The Flow-Structure Couplings of Fluidelastic Instability and the Effect of Frequency Detuning in Triangular Tube Bundles Subjected to a Two-Phase Flow
Bibliographic record
Abstract
Abstract For decades, fluidelastic instability (FEI) has been known to cause dramatic mechanical failures in tube bundles. Therefore, it has been extensively studied to mitigate its catastrophic consequences. Most of these studies were conducted in controlled experiments where significant simplifications to the geometry and flow conditions were utilized. One of these simplifications is the assumption that all tubes have the same dynamic characteristics. However, in steam generators with U-bend tube configuration, the natural frequencies of tubes are nonuniform due to manufacturing tolerances and tubes' curvature in the U-bend region. Thus, this investigation aims to understand the rule of frequency variation (detuning) on FEI in two-phase flow. This includes investigating the effect of detuning on transverse and streamwise FEI for air–water mixture flow. The role of FEI damping and stiffness couplings was investigated over the entire range of air void fraction, or equivalently, the mass-damping parameter. It was found that frequency detuning could elevate the stability threshold caused by either coupling at high air void fraction in the case of transverse FEI. Furthermore, the frequency detuning had a marginal effect on the stability threshold for water flow. It was observed that the mass-damping parameter has a critical impact on FEI under detuning conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".