Effect of surface waviness and aspect ratio on heat transfer inside a wavy enclosure
Bibliographic record
Abstract
A numerical simulation has been carried out to investigate the buoyancy induced flow and heat transfer characteristics inside a wavy walled enclosure. The enclosure consists of two parallel wavy and two straight walls. The top and the bottom walls are wavy and kept isothermal. Two straight‐vertical sidewalls are considered adiabatic. Governing equations are discretized using the control volume based finite‐volume method with collocated variable arrangement. Simulation was carried out for a range of surface waviness ratios, λ=0.00‐0.25; aspect ratios, A=0.25‐0.5; and Rayleigh numbers Ra=10 0 ‐10 7 for a fluid having Prandtl number equal to 1.0. Results are presented in the form of local and global Nusselt number distributions, streamlines, and isothermal lines for different values of surface waviness and aspect ratios. For a special case of λ=0 and A=1.0, the average Nusselt number distribution is compared with available reference. The results suggest that natural convection heat transfer is changed considerably when surface waviness changes and also depends on the aspect ratio of the domain. In addition to the heat transfer results, the heat transfer irreversibility in terms of Bejan number (Be) was measured. For a set of selected values of the parameters (λ, A, and Ra), a contour of the Bejan number is presented at the end of this paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".