An unconventional supersonic liquefied technology for natural gas
Bibliographic record
Abstract
We proposed a novel supersonic liquefied technology to liquefy the natural gas to LNG liquids. The Peng-Robinson equation of state combined with a thermodynamic process modeling package was employed to calculate the gas dynamics parameters in the supersonic liquefied process. The method to intensify the liquefied process was also discussed. The results show that natural gas expands to supersonic velocities with leading to the low pressure and temperature of 1611.5 kPa and -118.86 ˚C at the nozzle exit, respectively. The pressure-temperature (P-T) curves remaining in the dense phase region indicates that the supersonic liquefied apparatus can successfully liquefy the natural gas to LNG liquids. A large expansion ratio improves the performance of a supersonic liquefied apparatus since natural gas is expanded further. Inserting a long constant conduit between the Laval nozzle and diffuser deepens the supersonic liquefied process.
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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 | 0.000 |
| 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 | 0.001 |
| 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".