Natural convection in a porous cavity with spatial sidewall temperature variation
Bibliographic record
Abstract
Purpose To study the natural convention in a square porous cavity induced by heating one of the sidewalls and the other sidewall is cooled, while the horizontal walls are adiabatic. The heated wall is assumed to have spatial sinusoidal temperature variations about a constant mean value. Design/methodology/approach The Darcy model is used in the mathematical modeling of the natural convection in porous cavity. A finite volume method based on QUICK scheme is used to solve numerically the non-dimensional governing equations. Findings It is found that the average Nusselt number varies based on the hot wall temperature. It increases with an increase in the amplitude, while the maximum average Nusselt number occurs at the wave number of k=0.75 for Rayleigh number based on the permeability of the medium of 500 and 1000 and at k=0.70 for a Rayleigh number of 10-200. Research limitations/implications The effects of the amplitude (0-1.0) and the wave number (0-5) of the heated sidewall temperature variation on the natural convection in the cavity are investigated for Raleigh number 10-1000. Practical implications The spatial sinusoidal temperature variation occurs in the applications when a cylindrical heater or a periodic array of heaters placed on a flat wall. Originality/value This paper is providing the details of the heat transfer inside the cavity which can be used in thermal design.
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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".