Development of a Profile Matching Criteria to Model Dents in Pipelines Using Finite Element Analysis
Bibliographic record
Abstract
Current industry standards cite depth and interaction with additional stress risers as the key indicators of pipeline integrity concerns in regards to dents. There have been significant efforts towards the improvement of these benchmarks in recent years. Several dent assessment methods are presented in literature, including research focused on the use of finite element analysis (FEA). The accurate assessment of dents using FEA is heavily reliant on how close the shape produced by the FEA model aligns with the shape of the actual dent. The research presented in this paper has been conducted to evaluate the sensitivity of the stresses and strains to the dent profile shape. Information regarding the existence, shape, and size of dents is typically provided by in-line inspection (ILI) tools. An FEA model is then built in commercially available software, ABAQUS, to create a dent profile that closely resembles the profile given by the ILI. The study in this paper assesses the effect of different indenter sizes on the stresses and strains within the dent and provides a recommendation to quantify the error between the ILI and FEA profiles. The process of matching a dent profile using FEA is compared to an existing analytical method to calculate strain, the equations proposed in ASME B31.8. The FEA results were found to be more conservative than the strains calculated using ASME B31.8.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".