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Record W2956587399 · doi:10.1108/hff-11-2018-0677

Numerical study on bi-phase coupled stress fluid in the presence of Hafnium and metallic nanoparticles over an inclined plane

2019· article· en· W2956587399 on OpenAlexaff
A. Zeeshan, R. Ellahi, Fazle Mabood, Farooq Hussain

Bibliographic record

VenueInternational Journal of Numerical Methods for Heat &amp Fluid Flow · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
Fundersnot available
KeywordsDragMaterials scienceInclined planeMechanicsStress (linguistics)Plane (geometry)Flow (mathematics)Particle (ecology)Phase (matter)HafniumNewtonian fluidParasitic dragFluid dynamicsPhysicsGeometryGeologyMetallurgy

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine the simultaneous effects of Hafnium particles and partially submerged metallic particles for the flow of bi-phase coupled stress fluid over an inclined flat plane. Design/methodology/approach An unflinching free stream flow that stretches far from the surface of the plane with the possibility of containing some partially submerged metallic particles is considered. Innovative model has been proposed and designed using Runge–Kutta–Fehlberg method. Findings The findings show that the drag force resists the couple stress fluid, whereas the Newtonian flow is supported by increasing the velocity. For both types of flows, movement of the particle is retarded gradually against the drag force coefficient. Originality/value To the best of the authors’ knowledge, this model is reported for the first time.

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 score0.006
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.377
Teacher spread0.342 · 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

Citations76
Published2019
Admission routes1
Has abstractyes

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