Probabilistic assessments of running ductile fractures in dense-phase and supercritical CO2 pipelines
Bibliographic record
Abstract
Running ductile fracture (RDF) is a severe failure mode of high-pressure pipelines. Dense-phase and supercritical CO 2 pipelines are particularly susceptible to RDF due to the unique characteristics of the depressurization process resulting from a fracture of the pipeline. The present study carries out probabilistic analyses of RDF in dense-phase and supercritical CO 2 pipelines. The well-known Battelle two-curve method is employed to establish the limit state function for RDF by comparing the arrest pressure with the saturation pressure. The arrest pressure is computed using the Battelle through-wall crack model with the adjustment recently proposed in the literature. The saturation pressure is computed based on the one-dimensional isentropic decompression assumption and a rigorous equation of state. The first-order reliability method is employed to evaluate the probabilities of RDF in hypothetical CO 2 pipelines designed per DVN-RP-F104 with representative pipe attributes and initial operating conditions by considering uncertainties in the relevant pipe geometric and material properties. The analysis results indicate that the probability of RDF can vary by several orders of magnitude depending on the pipe attributes and initial operating conditions. Furthermore, the results shed light on the key uncertainties associated with the pipe geometric and material properties that influence the probability of RDF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".