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8 results for “Bottom currents”
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
Data for: Strong bottom currents in large, deep Lake Geneva generated by higher vertical-mode Poincaré waves
<p>Combining entire summer season current and temperature observations and 3D numerical modeling, we demonstrate that previously undetected vertical mode-two and vertical mode-three Poincaré waves in 309-meter deep Lake Geneva (Switzerland/France) generate strong bottom-boundary layer currents at 300-m depth. The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs), vertical thermistor lines, and the corresponding 3D modeling results. The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, <a href="http://mitgcm.org/">http://mitgcm.org/</a>, <a href="https://doi.org/10.1029/96JC02775">https://doi.org/10.1029/96JC02775</a>). The main MITgcm model configuration files are available online at <a href="https://doi.org/10.5281/zenodo.13144189">https://doi.org/10.5281/zenodo.13144189</a>.</p> <p>The related scientific publication can be found at <a href="https://doi.org/10.1038/s43247-024-01653-8">https://doi.org/10.1038/s43247-024-01653-8</a></p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: VelocityProfilies_2018_Musumarra
<p>The experimental campaign was devoted to study the velocity profile inside the small scale flume for several bottom configurations. In particular, the following configurations were considered: thin sand (D<sub>50</sub>=0.25 mm); coarse sand (D<sub>50</sub>=0.56 mm); mixed sand: 10% coarse sand and 90% thin sand; mixed sand: 20% coarse sand and 80% thin sand; mixed sand: 30% coarse sand and 70% thin sand; mixed sand: 40% coarse sand and 60% thin sand; small gravel (diameter between 3 and 5 mm); gravel (diameter between 9 and 14 mm); small gravel and thin sand; gravel and thin sand.</p>
Data: Near-bottom currents at Station M in the abyssal Northeast Pacific
<p>Current meter data collected by the Monterey Bay Aquarium Research Institute (MBARI) at Station M, located on the abyssal plain 220 km offshore of central California. Collected near the seabed at ~4000 m depth from October 2014–October 2018. Data and methods are described in the publication:</p> <p>Connolly, T. P., P. R. McGill, R. G. Henthorn, D. A. Burrier, C. Michaud, Near-bottom currents at Station M in the abyssal Northeast Pacific, <em>submitted to Deep Sea Research II</em></p> <p><strong>Data files</strong></p> <p>Each .zip folder contains a readme.txt file describing the data files.</p> <p><em>ADCP.zip</em> - contains data from a one-year Acoustic Doppler Current Profiler (ADCP) deployment. Includes the binary file uploaded from the instrument and a text file created using RDI tools.</p> <p><em>ADCP_netcdf.zip</em> - contains processed ADCP data in NetCDF format.</p> <p><em>Rover_merged.zip</em> - contains merged data files from four years of benthic rover current meter deployments, in Excel .xlsx format and comma-separated value .csv format.</p> <p><strong>Analysis code</strong></p> <p>Analysis code is available at <a href="https://github.com/tompc35/station-m-currents">https://github.com/tompc35/station-m-currents</a></p>
Data from: The effect of uncertain bottom friction on estimates of tidal current power
Uncertainty affects estimates of the power potential of tidal currents, resulting in large ranges in values reported for a given site, such as the Pentland Firth, UK. We examine the role of bottom friction, one of the most important sources of uncertainty. We do so by using perturbation methods to find the leading-order effect of bottom friction uncertainty in theoretical models by Garrett & Cummins (2005), Vennell (2010), and Garrett & Cummins (2013), which consider quasi-steady flow in a channel completely spanned by tidal turbines, a similar channel but retaining the inertial term, and a circular turbine farm in laterally unconfined flow. We find that bottom friction uncertainty acts to increase estimates of expected power in a fully-spanned channel, but generally has the reverse effect in laterally unconfined farms. The optimal number of turbines, accounting for bottom friction uncertainty, is lower for a fully-spanned channel and higher in laterally unconfined farms. We estimate the typical magnitude of bottom friction uncertainty, which suggests that the effect on estimates of expected power lies in the range −5 to +30%, but is probably small for deep channels such as the Pentland Firth (5-10%). In such a channel, the uncertainty in power estimates due to bottom friction uncertainty remains considerable, and we estimate a relative standard derivation of 30%, increasing to 50% for small channels.
Data from: The effect of uncertain bottom friction on estimates of tidal current power
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Flocculation results from modeling framework (current bottom boundary layer case)
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