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Record W4311817918 · doi:10.1016/j.rineng.2022.100838

Investigation of second grade viscoelastic non-Newtonian nanofluid flow on the curve stretching surface in presence of MHD

2022· article· en· W4311817918 on OpenAlexaff
Kh. Hosseinzadeh, Moloud Mardani, M. Paikar, A. Hasibi, T. Tavangar, Mohammad Nimafar, Davood Domiri Ganji, Mohammad Behshad Shafii

Bibliographic record

VenueResults in Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsNanofluidMechanicsNon-Newtonian fluidNewtonian fluidMass transferHeat transferViscoelasticityThermodynamicsMaterials scienceDragMagnetohydrodynamicsFluid dynamicsChurchill–Bernstein equationJoule heatingSchmidt numberNusselt numberPhysicsReynolds numberComposite material

Abstract

fetched live from OpenAlex

MHD extended flow of second-grade viscoelastic nanofluid is studied in two-dimensional mode on a curve stretching surface. Items like Joule heating and curvature parameter have been applied to inspect the heat transfer and mass transfer rates. To produce the nonlinear ordinary differential system, appropriate transformations are used. The quasi-linearization method is utilized to derive the solutions numerically. The impact of specific variables on the properties of fluid has been studied. Conclusions indicate that the increase in Schmidt number halted the fluid concentration, whereas temperature grew because of an increment in radiation parameter. Viscous fluid velocity and concentration are reduced faster in comparison to viscoelastic fluid. The surface drag force is an increasing function of the non-Newtonian fluid. When values of Nt and Nb are increased, the heat transfer rate of second-grade fluid increases compared to Newtonian fluid. Schmidt number and chemical reaction parameters highly affect the mass transfer of second-grade fluid compared to Newtonian fluid.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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

Citations122
Published2022
Admission routes1
Has abstractyes

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