A comparison study between seven procedures to predict vortex-induced vibrations on industrial chimneys
Bibliographic record
Abstract
Structures like towers and industrial chimneys are quite vulnerable to the vortex shedding phenomenon, due to their slenderness and non-aerodynamic form. Furthermore, due to their low structural damping, these structures are also more likely to reach large displacement amplitudes, which is caused by the lock-in effect. Although these structures are considered simple from the structural and aerodynamic viewpoints, the study of crosswind vibrations in these structures is quite complicated, as it involves the interaction of complex topics of fluid and structural mechanics, turning a reliable determination of the structural response into one of the most complicated problems in Wind Engineering. Because of that, this study aimed to compare some methods for predicting the response due to the vortex shedding phenomenon using full scale data from industrial chimneys. The chosen methods, which are exposed in codes and standards like Eurocode, National Building Code of Canada and CICIND Model Code for Steel Chimneys, derive from the Ruscheweyh’s correlation length model and the Vickery & Basu’s spectral mathematical model. In addition, these methods are also compared to three proposals made for the Brazilian Wind Code. This study concludes that the methods based on the Vickery and Basu’s model work better for large displacement amplitudes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".