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Record W1525928228

Turbulence and drag in a high Reynolds number tidal passage targetted for in-stream tidal power

2013· article· en· W1525928228 on OpenAlex
Alex E. Hay, Justine McMillan, Richard Cheel, Douglas J. Schillinger

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2013 OCEANS - San Diego · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTurbulenceAcoustic Doppler current profilerGeologyDragBoundary layerReynolds stressAcoustic Doppler velocimetryTurbulence kinetic energyDrag coefficientCurrent meterGeodesyMechanicsPhysicsMeteorologyCurrent (fluid)Oceanography
DOInot available

Abstract

fetched live from OpenAlex

Results are presented from an investigation of turbulence and bottom drag carried out in Grand Passage, lower Bay of Fundy. Flow measurements were made using a broadband 600 kHz acoustic Doppler current profiler (ADCP) sampling at nearly 2 Hz, and two single-point sensors: an acoustic Doppler velocimeter (ADV) sampling at 1 Hz, and a time-of-flight velocity sensor (MAVS) sampling at 12 Hz. All instruments were bottommounted. The maximum depth-averaged tidal current speed was 1.6 m/s. The local bathymetry was characterized by 20 m mean water depth and ca. 0.5 m high, 8 m wavelength dunes. The ADCP was deployed to one side of the dune field; the singlepoint sensor platform was within the dune field. Due to high water clarity, the ADV correlations at moderate to high flow speeds were very low, precluding estimation of turbulence quantities. In contrast, the time-of-flight velocity data are noise free - the measurement does not require the presence of sound scatterers - and the spectra exhibit a well-defined inertial subrange. Turbulent Reynolds stress estimates from the time-offlight sensor data yield friction velocities (u*) and bottom drag coefficients (Cd) comparable to those determined from the ADCP profiles via the law-of-the-wall. The ratio of RMS vertical velocity variance to friction velocity in the time-of-flight data is close to 1.2, consistent with results obtained within the constant stress layer in the atmospheric boundary layer and in rough boundary laboratory experiments. Turbulence quantities are estimated from the (de-noised) ADCP velocity spectra via the variance method, modified here for application to a sloping seabed by taking advantage of the orientation relative to the local isobaths of the orthogonal acoustic beam pair planes. Noise levels were determined from the spectra ensemble-averaged in equal mean flow speed intervals, and are very close to the manufacturer's quoted value. When the constant stress layer was sufficiently thick - i.e. during ebb tide - the RMS turbulence intensities in the lower ADCP range bins are entirely consistent with the anisotropic relationships between Cartesian RMS turbulent velocity components and u* obtained in rough boundary wind tunnel experiments [1]; the ADCP Reynolds stresses agree with the Law-of-the-Wall shear stress estimates; and vertical profiles of Reynolds stress are very different from the linear decrease with height expected for (lower Reynolds number) straight and narrow open channel flows. During flood tide, the boundary layer was much thinner, and the lowermost ADCP bin - at 2.1 m height - was outside the constant stress layer. The pronounced asymmetries between flood and ebb (i.e. in boundary layer thickness, turbulence intensity, and (Cd) are attributed to differences in upstream bathymetry. Index Terms-turbulence, bottom stress, tidal power, broadband acoustic Doppler profiler, acoustic time-of-flight sensor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.204
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it