A probabilistic-based numerical modeling of natural gas pipelines with random corrosion morphology
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Bibliographic record
Abstract
This study presents a probabilistic-based method for modeling realistic corrosion morphology on natural gas pipelines with the random field node mapping coupling (RF-NMC) model. An anisotropic random field is used to reconstruct mesh geometry through node-level random displacement. High-precision mesh deformation and local coordinate mapping enable adaptive geometric transformation. This ensures an accurate representation of corrosion features. The model is embedded in a finite element (FE) modeling to achieve precise, fast, and flexible prediction of failure pressure and identify failure paths. Compared with simplified geometry models, the RF-NMC approach significantly improves the accuracy of failure pressure predictions, as confirmed by burst tests. The method strikes a balance between accuracy and computational efficiency, allowing for the quick simulation of complex corrosion geometries while maintaining reliability. The main novelty lies in directly coupling anisotropic random fields with FE mesh nodes. The proposed method's automation potential is expected to support lifecycle integrity management of pipelines. • Presented a probabilistic-based method for corrosion modeling on natural gas pipeline with RF-NMC model. • Used anisotropic random field to reconstruct mesh geometry through node-level random displacement • Embedded RF-NMC model in FE analysis to achieve balanced failure prediction.
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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