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Record W7125386818 · doi:10.1142/s0219581x2650002x

Thermal Performance Analysis of Magnetohydrodynamics with Carbon Nanotubes on a Stretching/Shrinking Porous Sheet

2025· article· en· W7125386818 on OpenAlexaff
U. S. Mahabaleshwar, G. M. Sachin, K. N. Sneha, T. Maranna, L. M. Perez

Bibliographic record

VenueInternational Journal of Nanoscience · 2025
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNanofluidCarbon nanotubeThermal radiationHeat transferDarcy numberHartmann numberPorous mediumMagnetohydrodynamics

Abstract

fetched live from OpenAlex

Objectives: Carbon nanotubes (CNTs) can enhance heat transfer due to their superior thermal properties. Thermal performance of magnetohydrodynamics (MHD) with CNTs is increasingly important to improve energy efficiency of nanotechnology in a vast range of industrial, pharmaceutical and energy conversion. In this study, we investigate the effects of inclined MHD and slip on viscoelastic CNT flow with radiation and heat source/sink over a stretching sheet embedded in porous media. The suction/injection through the porous medium is considered. Methods: The governing PDEs describing the flow were converted into a system of nonlinear ordinary differential equations through the application of a similarity transformation and then solved analytically. The influences of the Hartmann number, inverse Darcy number, viscoelastic, mass suction/injection effect, radiation parameter and heat source/sink parameters on velocity and temperature profiles are graphically presented and thoroughly discussed. Findings: The results show that the fluid flow increases as the Hartmann number and inverse Darcy number increase, whereas reverse effects are observed in the viscoelastic parameter and solid volume fraction. At the larger inversed Darcy number and the Hartmann number, the axial velocity for single-wall carbon nanotubes (SWCNTs) becomes larger than that of multiwall carbon nanotubes (MWCNTs). Also, the temperature profile increases as the values of the thermal radiation, heat sources, and the Hartmann number increase. The temperature of the nanofluids (NFs) with SWCNT is larger than that with MWCNT when the thermal radiation and heat sources are the same. This shows that the SWCNT NFs can improve thermal performance better. The produced velocities of NFs with MWCNT are higher than those with SWCNT, leading to lower heat convection. Applications: CNTs can enhance heat transfer due to their superior thermal properties. The thermal performance of MHD with CNTs is increasingly important to improve energy efficiency of nanotechnology in a vast range of industrial, pharmaceutical and energy conversion.

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)0.0000.000

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 head0.214
Teacher spread0.210 · 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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