Free convective heat transfer flow in a glass bead porous medium varying permeability and sinusoidal wall temperature
Bibliographic record
Abstract
Abstract This study examines the effects of varying permeability and sinusoidal wall temperature fluctuations on the temporal heat transfer flow driven by natural convection inside a rectangular enclosure filled with a glass bead porous medium under local thermal nonequilibrium conditions for both the working fluid and the porous medium. The fluid’s thermal conductivity is assumed to be variable, and a Darcy–Brinkman–Forchheimer model is used to describe the fluid flow. The Galerkin-type finite element method simulates the constitutive equations governing the flow and heat transfer. The simulation explores the influence of different model parameters on the flow and thermal fields. The results indicate that at a steady state ( $$\tau = 1.0$$ τ = 1.0 ), when glass bead diameter Dp increased from 0.01 to 1.0, the values of $$\overline{{{\text{Nu}}_{{\text{f}}} }}$$ Nu f ¯ and $$\overline{{{\text{Nu}}_{{\text{s}}} }}$$ Nu s ¯ decreased by 48.8% and 26%, respectively. Besides, the value of the Nusselt number for the fluid increased by 280.61%, whereas the Nusselt number for the solid increased by 266.55% with the increase of the wave frequency n from 1 to 4. Furthermore, these physical quantities increased by 629.71% and 91.405% when the wave amplitude B rose from 0.1 to 1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".