Modeling the effect of backfill on dynamic fracture propagation in steel pipelines
Bibliographic record
Abstract
In this paper, dynamic ductile fracture propagation simulations were conducted to study the use of smoothed particle hydrodynamics (SPH) for modeling the effects of backfill in pipeline burst simulations. The effect of SPH parameters on fracture velocity was studied using the Battelle Two-Curve Method (BTCM) approach of decoupling mechanics and gas decompression but characterizing propagation toughness by crack tip opening angle (CTOA) rather than Charpy absorbed energy (CVN). The backfilled pipe model was developed and studied using the commercial finite element code ABAQUS 2017. Ductile fracture propagation was simulated using a shell based constant CTOA model. The current study examined the numerical aspects of applying SPH through comparing results with literature. The effects of particle size, various backfill material properties, and backfill depth on the fracture velocity were examined. It was found that the particle size had a minor effect on the fracture velocity and should be selected in proportion to the diameter of the pipe being examined. The numerical study showed that increasing the density and shear modulus of the backfill material resulted in a reduction of the fracture velocity. The effect of backfill depth up to 1.4 m was also examined numerically and found to have little effect on the fracture velocity, agreeing well with literature. The present study illustrates the sensitivity of the fracture velocity to the various parameters used in SPH models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".