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Record W4312320911 · doi:10.1121/10.0015856

Acoustic tomography measurement and large-eddy simulation of macroturbulence in high flow tidal channels

2022· article· en· W4312320911 on OpenAlexaff
Alex E. Hay, Len Zedel, Angus Creech, Mahdi Razaz, Kiyosi Kawanisi

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsMemorial University of NewfoundlandDalhousie University
Fundersnot available
KeywordsTurbulenceTomographyAcousticsDoppler effectPhysicsGeologyChannel (broadcasting)Acoustic Doppler velocimetryFlow (mathematics)OpticsMechanicsTelecommunicationsComputer scienceBlood flow

Abstract

fetched live from OpenAlex

Velocity spectra from a pilot tomography experiment in Grand Passage, Nova Scotia, exhibit the structure at 10 m to several 100 m scales similar to that of the spectra of vertically integrated velocity registered by bottom-mounted acoustic Doppler profilers. The experiment was motivated in part by the need for flow and turbulence measurement at sites being targeted for in-stream tidal power development, and by the potential of acoustic tomography for providing this information from shore-based locations in near real time. The measurements were made using a single pair of acoustic transceivers operating at 7 kHz and separated by 1.5 km at an oblique angle to the channel axis. Limited to a single cross-channel path, the experiment provided no information on the spatial structure of the turbulence. In order to investigate this structure, virtual tomographic experiments were carried out by propagating pulses through with meter-scale resolution velocity fields from a non-hydrostatic large-eddy simulation (LES) model of flow in Grand Passage. Results from these virtual experiments are presented, and their implications are discussed in relation to the potential of multi-transceiver tomography for macro-turbulence measurement and the role of horizontal shear in macro-turbulence production in coastal environments.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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