Fluid-Structure Interaction Analysis of Running Ductile Fractures in CO2 Pipelines With Toroidal Ring Crack Arrestors
Bibliographic record
Abstract
Abstract The increasing need for carbon capture, utilization, and storage to mitigate greenhouse gas emissions has heightened interest in the safe transportation of carbon dioxide (CO2) through pipelines. CO2 is preferably transported in its dense phase or supercritical state. However, dense-phase CO2 pipelines are particularly susceptible to running ductile fracture due to the unique decompression characteristics during an accidental release, which can lead to catastrophic pipeline failure if not effectively controlled. The objective of this study is to investigate the effectiveness of toroidal ring crack arrestors for preventing running ductile fracture in dense-phase CO2 pipelines by carrying out the fluid-structure interaction analysis to simulate the running ductile fracture process. The coupled Eulerian-Lagrangian approach is employed to capture the interaction between crack propagation and CO2 decompression. The GERG-2008 equation of state is incorporated in the fluid decompression model, while cohesive zone model is used to simulate the fracture extension. Toroidal ring crack arrestors are placed externally around the pipe circumference; key design variables considered in the present study include the number of rings and their spacing at a given location. Parametric fluid-structure interaction analyses are carried out to simulate running ductile fracture in a hypothetical dense-phase CO2 pipeline with representative pipe attributes by considering a typical fluid composition (i.e. CO2-rich mixtures with impurities). The analysis results shed light on the effectiveness of the toroidal ring arrestors for preventing running ductile fracture in dense-phase CO2 pipelines and provide insights into the optimal design parameters for such arrestors. This study further demonstrates the feasibility and advantages of using the sophisticated fluid-structure interaction model to assess and improve the structural integrity of dense-phase CO2 pipelines.
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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 | 0.001 | 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".