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Record W4360820526 · doi:10.1063/5.0139855

Comparing classical electrodynamic theories predicting deformation of a water droplet in a tightly focused Gaussian beam

2023· article· en· W4360820526 on OpenAlexafffund
Cael Warner, Chun-Sheng Wang, Kenneth J. Chau

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsLattice Boltzmann methodsKinetic energyMechanicsMomentum (technical analysis)Classical mechanicsAccelerationGaussianImpulse (physics)Computational physicsStatistical physics

Abstract

fetched live from OpenAlex

Optical forces are used to accelerate and trap water droplets in applications such as remote spectroscopy and noninvasive surgery. However, the microscopic deformation of droplets is difficult to predict. In this work, the local electrodynamic impulse imparted by a focused laser beam to a water droplet is numerically modeled via a simulation that invokes intensive conservation of electrodynamic and kinetic momentum. Electrodynamic momentum is modeled locally using a D3Q7 electrodynamic lattice-Boltzmann method, and kinetic momentum is modeled locally using a multi-phase D3Q27 weighted-orthogonal lattice-Boltzmann method. Six different electrodynamic theories are implemented in the simulation domain predicting three unique types of droplet dynamics driven by differences in the direction and distribution of force density. The unique water droplet morphology affects the center-of-mass acceleration of the droplet. This study suggests that empirical measurement of the light-driven acceleration of a droplet may help to validate a single electrodynamic theory.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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