MétaCan
Menu
Back to cohort

Three-dimensional numerical simulation of flow around stream deflectors: The effect of obstruction angle and length

2007· article· en· W1993874597 on OpenAlexafffund
T. Haltigin, Pascale M. Biron, Michel Lapointe

Bibliographic record

VenueJournal of Hydraulic Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsFlumeDownwellingMechanicsFlow (mathematics)Upstream (networking)Maximum flow problemComputer simulationMaterials scienceEnvironmental scienceGeologyPhysicsComputer scienceMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Fish habitat rehabilitation projects often use paired stream deflectors to increase bed shear stress and maintain a deep pool. Many of these projects have been unsuccessful, mainly because of the lack of knowledge of the complex three-dimensional (3D) flow dynamics created by the deflectors. A 3D numerical model was used to simulate the flow field around stream deflectors in a laboratory flume. Predicted velocities were successfully evaluated against laboratory measurements. Deflectors ranging from contraction ratios of 0.1 -0.35 oriented to flow at angles of 30-150° were used for the simulations. Results show that the upstream dynamic pressure maximum, upstream flow separation distance, upstream downwelling extent, and total downwelling distance all increase with deflector angles. However, local minimum dynamic pressure, nose velocity amplification, downstream downwelling extent, and flow contraction width are maximized at angles closer to 90°. In general, maximum downwelling magnitudes increase for longer deflectors, with the greatest values being found for the 135° deflectors. Increasing the length of the deflectors tends to amplify most of the flow parameters. These findings have important implications for the design of paired deflectors in fish habitat rehabilitation projects where the objective is to maximize the pool size downstream of the obstructions.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.326
Teacher spread0.301 · 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 designObservational
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

Citations22
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Hydraulic ResearchSame topicFish Ecology and Management StudiesFrench-language works237,207