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Record W2473951084 · doi:10.1139/cgj-2015-0248

Flume investigation of the influence of rigid barrier deflector angle on dry granular overflow mechanisms

2016· article· en· W2473951084 on OpenAlexvenueno aff
Clarence Edward Choi, Charles Wang Wai Ng, Saoirse Robin Goodwin, L.H.D. Liu

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersHong Kong GovernmentHong Kong University of Science and Technology
KeywordsFlumeDissipationFlow (mathematics)MechanicsInclination anglePlane (geometry)Geotechnical engineeringHorizontal planeMaterials scienceGeologyEngineeringStructural engineeringGeometryPhysics

Abstract

fetched live from OpenAlex

Mass-wasting processes are often intercepted using rigid barriers, which are sometimes equipped with deflectors to prevent overspilling. Despite the engineering value of deflectors, they are currently only installed using empirical and prescriptive approaches because flow interaction mechanisms are not well understood. A 5 m flume was used to study dry granular flow deposition and overflow processes with and without deflectors of varying angles. The deflector angle was varied as 0°, 30°, 45°, and 60° with respect to the horizontal plane. For the geometric and material parameters adopted in this study, experimental results reveal that deflector angles greater than 45° develop steep ramp-like deadzones that result in effective energy dissipation as approaching flow impacts the deflector, whereas deflectors less than 45° rapidly develop shallow ramp-like deadzones, which promote high-energy overflow. Deflectors of at least 45° are required to develop upward overflow launch angles, whilst deflectors of 30° or less result in downward overflow launch angles. Upward launch angles indicate that a steep deadzone develops and effectively dissipates flow energy. An orthogonal deflector increases launch lengths by 40% compared to a bare rigid barrier.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.352

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 head0.006
GPT teacher head0.181
Teacher spread0.174 · 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 designBench or experimental
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

Citations31
Published2016
Admission routes1
Has abstractyes

Explore more

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