Study on Supersonic Dehydration Efficiency of High Pressure Natural Gas
Bibliographic record
Abstract
Supersonic cyclone separator is a novel type of natural gas dewatering device that overcomes the shortcomings of traditional dewatering methods. In order to investigate the factors affecting the separation efficiency and improve the separation performance of the supersonic cyclone separator, the discrete particle model was employed in numerical calculation. On the basis of an accurate numerical model, the flow field of supersonic cyclone separator was analyzed, the trajectories of droplets were predicted, and the factors affecting the separation efficiency of droplets were investigated. The numerical results indicated that Laval nozzle could provide the necessary conditions for the condensation of water vapor. The swirler can throw droplets onto the wall or into the separator, both of which are foundations for realizing the separation of droplets. Droplets had three typical trajectories affected by centrifugal effect and inertia effect. The existence of a shock wave increases the swirl intensity of droplets, which is conducive to the separation of droplets. The diameter of droplets should be increased as much as possible in order to improve separation efficiency, and the gas–liquid area ratio should be about 45.25%, and the number of vanes should be 10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".