Corrosion Scale and Moisture Assessments – an Improvement to On-Stream Inspections for CUI Management
Bibliographic record
Abstract
Abstract Corrosion under insulation refers to localized corrosion under thermal insulations which has resulted in failure incidents in the hydrocarbons industry. The non-destructive examination (NDE) inspections for in-service assets (pipes, equipment) via stripping-off insulations are generally limited to a few feet (or meters) as the removal of insulations from larger sections is limited by the safety issues and required heat conservations in the assets. For these reasons, major CUI inspections are generally performed only during outage conditions, as it permits access and inspections for larger areas. On the other hand, the ambient temperatures (due to the out-of-service conditions) which also results in the moisture buildup on the insulated metals (via condensation), change the chemical composition of the corrosion scale and in turn, the kinetics and mode of the corrosion damage. Therefore, traditional NDEs conducted on out-of-service assets do not mimic the periodical in-service CUI damage. Moreover, there have been many events where insulated assets failed while in service as the metal loss rate from the localized CUI damage exceeded the future corrosion allowance. This study proposes an improved methodology for in-service CUI inspections via accounting for the chemical nature of corrosion products, insulation materials, moisture assessments, etc. to better predict the CUI damage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".