Identification of Non-Linear Damping of Nuclear Reactor Components in Case of One-to-One Internal Resonance
Bibliographic record
Abstract
In Pressurized Water Reactors (PWR) assemblies are exposed to challenging thermal, mechanical, and irradiation loads during operation. Global core and local fuel assembly flow fields coupled with seismic excitation result in fuel assembly and fuel rod vibrations. The fact that vibrations may become excessive in certain conditions has consequences on operational safety margins in fuel assemblies designs. In order to understand how the fuel assembly responds dynamically to an external excitation, it is important to identify the main characteristics of the structures. Among them, the fuel assembly system damping is a fundamental parameter that is usually identified by a number of experiments involving fluid-structure interaction. Recent studies have shown that the damping ratio increases with the excitation force when the structure is entering large-amplitude vibrations, in which case the geometric non-linearities have to be taken into account. The present paper presents an advanced identification procedure developed to identify the system characteristics from experimental non-linear response curves obtained from forced vibration tests, accounting for fluid-structure interaction, at different excitation levels. Furthermore, the numerical tool developed in this analysis is capable of working with systems presenting one-to-one internal resonance, i.e. systems with symmetry such as circular tubes and circular cylindrical shells. The method relies on a harmonic decomposition of the displacement to cope with the data usually available by vibration measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".