An Automatic Dent Assessment Tool Using Finite Element Method
Bibliographic record
Abstract
Abstract Dents are permanent plastic deformations of the pipeline that occur during pipeline construction and operations. Stress and strain concentration at dents may initiate cracks, which pose a threat to the integrity of the pipeline. Since formation of dents involves plastic deformation, a traditional depth-based assessment method cannot accurately capture the strain concentration, e.g. a sharp dent. Dent assessment methods have shifted to strain-based approach in recent years. Engineering Critical Assessment (ECA) procedures are evolving with the development of new methods to calculate the strains associated with a dent. It has been well established that three-dimensional (3D) elastic-plastic Finite Element Analysis (FEA) is the most accurate in the denting process simulation and dent strain calculation. However, FEA modelling of dents interacting with other threats can be extremely laborious and requires high level of FEA expertise. Moreover, continuum FEA is usually computationally expensive and imposes onerous demands for analysis efforts. This paper describes the process that was used to develop an automatic dent assessment tool via FEA. The tool is capable of simulating the denting process of a plain dent and a dent interacting with other threats (e.g., metal loss or gouge). The dent geometry and curvature in FEA are shown to agree well with captured In-line Inspection (ILI) caliper data. The tool also supports batch processing of numerous dents with a highly intelligent post-processing engine that automatically calculates von Mises equivalent strain, ductile failure damage indicator (DFDI) and strain limit damage (SLD) and outputs the results with visual contour plots. The Rainflow pressure cycling counting algorithm and Miner’s rule are incorporated in the tool to predict the fatigue life of the dent utilizing historical pressure data. Case studies are provided to demonstrate the effectiveness of the tool. The development of the tool greatly facilitates the ECA of dents allowing for accurate and efficient management of dents impacting TC Energy’s pipeline system.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".