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Record W7133064758

Planar PIV in fibre laden flows

2003· dissertation· W7133064758 on OpenAlexfundno aff
Homer Jermain Henry

Bibliographic record

VenueTSpace · 2003
Typedissertation
Language
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsParticle image velocimetryVorticityWakeBoundary layerViscositySquare (algebra)Flow (mathematics)VortexShear (geology)Plane (geometry)
DOInot available

Abstract

fetched live from OpenAlex

Two dimensional shear flows of water, oil and oil-fibre suspensions of 0.01%, 0.025% and 0.05% mass consistency were examined using particle image velocimetry (PIV). A square obstacle that block 50% of the flow area in a rectangular flow cell induced shear. PIV measurements were taken at various positions on the plane of symmetry, adjacent to the square and in the wake region. The data shows that the higher viscosity of the oil results in a much larger boundary layer thickness than that of water and reattachment to the square that does not occur with water. The data also indicates that fibre loading causes a reduction in the intensity of vorticity and velocity fluctuations at the fibre length scale. Two dimensional shear flows of water, oil and oil-fibre suspensions of 0.01%, 0.025% and 0.05% mass consistency were examined using particle image velocimetry (PIV). A square obstacle that block 50% of the flow area in a rectangular flow cell induced shear. PIV measurements were taken at various positions on the plane of symmetry, adjacent to the square and in the wake region. The data shows that the higher viscosity of the oil results in a much larger boundary layer thickness than that of water and reattachment to the square that does not occur with water. The data also indicates that fibre loading causes a reduction in the intensity of vorticity and velocity fluctuations at the fibre length scale.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.266
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2003
Admission routes1
Has abstractyes

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