Numerical validation of natural convection heat transfer with horizontal rectangular fin array using straight knurling patterns on fins—correlation for Nusselt number
Bibliographic record
Abstract
Abstract Natural convection heat transfer from horizontal rectangular fin array with various knurling patterns has been studied experimentally by the present authors to investigate the effect of knurl‐produced surface roughness of fin on the heat transfer rate. The parameters like knurling height from base, knurling depth, fin spacing, and supply wattage were considered for parametric study. In the present paper, numerical method (CFD) is used to simulate natural convection phenomena with knurled fins and results are validated with the experimental data available from literature. The numerical results show similar trends compared with experimental data and one can use this method to study various fin configurations for knurling patterns. The flow patterns from experiments and numerical method are compared for different supply wattages and fin spacing to back up the conclusion. It is also observed that the variation in nondimensional roughness depth and spacing ( D kn / H and S / H ) have more effect on Nusselt number than roughness height parameter ( H kn / H ). Further, the method is extended to study numerically large number of fin configurations with knurling patterns to gather sufficient data for Nusselt number with respect to fin geometric parameters as above and establish correlation for heat transfer coefficient for such type of fins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".