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Record W2153395685 · doi:10.1115/fedsm2002-31354

Turbulence Measurements Within a Cyclic Flow and Analysis in the Momentum Equation

2002· article· en· W2153395685 on OpenAlexaff
Eugene Suk, Daniel Fetter, Pierre E. Sullivan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrankTurbulenceParticle image velocimetryMechanicsStandard deviationPhysicsInletFlow (mathematics)OpticsMathematicsStatisticsClassical mechanicsEngineeringMotion (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

Particle Image Velocimetry (PIV) measurements were performed within an optical water analog engine. A unique triggering and data collection system was developed to allow a CCD camera to acquire two consecutive image frames at predetermined crank angles. The water analog engine operated at 15 RPM and had a square cross-section with two circular valved inlets. Measurements were made throughout an entire cycle to determine mean and turbulence statistics and results at 60 crank angle degree are discussed in this paper. Different averaging techniques were used and results between the techniques were compared to provide a number of statistical quantities having large discrepancies in scales and distributions. A study of the equations of motion showed that different averaging techniques results in differing physical interpretations of the flow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.058
GPT teacher head0.210
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2002
Admission routes1
Has abstractyes

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