Simulation of Long Term Pipe Exposure to Disbondment With an Advance Permeable Coating Model (PCM3.0)
Bibliographic record
Abstract
The Permeable Coating Model (PCM) is a mathematical model which has been developed to predict the generation and evolution of environments under a disbonded permeable coating as a consequence of the action of CP. The early version of the PCM was presented at IPC 2004, which focused on the prediction of the environment under a disbonded permeable coating in a fully water-saturated soil without including the generation of CO2 in the soil. As a consequence, the model predicted the generation of a high-pH environment for NaOH-based solution rather than a concentrated HCO3−/CO32− trapped water. The advanced version of PCM takes into account the generation of CO2 in soil by both microbial activity and plant roots respiration. Also, the concept of degree of saturation was introduced, which enables the PCM to predict the pipe surface conditions for situations in which the pipeline is either permanently above or below the water table. The simulation results from the advanced version of PCM show that the concentrated carbonate (i.e, 0.1 to 1 M) and high pH (> 9) environment required for high pH SCC, can be developed within 10 years with a CP level of −1.5VCSE and T > 25°C. For low temperatures (i.e., T ≤ 25°C) a time longer than 10 years is necessary to establish this concentrated carbonate and high pH environment. The results also suggest that although the necessary environment can be generated through the application of CP = −1.5 VCSE, the selected CP level does not cause the potential on the pipe surface to reach the critical potential range (i.e., −750 mVCSE to −600 mVCSE) required for high pH SCC. As expected, the loss of CP after an application of CP for 10 years could provide the environment needed for near-neutral pH SCC to occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".