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Record W2248118266 · doi:10.36884/jafm.5.01.11955

Numerical Investigation of Head-on Binary Drop Collisions in a Dynamically Inert Environment

2012· article· en· W2248118266 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHead (geology)Binary numberDrop (telecommunication)MechanicsInertInert gasMaterials scienceEnvironmental scienceComputer scienceGeologyPhysicsComposite materialMathematicsGeomorphologyTelecommunicationsArithmetic

Abstract

fetched live from OpenAlex

The results of three-dimensional numerical simulations of drop collisions without the effect of a surrounding environment are presented. The numerical model is based on an Eulerian, finite-difference, Volume-of-Fluid method. Surface tension is included using the Continuum Surface Force method. Head-on collisions using equal size drops with three different fluid properties of water, mercury and tetradecane are presented. Various drop diameters ranging from 200 m to 5 mm are considered. A separation criterion based upon deformation data is found. The lower critical Weber numbers are found for mercury and water drops while tetradecane drops did not result in separation for the range of Weber numbers considered. The effect of Reynolds number is investigated and regions of permanent coalescence and separation are plotted in the Weber-Reynolds number plane. The role of viscosity and its effect on dissipation is also investigated. Finally, the validity of the assumptions made in some of the collision models is assessed.

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.

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 score0.770
Threshold uncertainty score0.554

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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