Darcy Forchheimer electromagnetic stretched flow of carbon nanotubes over an inclined cylinder: Entropy optimization and quartic chemical reaction
Bibliographic record
Abstract
Carbon nanotubes (CNTs) are characterized with exceptional electrical, thermal, mechanical, chemical, and optical properties (e.g., electrical conductivity, large specific surface area, high thermal conductivity, high hardness and stiffness, light weight, special electronic structure, high aspect ratio and chemical stability, and low specific gravity). Because of such outstanding properties, CNTs are being considered as prime candidate materials in multidisciplinary fields comprising of automotive, material science, aerospace, optical, electrical, biomedical, and energy conversion for nanoscale applications. In view of such advantages, electromagnetic influence on the Darcy Forchheimer flow of single‐walled CNT (SWCNT)/multi‐walled CNT (MWCNT) nanomaterials over an inclined‐extended cylinder subject to quartic chemical reactions has been explored in the present study to improve the performance of existing heat transfer systems. The heat transportation model is enriched with nonlinear thermal radiation, dissipation, and Ohmic heating. This article is more specific about improving the efficiency of thermal‐flow systems through entropy minimization. The dimensionless nonlinear PDEs are solved via Runge–Kutta–Fehlberg approach with shooting technique. The outcome of our investigation reveals that curvature parameter augments the flow field and rate of heat and mass transfer from the cylindrical and flat surfaces. Greater electromagnetic influence favors the flow and viscous drag of SWCNT/MWCNT‐water nanofluids and rate of heat transportation from the extended cylindrical surface. Augmented volume fraction of solid nanoparticles upsurges the entropy generation and Bejan numbers appreciably. The rate of heat transportation from the extended cylindrical surface for MWCNT nanofluid is greater than that of SWCNT nanofluid.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".