Advancing the State-of-the-Art for Fretting Wear Testing
Bibliographic record
Abstract
Abstract High-temperature fretting wear tests have been conducted at CNL’s Chalk River Laboratories since the 1970s to study the wear characteristics of nuclear power plant components. The initial testing approach relied on an unbalanced motor excitation system, along with pre- and post-test characterization of the work rate. The approach was developed over subsequent decades to enable work rate monitoring during test runtime, through the incorporation of high-temperature dynamic force and displacement sensors into the harsh test environments (up to 12 MPa, 320°C). The approach has been used to predict the life of CANDU and light water reactor components subjected to in-service vibrations, for example in the case of fuel fretting and steam generator tube/support fretting. In 2019, an effort was initiated to update the existing fretting wear testing capabilities to leverage advancements in data acquisition hardware, computational technology, and vibration instrumentation, to meet the evolving needs of the nuclear industry. This effort saw an overhaul of the test control and monitoring approach, enabling the excitation of the test specimens to be generated based on empirical excitation force spectra, as well as real-time monitoring and tallying of contact events throughout tests of extended duration (typically 500 hours). Alongside retrofitting the existing facilities, new capabilities are also being developed to enable fretting wear testing in advanced reactor conditions, such as testing in 700°C helium representative of high-temperature gas reactors, and testing in refreshed autoclave environments to enable more stringent chemistry control. An overview of the recently-upgraded fretting wear testing capabilities and those under development is provided.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".