The Effect of Fins on Fluidelastic Instability in In-Line and Rotated Square Arrays
Bibliographic record
Abstract
The study of fluidelastic instability in tube arrays has been ongoing for four decades. Although much research has been conducted, a full understanding of the mechanisms involved is still not available. Designers of cross-flow heat exchangers must depend on experience and empirical data from laboratory studies. As new designs are developed, which differ from these experimental facilities, there is an increased risk of failure due to fluidelastic instability. An experimental program was conducted to examine fluidelastic instability in in-line and rotated square finned tube arrays. Three arrays of each geometry type were studied; two with serrated, helically wound finned tubes of different fin densities, and the third, a bare tube which had the same base diameter as the finned tubes. The finned tubes under consideration were commercial finned tubes of a type typically used in the fossil and process industries. The addition of fins to tubes in heat exchangers enhances heat transfer due to the increased surface area and the turbulence produced by the flow moving over the fins. The resulting flow pattern/distribution due to the fins is therefore much more complicated than in bare tube arrays. Previous research has shown that an effective diameter of a finned tube is useful in the prediction of vortex shedding. This concept is used to compare the finned tube results with the existing bare tube array guidelines for fluidelastic instability. All of the tube arrays in the present study have the same tube pitch, and have been scaled to have the same mass ratio. Results for the rotated square arrays show that the use of an effective diameter is beneficial in the scaling of fluidelastic instability and the finned tube results are found to fit within the scatter of the existing data for fluidelastic instability. For in-line square arrays, the results indicate that fins significantly increase the stability threshold.
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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".