Numerical simulation of ductile fracture in polyethylene pipe with continuum damage mechanics and Gurson-Tvergaard-Needleman damage models
Bibliographic record
Abstract
A phenomenon-based hybrid approach of experimental testing and finite element simulations is used to describe the fracture behavior of pipe-grade polyethylene. The experimental testing adopts a modified D-split test method to stretch the pipe ring (notched pipe ring) specimens that have symmetric, double-edged flat notches along the pipe direction. Two series of experimental testing were conducted: (1) monotonic loading till fracture and (2) monotonic loading to a predefined strain level, keeping constant displacement for a period of time, and then unloaded. Crosshead speeds of 0.01, 1, and 100 mm/min were used in both series of tests. Likewise, two series of finite element simulation were conducted to establish the constitutive equations, either with or without considering damage evolution during the deformation process. The constitutive equation without the consideration of damage was established using results from the first series of experimental testing, and that with damage was inspired from the second series which showed the decrease in unloading modulus with the increase of crosshead speed or the predefined strain level. The results show that with the consideration of damage evolution, the constitutive equations enable the finite element simulation to determine the whole stress–strain relationship during both necking and fracture processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".