Predicting Fluidelastic Instability in Tube Array With Potential Theory
Bibliographic record
Abstract
This paper studies the possibility to use potential theory to predict fluidelastic instability critical velocity in tube bundles. Potential flow is calculated semi analytically using Laurent expansions with the addition of discrete vortices behind the tube. The only experimental criterion used in this approach is the location of vortices behind the tubes. Using the linearized unsteady Bernoulli theorem we are able to model fluid forces as added mass, damping and stiffness effects. Fluid forces include coupling terms; that is the force on another tube induced by tube acceleration, velocity and location. The tube array is then described by a mass, damping and stiffness. The fluidelastic instability critical velocity becomes the solution of a linear eigenvalue problem. This approach has been compared with several experimental values of mass, damping and stiffness measurements, as well as the critical velocity. Mass matrix is in a very good agreement with experimental values, however damping and stiffness models still need some improvement. In the end, the model is able to predict the critical velocity within 20% of experimental values. This approach does not need stiffness experimental values (stability derivative) nor time delay as the stiffness, damping and mass matrices are calculated independently. The main purpose of this work is to understand the effect of induced forces involved in the fluidelastic instability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".