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Record W2075361755 · doi:10.1063/1.3703283

Investigating the motion of particles in an ultrasonic acoustic wave field using PIV/PTV

2012· article· en· W2075361755 on OpenAlexaff
David S. Nobes, Alireza Setayeshgar, Michael Lipsett, Charles Robert Koch

Bibliographic record

VenueAIP conference proceedings · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcoustic radiation forceMagnetosphere particle motionParticle tracking velocimetryParticle (ecology)WavelengthPhysicsDragBuoyancyParticle image velocimetryMechanicsAcoustic radiationAcoustic waveOpticsRadiation pressureNeutral buoyancyParticle velocityAcousticsMaterials scienceRadiationUltrasoundMagnetic fieldGeology

Abstract

fetched live from OpenAlex

The influence of a multi-wavelength acoustic standing wave field on the motion of micron-sized particles is experimentally investigated using particle image velocimetry/particle tracking velocimetry (PIV/PTV) to examine existing theories describing the radiation force on particles. An ultrasonic acoustic wave is introduced into a column chamber containing a mixture of distilled water and a disperse population of spherical particles. In this system the acoustic field is aligned with gravity to form horizontal bands of particles, which are also influenced by buoyancy and drag forces. Accounting for these forces with the acoustic radiation pressure, the motion of an individual particle is modeled. There is a good agreement between the pattern of the particles motion in experimental results and the predicted single particle motion; however, due to the concentration of particles in the experiment, a difference is observed in the maximum value of the velocity of the particles in the experiment and in the single particle model.

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 score0.236
Threshold uncertainty score0.359

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 head0.054
GPT teacher head0.249
Teacher spread0.194 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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