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Record W4392361825 · doi:10.24271/psr.2024.416203.1389

THERMAL RADIATION EFFECTS ON MHD CASSON AND MAXWELL NANOFLUIDS OVER A POROUS STRETCHING SURFACE

2024· article· en· W4392361825 on OpenAlexaff
Tagallamudi Srinivasa Rao, Matam Mohan Babu, Ramesh Bojja, Naga Santoshi P.

Bibliographic record

Venuepasser · 2024
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsNanofluidMagnetohydrodynamicsThermal radiationMaterials scienceSurface (topology)ThermalPorosityRadiationPorous mediumMechanicsThermodynamicsPhysicsComposite materialOpticsMagnetic fieldGeometryMathematics

Abstract

fetched live from OpenAlex

The main goal of the work is to investigate to the influence the magnetohydrodynamic slip flow through a nonlinear porous stretching surface's upper Maxwell Casson convected nanofluid boundary layer flow was considered. The governing partial differential equations are transformed into nonlinear ordinary differential equations using the proper similarity transformations. The Shooting method was utilized to achieve the numerical solution of the updated equations utilizing the Runge-Kutta-Fehlberg approach. A wide range of essential fluid characteristics were thoroughly examined, including the Schmidt number, magnetic parameter, temperature slip parameter, concentration slip parameter, velocity, and nonlinear stretching parameter. Using graphs and tables, the impacts on temperature, concentration, and velocity were examined and reported. The investigation included calculating and thoroughly debating the skin friction coefficient, local Sherwood numbers, and local Nusselt numbers.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.600

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 communication0.0000.000
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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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