Non-Linear Damping Identification in Nuclear Systems Under External Excitation
Bibliographic record
Abstract
In Pressurized Water Reactors (PWR) the fluid-structure interaction between the coolant and fuel assemblies is an important phenomenon that is directly associated to safety and performance issues for the nuclear industry. Fuel assemblies are formed by bundling fuel rods, long slender pressurized tubes containing uranium pellets, with hollow guide and instrumentation tubes using a number of spacer grids for support. In order to correctly simulate the response of a fuel assembly under external excitation, an investigation of the system damping is necessary. Recent study shows that the damping may grow significantly with the vibration amplitude, increasing the safety factor. In order to address this issue, the present study has developed a tool to identify the vibration characteristics of non-linear mechanical systems from experimental forced vibration data obtained at different excitation levels. In particular, this project focuses on the damping identification. A parameter identification methodology for non-linear systems, based on harmonic balance method, is used to identify the damping governing the motion of such systems. The tool has been developed by using hardening non-linear responses of two sandwich panel and a metal plate subjected to external harmonic excitation. The method has been validated by comparison with the damping identified by the full non-linear model of the two sandwich panels. In the three cases, an increase of damping with the vibration amplitude is found and discussed.
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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) | 0.001 | 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".