Investigation of Fluidelastic Instability of Fuel Rod Bundles Subjected to Combined Axial Flow and Jet Crossflow
Bibliographic record
Abstract
Abstract Flow-induced vibration is a major concern in the nuclear industry. The combined axial flow and jet crossflow has been found to induce fluidelastic instabilities (FEI) within the rod bundle, thus increasing the risk for fretting wear of the fuel rods. This paper presents an experimental work aiming to improve our understanding of the dynamic behavior of two rod bundles subjected to combined axial flow and localized jet crossflow. Two different bundle geometries were experimentally investigated. The first was a 6 × 5 flexible single-span bundle and the second one was a 7 × 5 reduced scale nuclear fuel assembly mockup. Two working flow conditions, pure axial flow and jet in transverse flow (JITF) are studied. The experiments show that the bundle is stable under pure axial flow as expected. Then, for the single-span the response of the array under JITF in two eccentricity scenarios is tested for different axial velocities. The results show that the fluidelastic instability (FEI) threshold could occur whenever the velocity ratio (VR=Vjet/Vaxial) is above 1.25. The second part of the work is the design, fabrication and tests on the multispan array. Characterization tests are performed to identify the modal parameters of the rod bundle. Two working flow conditions, pure axial flow and JITF are studied. The experiments show that the rod bundle is stable under pure axial flow. The response of the array under JITF in three eccentricity scenarios is tested for two axial velocities. The results show that the rod bundle is stable under the jet velocity tested for all cases.
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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 | 0.000 |
| 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".