Electrochemical Corrosion Finite Element Analysis and Burst Pressure Prediction of Externally Corroded Underground Gas Transmission Pipelines
Bibliographic record
Abstract
An integrative numerical simulation approach for pipeline integrity analysis is presented in this work, combining a corrosion model, which is the main focus of this paper, with a complementary structural nonlinear stress analysis, using the finite element method (FEM). Potential distributions in the trapped water existing beneath pipeline coating disbondments are modeled in conjunction with reaction kinetics on the corroding exposed steel surface using a moving boundary mesh. Temperature dependencies (25 °C and 50 °C) of reaction kinetics do not greatly affect final corrosion defect geometries after 3-yr simulation periods. Conversely, cathodic protection (CP) levels and pH dependencies within the near-neutral pH range (6.7–8.5) strongly govern depth profiles caused by corrosion, reaching a maximum of ∼3 mm into the pipeline wall. A 0.25 V amplification of CP potential combined with a 0.5 mm widening in disbondment opening size reduces defect penetration by almost 30%. Resulting corrosion defect geometries are used for stress examinations and burst pressure evaluations. Furthermore, nonlinear elastic–plastic stress analysis is carried out using shell elements in order to predict the burst pressure of corroded pipes. Corrosion is modeled by reducing the stiffness of a damaged element that has the dimensions of the defect. The predicted burst pressures are in good agreement with those obtained using an experimental-based formula.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".