Effect of Temperature on the Crevice Corrosion Propagation on Flange Faces
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The corrosion of flange faces poses a significant risk to the structural integrity of bolted flanged connections and can lead to severe leakage problems. It has been observed on multiple occasions that bolted flanged joints experience premature failure due to crevice corrosion on the flange faces, where the gasket is positioned, and these areas are not accessible nor visible. Crevice corrosion occurs due to the breakdown of the passive layer in an occluded area, specifically at the flange and gasket interface, greatly increasing the corrosion rate of the flange compared to general corrosion. The breakdown of the passive layer is a challenging phenomenon, occurring on a small scale and within a short period, making it difficult to detect through regular inspection methods. The propagation of crevice corrosion can be significantly influenced by working conditions. Given that the crevice geometry is one of the most influential factors in crevice corrosion, the specially designed COrrosion Quantification Test (COQT) fixture, introduced in previous papers, is utilized in this research, allowing the application of various electrochemical techniques to assess flange corrosion under a controlled environment. The primary objective of this research is to propose a methodology for measuring and comparing the crevice corrosion propagation rate of flange faces under different temperatures and fluid flow rates. The chemical composition of the flange specimens complies with ASTM A182 F321 stainless steel, while the gasket is a sheet graphite with no inhibitors. The results from potentiostatic polarization tests reveal an increase in crevice corrosion rate with elevated temperatures. Moreover, crevice corrosion rate decreases with the fluid flow rate increase. These findings highlight the influence of mass transport in controlling crevice corrosion propagation rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it