Study on the phase transition of high-speed natural gas flow in a nozzle by numerical method
Bibliographic record
Abstract
Supersonic swirling natural gas separation is a revolution in natural gas processing technology compared with traditional low-temperature separation technology.In the supersonic swirling separator,the supersonic low-temperature stream with small liquid droplets is formed when the natural gas passes through Laval nozzle and does adiabatic expansion.The phase transition in the nozzle is the key to the separation of natural gas.Based on the theory of phase transition,gas dynamics and the state equation of real gas,the mathematical model of high-speed natural gas flow with phase transition in Laval nozzle is established and the flow characteristics of the natural gas flow are investigated.The starting points of the phase transition and the mass ratio of condensed water to vapor at the outlet of the nozzle are calculated under different inlet conditions;the relations between the starting points of the phase transition and the mass ratio of condensed water to vapor and inlet pressure at constant inlet temperature are also analyzed.The results indicate that,the starting points of the phase transition move forward and the mass ratio of condensed water to vapor increases with the increase of the inlet pressure.The factors of influencing the normal operation of the separator are discussed and a method for predicting the normal operating pressure range of the supersonic swirling natural gas separator is established.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".