Influence of Insulation Stand-Off Membranes and Moisture Drainage on the Corrosion Under Insulation Behavior of Out-of-Service Carbon Steel Piping
Bibliographic record
Abstract
Corrosion under insulation (CUI) is reported as being the driver behind the majority of failures in thermally insulated process piping and poses significant maintenance expenditures and service interruptions. Small-bore pipes are more prone to failure from CUI due to their lower wall thickness and lower surface area in comparison to larger diameter pipes. This research work simulates the CUI behavior of small-bore piping over a 12-month period in outdoor settings to mimic the out-of-service conditions in an industrial field setting. For this, two sets of assemblies were made which comprised fibrous stone wool insulations applied over the carbon steel coupons with and without stand-off membranes and low-point drain. Both assemblies were presoaked via submerging in water and tested in outdoor conditions for 12 months followed by insulation removal and detailed characterizations. Corrosion behaviors of steel coupons were studied using weight loss, pit depth measurement, surface profile topography, and scanning electron microscopy, whereas chemical compositions of the corrosion products were investigated using x-ray diffraction. Corrosion rates derived from mass loss data were compared with those calculated using the semi-quantitative risk-based inspection method. The kinetics behind the formation of various corrosion products are also discussed. The stand-off membranes and low-point drain resulted in the reduced time of wetness (i.e., moisture exposure time) that in turn resulted in the domination of lepidocrocite (γ-FeOOH) along with reduced uniform metal loss rate and reduced pit depth in comparison to conventional closed-contact insulation system.
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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) | 0.000 | 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".