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Record W2055978595 · doi:10.1139/p07-166

Free-forced convective boundary-layer flow of a biomagnetic fluid under the action of a localized magnetic field

2008· article· en· W2055978595 on OpenAlexvenueno aff
N. G. Kafoussias, E. E. Tzirtzilakis, A. Raptis

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsLaminar flowMagnetic fieldMechanicsBoundary value problemBoundary layerViscosityDimensionless quantityFluid dynamicsFlow (mathematics)Blasius boundary layerClassical mechanicsBoundary layer thicknessThermodynamics

Abstract

fetched live from OpenAlex

The problem of the two-dimensional steady and laminar free-forced convective boundary-layer flow of a biomagnetic fluid over a semi-infinite vertical plate, under the action of a localized magnetic field, is numerically studied. The dynamic viscosity of the biomagnetic fluid as well as its thermal conductivity is considered to be temperature-dependent whereas the magnetization of the fluid varies linearly with the magnetic field strength. The numerical solution of the coupled and nonlinear system of partial differential equations (resulting after the introduction of appropriate nondimensional variables) with boundary conditions describing the problem under consideration, is obtained by an efficient numerical technique based on the common finite difference method. Numerical calculations were carried out for the case of blood (Pr = 21) for different values of the dimensionless parameters entering into the problem, especially for the magnetic parameter Mn and the viscosity–temperature parameter Θ r . The analysis of the obtained results, presented in figures, shows that the flow field is influenced by the application of the magnetic field, which could be interesting for medical and bioengineering applications. PACS Nos.: 44.20.+b, 44.25.+f, 44.27.+g, 47.15.Cb, 47.65.Cb, 47.63.–b, 47.90.+a

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.397

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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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