Heat Transfer Enhancement Methods: Concave and Convex Shape Fins
Bibliographic record
Abstract
The heat transfer coefficient is a common engineering concept and a significant number of researchers are focusing on improving the heat transfer performance of the system by increasing the heat transfer coefficient. Heat transfer enhancement techniques are broadly classified in three broad categories: passive, active and compound techniques. This study is concerned with the effect of fins placed inside a rectangular channel on heat transfer and pressure drop to be concave and convex against flow. The effect of some independent parameters such as Reynolds number, height, diameter, number and angle of the fins on Nusselt number, friction coefficient were experimentally studied. Turbulent flow experiments were performed for the range 2514-13111 of Reynolds number. The results showed that Reynolds number is the most effective parameter. Based on the Reynolds number, the heat transfer increased between 1.4-2.8 times, friction coefficient increased between 1.1-3.4 times according to smooth channel.
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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 | 0.000 | 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) | 0.000 | 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".