Effects of Argon as the primary impurity in anthropogenic carbon dioxide mixtures on the decompression wave speed
Bibliographic record
Abstract
In order to determine the material fracture resistance necessary to provide adequate control of the propagation of ductile fracture in a pipeline, a knowledge of the decompression wave speed (W) following the quasi‐instantaneous formation of an unstable, full‐bore rupture is necessary. The thermodynamic and fluid dynamics background of such calculations is understood, but predictions based on specific equations of state need to be validated against experimental measurements. A program of tests has been conducted using a specially constructed shock tube to determine the impact of impurities on W in carbon dioxide (CO2), so that the results can be compared to two existing theoretical models. In this paper, data and analysis results are presented for four tests involving simulated anthropogenic CO2 mixtures containing Argon (Ar) as the primary impurity. These mixtures represent typical oxy‐fuel CO2 capture technology with or without a purification train. Comparisons of the experimentally obtained W with predictions by two commonly used equation of state (EOS) models were also made. Generally, the GERG‐2008 EOS exhibits better overall performance than the Peng‐Robinson (PR) EOS when compared to the experimental results. The bubble point curve as predicted by GERG‐2008 was always at a higher pressure than that predicted by PR for these mixtures. This resulted in the plateau pressure prediction by GERG‐2008 being higher in cases where the isentropes intersect the phase envelope on the bubble point side. An example of pipeline material toughness required to arrest ductile fracture is presented which shows that prediction by GERG‐2008 is more conservative, and is therefore recommended.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".