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Record W3177386608 · doi:10.3390/w13131844

Velocity Field and Turbulence Structure around Spur Dikes with Different Angles of Orientation under Ice Covered Flow Conditions

2021· article· en· W3177386608 on OpenAlexaff
Rahim Jafari, Jueyi Sui

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsDikeTurbulenceGeologySpurTurbulence kinetic energyFlow (mathematics)Surface finishReynolds stressOrientation (vector space)Flow velocityMechanicsGeometryGeotechnical engineeringMaterials sciencePetrologyPhysics

Abstract

fetched live from OpenAlex

Spur dikes are well-known structures that are widely used in rivers and coastal regions. Depending on their types, sizes, and orientation angles, spur dikes can substantially change flow characteristics. Results of previous studies indicate that the presence of an ice cover in rivers can cause complicated flow structures. The present experimental study investigates velocity fields and turbulence structures in the vicinity of spur dikes under ice cover with different roughness coefficients. The spur dikes were set up at the following three angles of orientation, 90°, 60°, and 45°. Our results show that the strongest velocity fluctuation occurs immediately above the scour hole surface and very close to the dike tip. The increase in the dike angle toward upstream, the velocity component values increase, leads to a larger scour hole. Results show that an increase in dike angle of each 10° (from 45° to 90°) increases the scour depth between 5% and 10%, depending on flow conditions. Furthermore, the increase in the cover roughness coefficient and the blockage ratio of a spur dike leads to a further increase in turbulence kinetic energy and 3D velocity components values. The findings of this study imply that the appearance of an ice cover can increase turbulence intensities up to nearly 30%.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.201
Teacher spread0.196 · 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.

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

Citations43
Published2021
Admission routes1
Has abstractyes

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