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Record W3084248471 · doi:10.1088/1402-4896/abb5c7

Two-phase permeable non-Newtonian cross-nanomaterial flow with Arrhenius energy and entropy generation: Darcy-Forchheimer model

2020· article· en· W3084248471 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

VenuePhysica Scripta · 2020
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
Fundersnot available
KeywordsBejan numberNanofluidMechanicsEckert numberDarcy numberStreamlines, streaklines, and pathlinesBrinkman numberMaterials scienceInertiaHeat transferCompressibilityThermodynamicsPhysicsClassical mechanicsReynolds numberNusselt numberNatural convection

Abstract

fetched live from OpenAlex

Abstract The present work concentrates on two-dimensional steady incompressible flow of a non-Newtonian cross nanofluid past a linear stretching/shrinking sheet with a magnetic field in Darcy-Forchheimer porous regime. The entropy theory with Arrhenius energy is incorporated into the study. The shooting method is employed to obtain numerical solutions of the transformed system of non-linear equations. The influence of the governing parameters on the non-dimensional velocity, temperature, micro-rotation, drag force, heat and mass transfer rates, rate of entropy generation, Bejan number, streamlines and finally isotherms are incorporated. The significant outcomes of the current investigation are that increment in suction parameter uplifts flow velocity, temperature and concentration while injection is petered out them. Bejan number augments due to increment in inertia coefficient while reduces due to magnetic strength and Eckert number.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.233
Teacher spread0.214 · 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