Thermal management investigation on fluid processing <scp>within</scp> porous rhombic cavities: Heatlines versus entropy generation
Bibliographic record
Abstract
Abstract The study of natural convection in rhombic enclosure has been a subject of interest in the recent past. In the current work, the effect of various shapes of the rhombic enclosure (φ = 45°, 60°, and 75°) is investigated for fixed areas (A = 0.5, 1, and 1.5) over an extensive range of parameters (Prm = 0.015 − 1000, Dam = 10−5 − 10−2, and Ram = 106) for an optimal thermal configuration. The rhombic enclosure is subjected to the isothermal heating of the bottom wall along with the cold side and top walls. The results are shown in the form of entropy generation maps (Sθ and Sψ) along with the heatlines (Π), streamlines (ψ), and isotherms (θ). It is observed that Sθ decreases near the left wall whereas Sθ near the top and right walls increases with φ, irrespective of A. A large regime near the bottom portion of the left wall and right portion of the bottom wall corresponds to the higher magnitude of Sψ and that extends over a wider region for the higher φ. The results in terms of total entropy generation (Stotal), average Bejan number (Beav), and average Nusselt number ( ) are compared with those within the square cavity ( ). Overall, it may be concluded that the rhombic enclosure with at A = 0.5 is the optimal configuration for the thermal processing of the fluids based on the moderate Stotal and .
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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 | 0.000 |
| 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".