Fluidelastic Instability Modeling of Loosely Supported Multispan U-Tubes in Nuclear Steam Generators
Bibliographic record
Abstract
Steam generators in nuclear power plants have experienced tube failures caused by flow-induced vibrations. Two excitation mechanisms are responsible for such failures; random turbulence excitation and fluidelastic instability. The random turbulence excitation mechanism results in long-term failures due to fretting-wear damage at the tube supports, while fluidelastic instability results in short-term failures due to excessive vibration of the tubes. Such failures may require shutdowns, which result in production losses, and pose potential threats to human safety and the environment. Therefore, it is imperative to predict the nonlinear tube response and the associated fretting-wear damage to tubes due to fluid excitation. In this paper, a numerical model is developed to predict the nonlinear dynamic response of a steam generator with multispan U-tubes and anti-vibration bar supports, and the associated fretting wear due to fluid excitation. Both the crossflow turbulence and fluidelastic instability forces are considered in this model. The finite element method is utilized to model the vibrations and impact dynamics. The tube bundle geometry is similar to the geometry used in CANDU steam generators. Eight sets of flat-bar supports are considered. Moreover, the effect of clearances between the tubes and their supports, and axial offset between the supports are investigated. The results are presented and comparisons are made for the parameters influencing the fretting-wear damage, such as contact ratio, impact forces, and normal work rate. It is clear that tubes in loose flat-bar supports have complex dynamics due to a combination of geometry, tube-to-support clearance, offset, and misalignment. However, the results of the numerical simulation along with the developed model provide new insight into the flow-induced vibration mechanism and fretting wear of multispan U-tubes that can be incorporated into future design guidelines for steam generators and large heat exchangers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".