Numerical Estimation of Fluidelastic Instability in Tube Arrays
Bibliographic record
Abstract
This study investigates unsteady flow in tube bundles and fluid forces, which can lead to large tube vibration amplitudes, in particular, amplitudes associated with fluidelastic instability (FEI). The fluidelastic forces are approximated by the coupling of the unsteady flow model (UFM) with computational fluid dynamics (CFD). The CFD model employed solves the Reynolds averaged Navier–Stokes equations for unsteady turbulent flow and is cast in an arbitrary Lagrangian–Eulerian form to handle any motion associated with tubes. The CFD solution provides time domain forces that are used to calculate added damping and stiffness coefficients employed with the UFM. The investigation demonstrates that the UFM utilized in conjunction with CFD is a viable approach for calculating the stability map for a given tube array. The FEI was predicted for in-line square and normal triangle tube arrays over a mass damping parameter range of 0.1– 100. The effect of the P/d ratio and the Reynolds number on the FEI threshold was also investigated.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".