MétaCan
Menu
Back to cohort
Record W2745329693 · doi:10.11159/ffhmt17.173

Natural Convection in an Enclosure: Effect of Magnetic Field Dependent Thermal Conductivity

2017· article· en· W2745329693 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNatural convectionEnclosureThermal conductivityMaterials scienceMagnetic fieldConvectionMechanicsPhysicsThermodynamicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, the natural convection heat transfer process is investigated inside an annular enclosure filled with a magnetic nanofluid (Fe 3 O 4 magnetic nanoparticles dispersed in Kerosene). A uniform magnetic field (H) is applied along the axial direction of the enclosure. Thermal conductivity (k) is considered as a function of magnetic field. A nonlinear relationship between magnetic field and thermal conductivity in the magnetic nanofluid (MNF) is assumed and interpolated. Finite element method is utilized to solve the governing equations and calculate the Nusselt number and it is presented as a function of volume fraction and magnetic field strength. The results show the significant effect of applied magnetic field on heat transfer rate, more specifically on Nu, in the enclosure when higher volume fractions of nanoparticles are used. Thermal conductivity enhancement as a result of using magnetic field can be used for various applications such as thermal energy storage in which the heat transfer needs to be accurately controlled.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.576

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 head0.014
GPT teacher head0.238
Teacher spread0.223 · 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