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Record W2578324533 · doi:10.1139/cjp-2016-0749

Hydromagnetic flow and heat transfer with various nanoparticle additives past a wedge with high order velocity slip and temperature jump

2017· article· en· W2578324533 on OpenAlexvenueno aff
Qianfang Liu, Jing Zhu, Bandar Bin-Mohsin, Liancun Zheng

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersKing Saud UniversityDeanship of Scientific Research, King Saud UniversityNational Natural Science Foundation of China
KeywordsNanofluidMechanicsTemperature jumpSlip (aerodynamics)Heat transferJumpPhysicsHomotopy analysis methodWedge (geometry)Brownian motionThermodynamicsClassical mechanicsOptics

Abstract

fetched live from OpenAlex

Nanofluid slip flow with distinct solid particles past a wedge with convective surface and high order slip is discussed in this paper. The wedge model is modified by considering the effects of Brownian motion and thermophphoresis together with the high order velocity slip and temperature jump. In this study, the governing fundamental equations are first transformed into third-order ordinary differential equations and solved by using the homotopy analysis method (HAM). Through error analysis and comparison with previous research, the effectiveness of HAM is ascertained, and the crucial influence of nanoparticles and high-order slip on the fluid skin-friction coefficient and heat transfer coefficient is analyed. Thermophphoresis parameter and suction/injection parameter are found to cause an increase in velocity and temperature. The rate of heat transfer in the Cu–water nanofluid is found to be higher than the others.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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