Time Domain Models for Damping-Controlled Fluidelastic Instability Forces in Tubes With Loose Supports
Bibliographic record
Abstract
This paper presents simulations of a loosely supported cantilever tube subjected to turbulence and fluidelastic instability forces. Several time domain fluid force models are presented to simulate the damping-controlled fluidelastic instability mechanism in tube arrays. These models include a negative damping model based on the Connors equation, fluid force coefficient-based models (Chen, 1983, “Instability Mechanisms and Stability Criteria of a Group of Cylinders Subjected to Cross-Flow. Part 1: Theory,” Trans. ASME, J. Vib., Acoust., Stress, Reliab. Des., 105, pp. 51–58; Tanaka and Takahara, 1981, “Fluid Elastic Vibration of Tube Array in Cross Flow,” J. Sound Vib., 77, pp. 19–37), and two semi-analytical models (Price and Païdoussis, 1984, “An Improved Mathematical Model for the Stability of Cylinder Rows Subjected to Cross-Flow,” J. Sound Vib., 97(4), pp. 615–640; Lever and Weaver, 1982, “A Theoretical Model for the Fluidelastic Instability in Heat Exchanger Tube Bundles,” ASME J. Pressure Vessel Technol., 104, pp. 104–147). Time domain modeling and implementation challenges for each of these theories were discussed. For each model, the flow velocity and the support clearance were varied. Special attention was paid to the tube/support interaction parameters that affect wear, such as impact forces and normal work rate. As the prediction of the linear threshold varies depending on the model utilized, the nonlinear response also differs. The investigated models exhibit similar response characteristics for the lift response. The greatest differences were seen in the prediction of the drag response, the impact force level, and the normal work rate. Simulation results show that the Connors-based model consistently underestimates the response and the tube/support interaction parameters for the loose support case.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".