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Record W4386416753 · doi:10.1142/s0129183124500487

Numerical solution of Lock-exchange flow in a curved channel with an obstacle

2023· article· en· W4386416753 on OpenAlexaff
Javad Mohammadi, Bahareh Pirzadeh, Gholamreza Azizyan, Azam Abdollahi

Bibliographic record

VenueInternational Journal of Modern Physics C · 2023
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsLakehead University
Fundersnot available
KeywordsObstacleMechanicsFlow (mathematics)Channel (broadcasting)RADIUSCurrent (fluid)Open-channel flowLock (firearm)PhysicsGeologyGeometryComputer scienceMathematicsEngineeringTelecommunicationsStructural engineeringGeography

Abstract

fetched live from OpenAlex

Gravity currents modify their flow characteristics in the presence of an obstacle. Also, the flow path to the dam reservoirs is not always direct. Since no studies have addressed the feedback between the hydrodynamics of a gravity current in a curved channel and the location effects of the obstacle, in this research, a Lock-exchange density current flows in a 120[Formula: see text] bending channel. The numerical simulation has been performed using OpenFOAM software. The models include no-obstacle curved channel and a curved channel with an obstacle in different positions concerning the increased radius of the curved channel. Results indicated that the obstacle directed the concentration towards the banks, with its maximum value tending from the outer to the inner bank, especially in the tail. The tail longitudinal velocity maximized near the channel bed in areas far from the obstacle, and in the outer bank in areas near it. The secondary flow reduces its lowest and most different pattern observed around the obstacle. In displacing the latter, if the front has at a certain distance, the secondary flow does not change much, but if it has at the channel end, the post-obstacle secondary flow would increase as the obstacle neared the Lock.

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 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 score0.152
Threshold uncertainty score0.294

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)0.0000.000

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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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
Published2023
Admission routes1
Has abstractyes

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