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Record W2672488506 · doi:10.1299/jsmemnm.2012.4.13

G1-1-3 Study on the turbulence measurement of a supersonic micro jet using MTV

2012· article· en· W2672488506 on OpenAlexaboutno aff
Katsuhito Mii, Takayuki Sakurai, Taro HANDA

Bibliographic record

VenueThe Proceedings of the Symposium on Micro-Nano Science and Technology · 2012
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
Fundersnot available
KeywordsJet (fluid)Supersonic speedPhysicsTurbulenceOpticsFlappingTurbulence kinetic energyNozzleRoot mean squareVelocimetryMechanics

Abstract

fetched live from OpenAlex

The molecular tagging velocimetry (MTV) is applied to measure the velocity fluctuation in a supersonic micro jet issuing from the two-dimensional Laval nozzle whose height is 500μm at the exit. This velocimetry uses the forth harmonic of a Nd:YAG laser (266nm) as a light source to excite the acetone molecules seeded in the flow, and fluorescence images are captured by a CCD camera 300ns after the laser illumination. The distribution of root-mean-square (RMS) of velocity fluctuation is obtained from these images. The distribution represents well the characteristics of the turbulent micro jet although unreasonable RMS values due to low fluorescence intensity are recognized around the jet. The distribution of single-time two-point spatial correlations is also obtained from the measured velocity fluctuations and this distribution reveals that the micro jet fluctuates in a flapping mode.

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)0.000
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.036
GPT teacher head0.269
Teacher spread0.232 · 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
Published2012
Admission routes1
Has abstractyes

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