Fluid-Structure Interaction Modelling to Predict Rupture of Dense-Phase CO2 Pipeline
Bibliographic record
Abstract
Abstract Fracture resistance in a rupturing pipeline is influenced by the pressure exerted on the flaps of the pipeline by the decompressing fluid. Current engineering methodologies mainly treat the fracture resistance curve and the decompression curve as uncoupled. To capture the coupled interaction, the Fluid Structure Interaction technique known as the Coupled Eulerian Lagrangian (CEL) method in Abaqus was used to model pipeline rupture. Initially, the CO2 fluid is in the dense-phase state but can exist in both a liquid and gas (2-phase) state during decompression. The Equation of State (EOS) for a CO2 mixture was calculated using the GERG-2008 EOS in REFPROP. The resultant properties were then used to describe a tabulated EOS in Abaqus for the Eulerian (fluid) domain. A user defined EOS by means of direct equations or a table lookup process were also used for description of the EOS of CO2 mixtures. Isentropic conditions were assumed. The fracture response of the pipeline in the Lagrangian (structure) domain was described using the Crack Tip Opening Angle (CTOA) criterion either directly via a user subroutine or indirectly by informing parameters of damage mechanics models in which the effective plastic strain to failure depends upon stress triaxiality and Lode angle. Full-scale shock tube simulations in the Eulerian domain were performed to validate the EOS by comparison with experimental data. The shock tube simulations were able to capture the 2-phase pressure plateau commonly reported with CO2 decompression. CEL simulations of a pipeline buried in soil were compared to full-scale CO2 pipeline rupture data available in literature. The predictions agreed well with the experimental data, with a predicted crack velocity of about 100 m/s. The pressure profile predicted for CO2 will be discussed. The influence of shock waves will be addressed, as well as comparing CTOA versus fracture speed.
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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 | 0.000 |
| 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) | 0.000 | 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".