Thermal & Moisture Cycling Impacts on the Corrosion Behavior of Carbon Steel under Contacting and Contact-Free Insulation
Bibliographic record
Abstract
Abstract CUI (corrosion under insulation) is among the leading damage mechanisms active in oil refining and hydrocarbon facilities. Reportedly, 10% of the total maintenance budget of a typical refinery is spent on inspecting and fixing CUI damages. The mechanism of CUI is complex and influenced by many factors namely the design of the insulated system, insulation type, ambient conditions, condition of coatings and insulation materials, as well as operating parameters. This study addresses the quantified influence of cyclic temperatures on CUI in comparison with constant temperatures for various insulation designs namely the closed-contacting and contact-free with low-point drainage. It also investigates the CUI behaviors under moisture cycling (i.e., wet-dry conditions) of candidate insulation designs in comparison to a fully wet environment. To simulate this, corrosion rates were determined under Isothermal wet, isothermal wet-dry, cyclic wet, and cyclic wet-dry conditions using weight loss measurement as per applicable ASTM G189-07 standard. The influence of cyclic temperatures and wet-dry conditions were also studied using the linear polarization resistance method in the system with contact-free insulation. The corroded coupons were then characterized using a microscope and surface topography. The wet-dry conditions under both cyclic and constant temperatures caused more corrosion rates than those under wet conditions. Also, cyclic temperatures caused more corrosion rates than constant temperatures under both wet and wet-dry conditions.
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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) | 0.001 | 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".