The Effects of Fluid Working Conditions on Flange Face Corrosion
Bibliographic record
Abstract
Abstract The second most common cause of hydrocarbon leakage is corrosion in offshore platforms. In seawater and hydrocarbon services, bolted flange joints can be susceptible to corrosion at their flange face. The current work considers corrosion of bolted flanged gasketed joints using the COQT fixture (COrrosion Quantification Test) to evaluate corrosion in flange faces. According to the literature, both crevice corrosion and galvanic corrosion widely occur in bolted flanged gasketed connections, creating leakage paths of the pressurized fluid. Leakage failure in bolted flanged gasketed joints can cause hazards to the environment and human safety. Corrosion in bolted gasketed joints was investigated in the literature. However, these studies do not consider the influence of the operating parameters such as fluid flow, pressure, pH, conductivity, temperature, and gasket contact pressure. With the developed COQT fixture, which was introduced in the previous paper, different electrochemical techniques can be applied to measure flange corrosion under controlled test conditions. The polarization technique will be used to measure and compare the corrosion rate of flange at different fluid flow rates, and gasket contact stresses. The flange sample material is ASTM A105, and the gasket material is Teflon. Electrochemical tests are conducted with a solution of 3.5% NaCl. Confocal microscopy is used to visualize the morphology of the damaged zones on the surface, and localize and quantify the pits size caused by corrosion, respectively. Comparing the results of the electrochemical tests and the microscopic studies will identify the most influent factor on the corrosion rate of flanges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".