Evaluation of Burst Pressure of Corroded Pipe Segments Using Three-Dimensional Finite Element Analyses
Bibliographic record
Abstract
In this paper, three-dimensional finite element models are developed to simulate full-scale burst tests of corroded pipes containing multiple naturally occurring corrosion anomalies. Both the von Mises and Tresca yield criteria and associated flow rules are employed in finite element analysis (FEA). For the Tresca criterion, the corresponding constitutive model subroutine is developed and incorporated in the FEA. The accuracy of FEA is investigated by comparing the burst pressures observed in the tests and corresponding burst pressures predicted using FEA. The implications of using the von Mises and Tresca criteria for the accuracy of the predicted burst pressure are investigated. Sensitivity analyses are also carried out to investigate the impact on the predicted burst pressure due to the mesh density in the corroded region, characterization of the geometry of the corrosion cluster and different types of element (e.g. solid and shell elements) used in FEA. The results suggest that the Tresca criterion always underestimates the burst pressure and the von Mises yield criterion predicts the burst pressure accurately. This study demonstrates the feasibility of using high-fidelity FEA and the Tresca yield criterion to simulate full-scale burst tests of corroded pipes and therefore establish a large database of burst pressure capacities of corroded pipes that can be used to develop an accurate, practical burst pressure capacity model amenable to the pipeline integrity management practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".