Bibliographic record
Abstract
This research is particularly focused on studying heat transfer enhancement of a photovoltaic (PV) panel by putting an obstacle at the panel’s windward edge. The heat transfer enhancement is performed by disturbing the airflow over the surface and increasing the heat and momentum transfer. Different objects such as triangular, square, rectangular, and discrete rectangular ribs and partial grids were applied at the leading edge of a surrogate PV panel and flow and the heat transfer of the panel are investigated experimentally. This approach was selected to expand understanding of effect of these different objects on the flow and turbulence structures over a flat surface by analyzing the flow comprehensively. It is observed that, a transverse object at the plate’s leading edge would cause some flow blockage in the streamwise direction, but at the same time creates some velocity in the normal and cross stream directions. In addition to that, the obstacle generates some turbulence over the surface which persists for a long downstream distance. Also, among all studied objects, discrete rectangular ribs demonstrate the highest heat transfer rate enhancement (maximum Nu/Nu0 of 1.5). However, ribs with larger gap ratios are observed to be more effective at enhancing the heat transfer augmentation at closer distances to the rib, while at larger downstream distances from the rib, discrete ribs with smaller gap ratios are more effective. Furthermore, this work attempted to recognize the most influential flow parameters on the heat transfer enhancement of the surface. It is seen that the flow structure over a surface downstream of an object (flow separation-reattachment behaviour) has a significant effect on the heat transfer enhancement trend. Also, turbulence intensities are the most dominant parameters in enhancing the heat transfer rate from the surface; however, flow velocity (mostly normal velocity) is also an important factor.
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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".