PSV Experimental Study on Supersonic Shock Atomization Nozzle for Gas Well
Bibliographic record
Abstract
"Liquid accumulation in gas well" could be observed in the natural gas well production. According to the principle of supersonic shock atomization technology, the particle shadow velocimetry (PSV) is used to compare and analyze the atomization performance of the Laval nozzle and the straight-hole nozzle under different inlet pressure conditions. By simultaneously measuring the droplet size and velocity using digital image processing algorithm, the effectiveness and feasibility of the Laval nozzle atomization technology is verified. The results show that after the gas phase is accelerated by the nozzle, it can promote the droplets to break into smaller droplets and thus enhance atomization. Using PSV technology, it is proved that the atomization performance of the Laval nozzle is better than that of the straight-hole nozzle. The droplets get more kinetic energy as the inlet gas phase pressure increases, and after colliding with the high-velocity gas, the droplets average size is further reduced. A tracer particle with great following performance and distinguished particle image should be added to the gas phase in order to attain the flow field distribution of the gas phase and the liquid phase. The study can provide technical support for the liquid accumulation elimination of low-production gas wells.
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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) | 0.002 | 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".