Structural Designing of Supersonic Swirling Devices Based on Computational Fluid Dynamics Theory
Bibliographic record
Abstract
The supersonic swirling device is a new apparatus that can be used for natural-gas liquefaction. The structure of the supersonic swirling device has an important impact on the liquefaction efficiency. Therefore, this study presents a structural design method for supersonic cyclones based on CFD theory. Using the production parameters of a liquefied natural gas (LNG) peak-shaving station as the study case, a detailed design and design comparison of each part of the supersonic swirling separator are carried out. An optimum LNG supersonic swirling separator design was obtained. To ensure that the designed supersonic swirling separator achieved better liquefaction effectiveness, it was ascertained that no large shockwaves were generated in the de Laval nozzle, the pressure loss on the swirler was small, and the swirler was able to produce a large centripetal acceleration. The opening angle of the diffuser and the length of the straight tube were designed considering the location at which normal shockwaves were generated. The location at which shockwaves are generated and the friction effect are important parameters that determine the gap size. With this design guidance, the optimal structural dimensions of the supersonic swirling device for a given processing capacity were determined as follows: a swirler with six vanes and an 8 mm wide channel; a 10D-long straight tube, an opening angle of 20° between the straight tube and the divergent section, and a gap size of 2 mm. Compared with “Twister II”, the new device has better liquefaction efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".