A Semi-Empirical Approach for Modelling the Effect of Tube-Support Preload on Fluidelastic Instability of Tube Arrays
Bibliographic record
Abstract
Abstract The purpose of this study is to develop a simplified semi-empirical model for characterizing the effect of tube support preloads on fluidelastic instability (FEI). Such a model is useful for a first order approximation of the onset for FEI of a tube bundle subjected to preload. A numerical model is used to generate data on FEI of tube arrays subjected to tube-support preloads. The numerical model uses the quasi-steady approach for modelling fluidelastic instability. Tube-support interaction at the loose supports is modelled using a combination of the Hertz contact model and force balance friction model. Effects of the support preload, tube-support impact, and friction are accounted for in the model. The numerical model is validated against experimental data, and then used to generate FEI threshold data. The results are then consolidated to produce a simplified semi empirical correlation for reproducing the effect of the tube support preload on the FEI behavior of a tube bundle. The formulation of the tube-support preload correlation is justified using a theoretical approach based on the Connors’ model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".