Corrosion under Insulation Performance of Insulation Stand-Offs and Non-Metallic Membranes
Bibliographic record
Abstract
Abstract CUI (Corrosion Under Insulation) is among the key damage mechanisms affecting equipment and piping in hydrocarbon processing facilities, as well as pipelines. The key reason behind CUI is the contact of soaked insulations with the metal(s). Insulation stand-offs and membranes can keep the soaked insulation off the pipe and mitigate CUI risk. This research study addresses the CUI simulation tests to characterize the corrosion behaviors of carbon steel under isothermal wet and cyclic wet conditions, in the presence of insulation stand-offs, low point drains, and Teflon membranes. The corroded coupons were characterized using microscope and surface topography to investigate the damage modes namely pitting, uniform corrosion, localized damage. Insulation stand-offs with low point drains showed uniform corrosion in comparison to closed-contacting insulation, which mainly caused localized corrosion and pitting. Teflon membrane with low point drains caused the least corrosion rate among candidate configurations and did not show any noticeable surface damage from pitting or even uniform corrosion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".