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Record W3176795186 · doi:10.5194/egusphere-egu21-9676

Study of turbulent mixing processes at a mesoscale confluence through aerial drone imagery and eddy-resolved modelling

2021· article· en· W3176795186 on OpenAlexaffabout
Jason Duguay, Pascale M. Biron, Thomas Buffin‐Bélanger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsVortexGeologyTurbulenceMixing (physics)DownwellingMesoscale meteorologyConfluenceMechanicsFlow (mathematics)EddyPhysicsGeometryGeophysicsUpwellingOceanography

Abstract

fetched live from OpenAlex

The large-scale turbulent structures that develop at confluences fall into three main categories: vertically orientated (Kelvin-Helmholtz) vortices, large-scale secondary flow helical cells and smaller strongly coherent streamwise orientated vortices. The causal mechanisms of each class, how they interact with one another and their respective contributions to mixing is still unclear. Our investigation emphasises the role played by the instantaneous flow field in mixing at a mesoscale confluence (Mitis-Neigette, Quebec, Canada) by complementing aerial drone observations of turbulent suspended sediment mixing processes with results from a high-resolution eddy-resolved numerical simulation. The high velocity near-surface flow of the main channel (Mitis) separates at the crest of the scour hole before downwelling upon collision with the slower tributary (Neigette). Fed by incursions of lateral momentum of the Mitis, shear generated Kelvin-Helmholtz instabilities expand as they advect along the mixing-interface. As the instabilities shed, water from the deeper Neigette passes underneath the fast, over-topping Mitis, causing a large portion of the Neigette’s discharge to cross under the mixing-interface in a short distance. The remaining flow of the tributary crosses over inside large-scale lateral incursions farther downstream. The downwelling Mitis, upwelling Neigette and recirculatory cell interact to generate coherent streamwise vortical structures which assist in rapidly mixing the waters of the two rivers in the vicinity of the mixing-interface. Evidence of large-scale helical cells were not observed in the flow field. Results suggest that flow interaction with bathymetry, and both vertical and streamwise orientated coherent turbulent structures play important roles in mixing at confluences. Our findings strongly suggest that investigating mixing at confluences cannot be based solely on mean flow field variables as this approach can be misleading. Visualization of a confluence’s mixing processes as revealed by suspended sediment gradients captured in aerial drone imagery complemented with eddy-resolved numerical modelling of the underlying flow is a promising means to gain insights on the role of large-scale turbulent structures on mixing at confluences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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