Natural convection and entropy generation inside a square chamber divided by a corrugated porous partition
Bibliographic record
Abstract
A thorough investigation of free convection and entropy generation occurring inside a differentially heated square chamber that is filled with water and divided by a water-saturated, corrugated porous partition is performed in this numerical study. The governing mathematical equations, that describe the flow and heat transfer phenomena for fluid and porous domains, are the Navier-Stokes (modified for the porous domain using the Darcy-Brinkman-Forchheimer model) and thermal energy equations. Those systems of equations are solved using the Galerkin finite element analysis. Parametric changes are carried out for different positions, thicknesses, amplitudes, and frequencies of corrugation of the porous partition, and the corresponding results are quantitatively presented in terms of average Nusselt number along the heated wall and total entropy generation of the entire chamber with increasing Rayleigh number (10 3 ≤ Ra ≤ 10 7 ). Corresponding variations are qualitatively visualized in terms of streamlines and isotherms for comparing the related parametric changes. Furthermore, a comparative analysis is included between the use of porous and solid partitions along with a chamber without any partition. Conclusive results show that using a porous rather than a solid partition can increase the average Nusselt number by 26.28% at Ra = 10 5 up to a maximum of 565% at Ra = 10 7 . Similarly, lower thickness, higher frequency, and higher amplitude can increase the average Nusselt number by around 37.5%, 2.89%, and 1.17%, respectively.
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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) | 0.001 | 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".