Ultrasonic transducer for high temperature thickness monitoring
Bibliographic record
Abstract
Refineries typically undergo 30–60 days of programmed shutdowns every 4 to 6 years to assess the integrity of infrastructure not accessible during service. So far, the literature demonstrated the feasibility of using ultrasonic probes for continuous long-term monitoring up to 350°C. However, when the temperature keeps rising, an air- or water-cooling system is required or a long delay line is used to move the probe away from the heat source. Providing a real time monitoring solution for the most critical components operating at high temperature would increase safety and reduce the maintenance burden. In this talk, an ultrasonic probe operating completely immersed inside a 600°C (1112°F) environment for extended periods of time is presented. The design of the transducer will be discussed. Its small footprint enables it to be mounted at several critical and difficult to access locations. In order to validate performances transducers were mounted on plates and pipes of different materials and thicknesses and the assembly was put inside a furnace. The results of long-term stability at 600°C, the consistency of the measurements over a temperature range from 20°C to 600°C, and the robustness during aggressive thermal cycling will be presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".