Rig Structural Analysis Results. This is the detailed documentation for scaled device testing in a tow tank, including models, drawings, presentations, cost of energy analysis, and structural analysis. This dataset also includes specific information on drivetrain, roller bearing, blade fabrication, mooring, and rotor characteristics.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Underwater Mapping Results for Seabotix vLBV300 Vehicle with Tritech Gemini 720i Imaging Sonar near Newport, OR

This document presents results from tests to demonstrate underwater mapping capabilities of an underwater vehicle in conditions typically found in marine renewable energy arrays. These tests were performed with a tethered Seabotix vLBV300 underwater vehicle. The vehicle is equipped with an inertial navigation system (INS) based on a Gladiator Landmark 40 IMU and Teledyne Explorer Doppler Velocity Log, as well as a Gemini 720i scanning sonar acquired from Tritech. The results presented include both indoor pool and offshore deployments. The indoor pool deployments were performed on October 7, 2016 and February 3, 2017 in Corvallis, OR. The offshore deployment was performed on April 20, 2016 off the coast of Newport, OR (44.678 degrees N, 124.109 degrees W). During the mission period, the sea state varied between 3 and 4, with an average significant wave height of 1.6 m. Data was recorded from both the INS and the sonar.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

AeroDyn V15.04: Design Tool for Wind and MHK Turbines

AeroDyn is a time-domain wind and MHK turbine aerodynamics module that can be coupled into the FAST version 8 multi-physics engineering tool to enable aero-elastic simulation of horizontal-axis wind turbines. AeroDyn V15.04 has been updated to include a cavitation check for MHK turbines, and can be driven as a standalone code to compute wind turbine aerodynamic response uncoupled from FAST. Note that while AeroDyn has been updated to v15.04, FAST v8.16 has not yet been updated and still uses AeroDyn v15.03.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Aquantis 2.5 MW Ocean Current Generation Device Design Details

Items in this submission provide the detailed design of the Aquantis Ocean Current Turbine and accompanying analysis documents, including preliminary designs, verification of design reports, CAD drawings of the hydrostatic drivetrain, a test plan and an operating conditions simulation report. This dataset also contains analysis trade off studies of fixed vs. variable pitch and 2 vs. 3 blades.

16 TIDAL AND WAVE POWER↗