Measurements of Decompression Wave Speed in Binary Mixtures of Carbon Dioxide and Impurities
Bibliographic record
Abstract
Shock tube tests were conducted on a number of binary CO2 mixtures with N2, O2, CH4, H2, CO, and Ar impurities, from a range of initial pressures and temperatures. This paper provides examples of results from these tests. The resulting decompression wave speeds are compared with predictions made utilizing different equations of state (EOS). It was found that, for the most part (except for binaries with H2), the GERG-2008 EOS shows much better performance than the Peng–Robinson (PR) EOS. All binaries showed a very long plateau in the decompression wave speed curves. It was also shown that tangency of the fracture propagation speed curve would normally occur on the pressure plateau, and hence, the accuracy of the calculated arrest toughness for pipelines transporting these binary mixtures is highly dependent on the accuracy of the predicted plateau pressure. Again, for the most part, GERG-2008 predictions of the plateau are in good agreement with the measurements in binary mixtures with N2, O2, and CH4. An example of the determination of pipeline material toughness required to arrest ductile fracture is presented, which shows that prediction by GERG-2008 is generally more conservative and is therefore recommended. However, both GERG-2008 and PR EOS show much worse performance for the other three binaries: CO2 + H2, CO2 + CO, and CO2 + Ar, with CO2 + H2 being the worst. This is likely due to the lack of experimental data for these three binary mixtures that were used in the development of these EOS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".