Modeling and Simulation of the Pitting Microbiologically Influenced Corrosion in Different Industrial Systems
Bibliographic record
Abstract
Abstract A significant percentage of the corrosion failures in many industries are caused or accelerated by the effects resulting from the activities of specific microorganisms. In aqueous environments, microorganisms tend to attach themselves to metal surfaces and form biofilms. These biofilms create nonuniform surface conditions, leading to severe localized pitting microbiologically influenced corrosion (MIC). In this study a numerical model was developed to study the anaerobic pitting MIC of steel in sulfate-reducing bacteria (SRB) environments. The transient two-dimensional model in cylindrical-coordinates was solved using the finite difference technique, employing the alternating direction implicit (ADI) method. The SRB cathodic depolarization theory was adopted as the MIC mechanism. The pitting MIC model was applied to marine, waste, and fresh water systems. The effect of sulfate concentration and SRB kinetic parameters on the growth rate and magnitude of the pitting MIC were investigated. The model was very successful in estimating and predicting the pitting MIC growth rate and depth as well as the pit shape at any time in the three studied environments. The model results were also found to be in good agreement with the experimental data found in the literature under the same conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".