The Influence of Flexible Strip Height on Convective Heat Transfer Enhancement
Bibliographic record
Abstract
Abstract A 12.7 mm wide flexible rectangular strip, made from 0.1 mm-thick aluminum sheet, is experimentally explored as a vortical flow generator for promoting heat convection from a flat plate in a wind tunnel. The strip is positioned normal to the freestream with an incoming velocity of 10 m/s, resulting in a Reynolds number, based on the strip width, of 8,500. The influence of the height of the flexible strip on the convective heat transfer enhancement is of interest. Three strip heights, 25.4 mm, 38.1 mm and 50.8 mm, were investigated. The heat transfer results are expressed in terms of Nusselt number, Nu, normalized by the unperturbed reference Nu0. The shortest, 25.4 mm high flexible strip resulted in the highest peak and overall heat transfer enhancement. The distribution of the local heat transfer enhancement is found to correlate well with the turbulent flow motion detailed using a triple-sensor hot-wire anemometer. Pointedly, the heat transfer rate is most elevated when the local flow is moving toward the hot plate, sweeping across a stretch of the surface before moving away from it. These effective convective motions are most effectively generated by the vortices produced by the shortest strip.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".