Simulation of Dynamic Ductile Failure in Pipelines
Bibliographic record
Abstract
A finite-element method computer simulation was constructed in order to assist in determining what material properties affect the resistance to dynamic ductile failure in pipelines. Such failure is caused by stable axial tearing that involves a substantial amount of plastic deformation, and is driven by the kinetic energy of the expanding gas. Various semi-empirical relationships exist in the literature to predict the toughness required for resistance to the propagation of a dynamic ductile failure, but these tend to be ineffective when applied to higher strength grades of steel. The present finite-element model is composed of two main sub-models. The gas decompression algorithm is based on analytical expressions and calculates the gas pressure throughout the pipe as the ductile fracture propagates. The material-response algorithm determines the behaviour of the material under the changing loading conditions. It simulates the material response, including rate-dependent yield as well as anisotropy of yield and work hardening. The model is validated by using comparisons with published data from the literature. This paper focuses on a description of the different components of the model and their interaction. In addition some observations from the various simulations are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".