Prediction of threshold von-mises stress distribution of the sections of oil pipeline steel with internal corrosion defects using finite element analysis
Bibliographic record
Abstract
The current work presents a finite element analysis (FEA) based investigation of the structural steel pipe with internal corrosion defects. A total of 27 different geometrical conditions for internal corrosion defect were considered using 3 different internal pressures of 2.2 MPa, 4.5 MPa, and 6 MPa. The validation of the FEA model was carried out using the analytical solution for failure pressure using radial and hoop stresses. The failure pressure of the uncorroded pipe was 11.5 MPa. In contrast, for pipe with internal corrosion defect having the largest defect depth (1.7 mm), largest length (454 mm), and sharpest geometry (width of 26 mm), the failure pressure from FEA was 6 MPa. The remaining strength at this boundary condition was 0.521. The radial stress influences the strain in wall thickness which was 8.8 mm and much less as compared to other dimensions of pipeline which diminishes the material's ability to resist the failure pressure. The Von-Mises stress accumulation inside the interface increases the stress intensity (K) distribution at the vicinity of the internal corrosion defect geometry vis-à-vis lowers the K-distribution just outside of the internal corrosion defect. The largest factor of safety (FOS) of 2.11 was obtained at threshold boundary conditions considering fatigue limit as the optimum stress. It is then suggested that the FOS for the "break-before-leak" leak model can be anywhere between 2.11 to 1.45 and hence the pipeline cannot burst into rapture.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".