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Record W2008421901 · doi:10.1103/physreve.71.066311

Convective instability of magnetic fluids under alternating magnetic fields

2005· article· en· W2008421901 on OpenAlexafffund
P. N. Kaloni, J. X. Lou

Bibliographic record

VenuePhysical Review E · 2005
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInstabilityFloquet theoryMagnetic fieldBoundary value problemPartial differential equationEigenvalues and eigenvectorsVortexRotation (mathematics)MechanicsPhysicsMathematical analysisMathematicsClassical mechanicsConvectionSpectral methodNonlinear systemGeometry

Abstract

fetched live from OpenAlex

A theoretical investigation of the convective instability problem in the thin horizontal layer of a magnetic fluid heated from below and under alternating magnetic fields is carried out. Both the quasistationary model and the model with internal rotation with vortex viscosity are considered. Floquet theory is used for discussing the existence and stability boundaries of the differential equations with periodic coefficients. The Chebyshev pseudospectral method is employed to discretize the partial differential equation, and QZ algorithm is used for solving the eigenvalue problem. For quasistationary model, both free-free and rigid-rigid boundary cases are considered, whereas for the model with internal rotation only rigid-rigid boundary condition is studied. The effect of frequency variations on the stability are considered in all the cases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.013
GPT teacher head0.267
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2005
Admission routes2
Has abstractyes

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