Effect of inlet water vapor mass fraction on flow characteristics in Laval nozzle
Bibliographic record
Abstract
Abstract The Laval nozzle is an important component of the supersonic cyclone to achieve the change of gas–liquid two-phase, and the condensation characteristics of the Laval nozzle have an important influence on the separation performance of the supersonic cyclone. In this work, the effect of inlet water vapor mass fraction on the condensation characteristics in the Laval nozzle was investigated using numerical simulation and experimental methods by establishing a three-dimensional numerical model of air-water vapor supersonic condensation flow. The flow field structures in the Laval nozzle under different inlet water vapor mass fractions were investigated, including Mach number, pressure, and temperature and the effects of the inlet water vapor mass fraction on the liquefaction characteristics in the Laval nozzle were investigated. In addition, the droplet distribution in the Laval nozzle were also tested by a particle image velocimetry (PIV) experimental system. The comparison of simulation and experimental results indicates that the numerical model established in this work can effectively describe the real flow situation in the Laval nozzle. The results show that the inlet water vapor mass fraction has a little effect on the flow field structure in the Laval nozzle, and has the significant impact on the water vapor condensation characteristics. With increasing the inlet steam mass fraction from 5 % to 12.5 %, the nucleation rate, droplet number, and separation efficiency in the Laval nozzle increase to 4.05 × 10 21 kg −1 s −1 , 3.67 × 10 14 kg −1 , and 79.4 %, respectively, and when further increasing the inlet steam mass fraction to 15 %, these parameters decrease.
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 | 0.001 |
| 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 | 0.000 |
| 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".