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Record W3089362396 · doi:10.1080/01430750.2020.1831593

Effect of nonlinear radiation on 3D unsteady MHD stagnancy flow of Fe <sub>3</sub> O <sub>4</sub> /graphene–water hybrid nanofluid

2020· article· en· W3089362396 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.

Bibliographic record

VenueInternational Journal of Ambient Energy · 2020
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
FundersUniversity Grants Commission
KeywordsNanofluidMagnetohydrodynamic driveNusselt numberMechanicsStreamlines, streaklines, and pathlinesMaterials scienceMagnetohydrodynamicsFlow (mathematics)Nonlinear systemThermal radiationHeat transferThermodynamicsPhysicsReynolds numberPlasmaTurbulence

Abstract

fetched live from OpenAlex

Three-dimensional unsteady magnetohydrodynamic stagnancy flow of hybrid nanoliquid, with nonlinear radiation and uneven heat rise/sag, is studied hypothetically. We considered Fe3O4/graphene nanoparticles embedded in water. The physical problem is modelled mathematically and resolved using Runge–Kutta Fourth order with a shooting procedure. Influences of pertinent parameters on the flow and energy transport are noted numerically and graphically. Moreover, the wall friction and the local Nusselt number are computed and a comparative analysis of nano/hybrid nanofluids is performed with the help of streamlines and isotherms. It is found that the drive and energy transport of nano/hybrid nanofluid is highly influenced by the variation in the particle volume fraction as well as unsteadiness factor. Also, the average temperature of nanofluid in the saddle stagnation region is higher than that of hybrid nanofluid.

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 categoriesMeta-epidemiology (narrow)
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 score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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