The effects of tube Dimples-Protrusions on the thermo-fluidic properties of turbulent forced-convection
Bibliographic record
Abstract
Among the techniques offered to improve the efficiency of heat exchangers, the Dimpled surfaces were repeatedly reported as an applicable solution. In this regard, the current study is formed to evaluate the thermofluidic performance of Dimpled-Protruded tubes while following four novel investigations. Firstly, the impact of Protrusions next to the typical Dimple shape on the turbulence mixing and the forced-convection phenomenon was investigated. Secondly, a detailed comparison was carried out between the interfacial heat transfer of the Dimpled-Protruded tube and the smooth equivalent. Thirdly, a novel Dimpled-Protruded arrangement was utilized, and its thermal performance was evaluated in various Reynolds (Re) numbers. Lastly, both zonal and interfacial heat transfer mechanisms intensified by using Dimpled-Protruded shapes were scrutinized. Based on the results, the small vorticities at the Dimpled-Protruded locations were responsible for increasing the interfacial heat transfer. Moreover, the rough tube prompted the flow turbulence at lower Re; thus, the heat transfer improved by 36.21% compared with the smooth type. Meanwhile, although the convective heat transfer improved up to 84.46%, the friction losses increased between 25% and 60% as the Re increased. Fortunately, however, the friction effects have produced insignificant pressure drops, proving that Dimpled-Protruded tubes effectively improve forced-convecting heat transfer.
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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) | 0.000 | 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 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".