High temperature flexible ultrasonic transducers for NDT of pipes
Bibliographic record
Abstract
NDT of power, chemical and petroleum plants is an increasingly important element for safety improvement and extension of plant life span. Such plants contain numerous tubular structures and to obtain ultrasonic signals with sufficient signal-to-noise ratio (SNR), ultrasonic transducers (UTs) often need to conform to these structures and operate at elevated temperatures. Flexible UTs are suitable under such conditions and adaptable to different tube diameters because they ensure good self-alignment with the object’s surface and a uniform couplant thickness. This results in good transmission of ultrasonic energy into the component and reduced noise. In this study flexible UTs, consisting of piezoelectric films with thicknesses larger than 40 μm deposited on a 75 μm thick metal membrane, were developed for NDT applications up to 500°C. The piezoelectric films were made by a sol-gel spray technique and can be used for NDT of pipes of diameters larger than 25.4 mm. At room temperature the ultrasonic performances of flexible UTs were at least as good as commercially available 5 MHz and 10 MHz broadband UTs. At elevated temperatures accurate pipe thickness measurements were achieved because of the high SNR of the ultrasonic echoes obtained in the pulse-echo mode. For continuous NDT, an induction brazing technique which can be performed on-NDT-site was developed to braze flexible UTs to steel pipes. The brazing material serves as a high temperature ultrasonic couplant. Finally, the development of high temperature flexible UT arrays for NDT will be discussed.
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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".