Ultrasonic Array Imaging for Defect Detection in the Nuclear Industry
Bibliographic record
Abstract
Non-destructive evaluation of critical nuclear power plant components has to be rapid and reliable. Ultrasonic testing satisfies these requirements and is used extensively in the industry. Ultrasonic phased arrays offer higher sensitivity and more complete and intuitive data analysis in comparison to conventional single element probes. In the Canadian nuclear industry, components such as pressure tubes are typically tested with focused ultrasound. Phased array imaging, with focused parallel transmissions, is possible, but has not yet been considered in most industrial applications. This work tests a variation of the medical imaging algorithm Unified Pixel-Based Beamforming (UPBB) for a pressure tube inspection. The technique detects all of the considered discontinuities, but could require a long acquisition time. An adaptive, combined Plane Wave and UPBB imaging is proposed to improve performance with respect to inspection time. This combined inspection firstly provides an overview of the conditions in a large region of the component and then targets additional locations of interest with focused waves.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".