Comparative Analysis of Running Ductile Fractures in Dense-Phase and Supercritical CO2 Pipelines Designed Per DNV-RP-F104 and ISO-27913
Bibliographic record
Abstract
Abstract Transmission pipelines carrying dense-phase and supercritical CO2 are susceptible to running ductile fractures (RDF). This vulnerability arises from the unique thermodynamic characteristics of dense-phase and supercritical CO2 during its release from an orifice in the pipeline. Dense-phase and supercritical CO2 pipelines operate at high pressures, and depressurization resulting from a through-wall fracture can lead to a phase change in CO2. The present study investigates the differences between two well-known standards DNV-RP-F104 and ISO-27913 in terms of the minimum wall thickness and Charpy v-notch (CVN) impact energy required for the RDF control for a set of analysis cases representative of dense-phase and supercritical CO2 pipelines. The prediction of RDF is commonly carried out using the Battelle two-curve method (BTCM). Given the long plateau in the decompression wave speed curve that is typical of CO2 pipelines, a simplified BTCM is employed in this study for the RDF assessment by comparing the arrest pressure with the saturation pressure. The arrest pressure is computed using the Battelle through-wall crack model with modifications based on full-scale experimental data reported in the literature. The open-source tool RAMDECOM is used to compute the saturation pressure for a given set of initial operating pressure and temperature based on the one-dimensional isentropic decompression assumption and rigorous equation of state. It was observed that the wall thickness and CVN impact energy values determined per ISO-27913 are higher than those determined per DNV-RP-F104, suggesting a more conservative design based on ISO-27913. DNV-RP-F104 emerged as more straightforward for designing the analysis cases considered in the present study, whereas ISO-27913 necessitated adjustments in the calculated wall thickness to adhere to its maximum CVN impact energy requirement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".