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Record W2253729711 · doi:10.2298/tsci150411163g

Oblique stagnation point flow of a non-Newtonian nanofluid over stretching surface with radiation: A numerical study

2015· article· en· W2253729711 on OpenAlexaff
Abuzar Ghaffari, Tariq Javed, F. Labropulu

Bibliographic record

VenueThermal Science · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBiot numberNanofluidMechanicsStagnation pointBoundary layerMaterials scienceHeat transferStagnation temperatureThermal radiationFlow (mathematics)Partial differential equationBoundary value problemPhysicsClassical mechanicsThermodynamicsMathematical analysisMathematics

Abstract

fetched live from OpenAlex

In this study, we discussed the enhancement of thermal conductivity of elasticoviscous fluid filled with nanoparticles, due to the implementation of radiation and convective boundary condition. The flow is considered impinging obliquely in the region of oblique stagnation point on the stretching surface. The obtained governing partial differential equations are transformed into a system of ordinary differential equations by employing a suitable transformation. The solution of the resulting equations is computed numerically using Chebyshev spectral newton iterative scheme. An excellent agreement with the results available in literature is obtained and shown through tables. The effects of involving parameters on the fluid flow and heat transfer are observed and shown through graphs. It is importantly noted that the larger values of Biot number imply the enhancement in heat transfer, thermal boundary layer thickness, and concentration boundary layer thickness.

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 score0.004
Threshold uncertainty score0.007

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 studies0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.224 · 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

Citations34
Published2015
Admission routes1
Has abstractyes

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