Flow Induced Vibration and Fretting Wear: An Integrated Approach
Bibliographic record
Abstract
Fuel assemblies are exposed to severe thermal, mechanical and radiation loads during operation. Global core and local fuel assembly flow fields typically result in fuel rod vibration. Under certain conditions, this vibration, when coupled with other factors, might result in excessive cladding fretting wear. This phenomenon is of the concern for nuclear fuel designers, especially in light of the need for higher burnup, longer cycle lengths, and operational safety margins in fuel designs. Understanding of (1) the fretting wear margins for a particular nuclear fuel design, (2) the probability of a fuel assembly exposed to a particular set of thermal, mechanical, flow and radiation conditions being at risk of excessive wear, and (3) the factors affecting fretting wear resistance, are important in order to better guide design, testing, and operational flexibility. In this paper, an integrated method to estimate fretting margin of nuclear fuel is presented, including its formulation, benchmark against experimental data and example application to in-core conditions. The major features of the method are as follows: • flow and rod vibration response are coupled through a linear structural analysis model, • flow field is determined using a sub-channel thermal-hydraulic code, • wear progression is treated as a time-dependent process, through taking into account impact of resulting rod-to-support clearance, • a possibility of a fluid-elastic instability is accounted for. Supporting data on basic wear mechanisms, flow field and fuel assembly fretting wear behavior obtained at a number of experimental facilities at Westinghouse Electric Company and Atomic Energy of Canada Limited are also presented. These facility include: • VIPER hydraulic test loop data where vibration response and wear are measured under prototypical flow conditions, and • autoclave fretting-wear machine steam employed to determine fretting-wear coefficients of fuel rod and grid-support designs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".