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Rate of Particle Depletion via Coagulation in Isotropic Turbulence

2025· preprint· en· W4411253656 on OpenAlexaff
Amir R Moayed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTurbulenceIsotropyCoagulationParticle (ecology)MechanicsPhysicsStatistical physicsGeologyOpticsPsychology

Abstract

fetched live from OpenAlex

We revisit the turbulent-coagulation fluctuation model originally proposed by Koch & Pope (2002) and extend it to regimes of elevated particle concentration and high Taylor-scale Reynolds number (𝑅𝑒 #). By retaining first-order fluctuations in both singlet concentration and shear rate-modeled as coupled Ornstein-Uhlenbeck processeswe derive a closed-form analytical expression for the rate ratio, incorporating the full shear-concentration correlation. Our model reduces to the Koch-Pope result in the dilute limit but reveals a power-law enhancement in collision rates at higher 𝑅𝑒 # and particle volume fractions due to non-Gaussian concentration intermittency. These analytical predictions are validated by stochastic simulations supported by DNS, which recover the classical Koch-Pope behavior at low concentrations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.527

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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