On the Use of Advanced UT Phased Array Methodology and Equipment
Bibliographic record
Abstract
Phased array UT techniques are being used for inspections in nuclear and conventional power plants for more than 10 years. Applications involving simple linear arrays, using either sectorial scanning or multiple-angle raster scanning, are now commonly used and accepted. But the power generation industry is continuously looking for inspection solutions to more challenging inspection configurations, and this requires advanced software features and state-of-the-art phased array hardware. This paper will present several innovative applications of phased array technology, offering even more flexibility and increased inspection performance to the industry. It will be illustrated how the recently developed DYNARAY ™ phased array hardware and the new UltraVision ® 3 software can support these innovative phased array inspection concepts. For instance, 2D matrix arrays with a large number of active elements can be used to perform one-pass weld inspections while simultaneously searching for various flaw orientations. Also, position dependant focal law groups can be used to perform high-quality inspections on rough and wavy surfaces. The presented case studies will include considerations about phased array probe design, selection and generation of focal laws, inspection coverage and inspection performance.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".