Study on Effects of Surface Division Patterns on Condensation Heat Transfer for dropwise and Film Condensation Coexisting Surfaces
Bibliographic record
Abstract
Experiments were taken to examine the possibility of enhancing condensation heat transfer characteristics of steam by using a thick polymer film promoting dropwise condensation on dropwise and film condensation coexisting (DFC) surfaces. A polymer film with thickness of more than 1 mm was coated on the dropwise-condensation regions of the external surface of brass tubes with different surface divisions. The area ratio of dropwise parts and filmwise parts was fixed at 1:1 for the six surfaces, whilst the division numbers were different with each other. It was found that the condensation heat transfer characteristics were greatly influenced by the surface division number. Compared with the bare surface, the condensation heat transfer of dropwise and film condensation coexisting surface showed an enhancement ratio of 1.3 to 4. Analyzing the experimental results of this paper and reported in literatures indicated that the heat transfer enhancement characteristics of dropwise and film condensation coexisting surface depends not only on the surface division pattern at a fixed area ratio of dropwise and film parts, but also on the operating condition. The heat transfer enhancement ratio showed that there exists a maximum value while increasing the surface subcooling degree. An optimal choice exists between surface division numbers and surface subcooling degree.
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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 | 0.000 |
| 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".