Time Domain Models for Damping-Controlled Fluidelastic Instability Forces in Multi-Span Tubes With Loose Supports
Bibliographic record
Abstract
This paper presents simulations of a loosely supported multi-span tube subjected to turbulence and fluidelastic instability forces. Several time-domain fluid force models simulating the damping controlled fluidelastic instability mechanism in tube arrays are presented. These models include the negative damping model based on the Connors equation, fluid force coefficient-based models (Chen; Tanaka and Takahara), and two semi-analytical models (Price and Pai¨doussis; and Lever and Weaver). Time domain modelling challenges for each of these theories are discussed. The implemented models are validated against available experimental data. The linear simulations show that the Connors-equation based model exhibits the most conservative prediction of the critical flow velocity when the recommended design values for the Connors equation are used. The models are then utilized to simulate the nonlinear response of a three-span cantilever tube in a lattice bar support subjected to air crossflow. The tube is subjected to a single-phase flow passing over one of the tubes spans and the flow velocity and the support clearance are varied. Special attention is paid to the tube/support interaction parameters that affect wear, such as impact forces, contact ratio, and normal work rate. As was seen for the linear cases, the reduced flow velocity at the instability threshold differs for the fluid force models considered. The investigated models do, however, exhibit similar response characteristics for the impact force, tip lift response, and work rate, except for the Connors-based model that overestimates the response and the tube/support interaction parameters for the loose support case, especially at large clearances.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".