A study on the contact quality improvement of fin to tubeassemblies
Bibliographic record
Abstract
Abstract: Fin to tube assembly is a common type of connection in heat exchangers, especially for smaller equipment. The tube is expanded by a die or an expander during the assembly process to close the gap between the tube and the fin collar for better heat transfer. The generated interference and the contact area depend not only on the radial expansion produced by the die and initial gap but also on the shape of the fin contact area with the tube before expansion. The die expands to close the gap and produces a small interference that allows the fin collar to adhere to the tube. However, the contact at the interface is not continuous across the width of the mating surfaces according to recent research. As a result of this poor contact quality, the heat transfer due to conduction is considerably reduced and the heat exchanger efficiency is highly compromised. This study is aimed at establishing a relationship between the profile shape of the fin hole and the contact quality and provides guidelines for improving the quality of tube to fin contact. Tubes with different materials, dies with different sizes and fins with collar with hourglass proposed shape will be assessed using a series of expansion simulations conducted on different FE models. Finally, the micro gaps generated during the expansion process at the tube to fin interface are utilized to evaluate the quality of the contact surface.
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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".