Probabilistic Evaluation of Running Ductile Fracture Length in Dense-Phase CO2 Pipelines
Bibliographic record
Abstract
Abstract A running ductile fracture (RDF) in dense/supercritical-phase CO2 pipelines is a severe failure mode that can release large volumes of CO2 and poses risk to the public. Uncertainties exist in the final RDF length due to varying mechanical and geometric properties along the pipeline. This study uses a probabilistic approach to quantify the final crack length of RDFs in dense/supercritical phase CO2 pipelines by accounting for the inter- and intra-joint variability of key pipeline properties including Charpy-V Notch toughness, wall thickness, and yield strength, as well as these variables’ spatial correlations. A Monte Carlo simulation is developed to evaluate the cumulative distribution function (CDF) of the final RDF length in CO2 pipelines using probability distributions of the previously mentioned variables and a limit state function based on the Battelle two-curve method. 12 cases of CO2 pipelines with X65 grade steel and an outside diameter of 609.6 mm are investigated, including various operating pressures and temperatures, and three fluid compositions consisting of pure CO2 and CO2-rich mixtures with impurities. Two spatial correlation scenarios are considered to investigate the effect of spatial correlation on RDF length. CDFs are developed for each case and compared. The results show that spatial correlation can significantly increase RDF length and should be accounted for in probabilistic analyses. This study is useful for developing risk-based design codes to ensure adequate fracture control in CO2 pipelines.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".