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Record W4413367885 · doi:10.18280/mmep.120723

Combined Effect of Heat Source and Pressure in an Unsteady MHD Flow with Chemical Reaction Through an Infinite Oscillating Vertical Porous Surface

2025· article· en· W4413367885 on OpenAlexvenueno aff
Devi Annadurai, Sivakami Lakshminathan

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2025
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetohydrodynamicsMechanicsPorosityFlow (mathematics)Materials scienceSurface (topology)Porous mediumThermodynamicsPhysicsComposite materialGeometryMagnetic fieldMathematics

Abstract

fetched live from OpenAlex

In this study, heat source, pressure gradient, and chemical reaction are investigated in the analytical solution of an unsteady magnetohydrodynamic fluid with heat and mass transfer along a vertical oscillatory surface under the porous medium.This study also investigates the temperature and concentration along with velocity, closer to the surface over time t>0.The Laplace transformation procedure has been implemented for resolving the governing terms of the flow, yielding solutions for temperature, concentration, and velocity.Significant factors that affect velocity, temperature, and concentration-including the Schmidt number, thermal Grashof number, and solutal Grashof number are analysed and results are illustrated graphically using MATLAB.The results obtained here are consistent with the previously published work.From the current investigation, it is reported that velocity decreases with a rise in Hartmann's number (M).As the chemical reaction Kr rises, the velocity has a retarding effect, whereas the concentration increases, and while the heat source (Qs) increases corresponding velocity and temperature both are increasing.As pressure gradient P increases, velocity decreases gradually.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0000.001
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 head0.009
GPT teacher head0.203
Teacher spread0.193 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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