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Record W4412914459 · doi:10.1063/5.0278669

Nonlinear spin-up flow in magnetic colloidal suspensions under high-frequency rotating magnetic fields

2025· article· en· W4412914459 on OpenAlexafffund
Zakaria Larbi, Faı̈çal Larachi, Seyed Mohammad Taghavi, Abdelwahid Azzi

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsMagnetic fieldNonlinear systemCondensed matter physicsRotating magnetic fieldFlow (mathematics)MechanicsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the spin-up flow dynamics of dilute ferrofluids under a rotating magnetic field with a focus on high frequencies. Three models are compared: the first- and second-order approximations of the orientational probability density function and the full Smoluchowski equation. The first-order approximation, while computationally simple, has not been tested for its validity in high-frequency regimes, nor has it been compared with experimental data in such regimes. A parametric study explores the effects of magnetic field intensity, nanoparticle concentration, and particle diameter in the clusterless regime. Two types of ferrofluids—water-based and oil-based—are considered, each with different behaviors at high frequencies. The results show a transition in spin-up behavior from increasing torque and velocity with frequency to a regime where torque flattening causes flow attenuation. While the full Smoluchowski model captures additional nonlinear effects, the second-order approximation offers a computationally efficient and accurate alternative. It provides a good balance between accuracy and efficiency, especially when additional phenomena, such as the Kelvin body force, come into play.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.650

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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