An Experimental Study of the Phase Lag Causing Fluidelastic Instability in Tube Bundles
Bibliographic record
Abstract
The phenomenon of fluidelastic instability forms a major limitation on the performance of tube and shell heat exchangers. It is believed that fluidelastic instability is attributed to two main mechanisms; the first is called the “Damping Mechanism”, while the second is called the “Stiffness Mechanism”. It is established in the literature that in order to model the damping controlled fluidelastic instability, a finite time delay between tube vibration and fluid response has to be introduced. Experimental investigation of the time delay between structural motion and the induced fluid forces is detailed in the present study. A parallel triangular tube array consisting of seven rows and six columns of aluminum tubes is built with a pitch ratio of 1.54. Hot-wire measurements of the interstitial flow perturbations are recorded while monitoring the tube vibrations in the lift and drag directions. Pressure transducers are installed inside the instrumented tubes to monitor the fluid forces. The phase lag between tube vibration and flow perturbation is obtained at different locations in the array. The effect of tube frequency, turbulence level, location of measurements, and mean gap velocity on the relative phase values is investigated. It is found that there are two well-defined regions of phase trends along the flow channel. It is concluded from this study that the time delay between tube vibration and downstream flow perturbation is associated with the vorticity convection downstream, while the time delay for upstream perturbations is associated with the effect of flow separation and vorticity generation which is propagated upstream from the vibrating tube.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".