Modeling of Fluidelastic Instability Forces in Fully Flexible Tube Arrays
Bibliographic record
Abstract
Fluidelastic instability remains the most devastating phenomenon in tube bundles subjected to cross-flow. Models have been developed to estimate the threshold of instability. Moreover, several time-domain models of fluidelastic instability have been developed to determine tube/support interaction parameters of tubes with loose supports. The present work deals with time domain modeling of fluid-elastic instability forces in a fully flexible tube array subjected to cross-flow. The model is based on the flow redistribution theory proposed initially by Lever and Weaver [1]. The proposed model utilizes fewer input parameters and can model various tube bundle geometries with any pitch-to-diameter ratio. Finite element method is used for solving the system response. The flow field inside the tube array is discretized into flow subdomains, each of which is surrounded by 4 tubes. The perturbation in the flow field, within each subdomain, is obtained by superimposing the effects of neighboring tube motions. The model has been applied to assess the response of a single flexible tube as well as multiple flexible tubes. It is shown that the single flexible model overestimates the stability threshold compared to the multiple flexible tube counterpart, especially at high mass-damping parameters. The results show a good agreement between the predicted and the experimental results. The proposed model does not assume any predetermined tube response or any tube motion pattern.
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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 | 0.000 |
| 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 | 0.001 | 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".