Acoustic Emission Testing of a Process Reactor
Bibliographic record
Abstract
An Acoustic Emission Test (AET) was performed on a hydrocarbon processing reactor to inspect for environmental cracking in 2000. The thick-wall reactor was built in 1965, fabricated from 2.25Cr-1Mo steel with Type 347 stainless steel weld overlay. Past internal inspections of the reactor using liquid penetrant had revealed cracking at internal ring supports. Such inspections were exhaustive and time consuming. The AET was performed to achieve 100% inspection coverage of the reactor and to identify suspect areas for further evaluation. The test included over-pressurization of the reactor to 10% above its operating pressure before the shutdown and the actual cooling of the reactor from 745°F to 300°F. Upon completion of the acoustic emission (AE) monitoring, significant indications were identified, most noticeably at the reactor dump nozzle and the bottom head to shell weld. Both of these were in areas that had not been inspected previously. Follow-up inspections, including external ultrasonic and internal liquid penetrant test, were performed to investigate all the areas of significant AE activities. The results showed internal cracks isolated to the weld overlay at the catalyst dump nozzle and a 12” long weld slag inclusion in the bottom head to shell weld. A methodology was developed also to evaluation criticality of AE signals during different stages of AE test.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".