Experimental analysis of the effect of water pressure on the atomization performance of a Linear Laval nozzle and comparison with numerical analysis
Bibliographic record
Abstract
Inhaling dust can lead to respiratory diseases, and dust accumulation in the workplace can pose fire and \nexplosion hazards. Traditional dust removal nozzles require high water pressure and produce large droplet \ndiameters. The Laval nozzle, utilizing a converging-diverging section to accelerate fluid to supersonic speeds, \nachieves finer droplets and a more concentrated particle size distribution. However, curved Laval nozzle is \ndifferent to manufacture. To study the effect of water pressure on the atomization performance of a Linear \nLaval nozzle, a laser particle analyzer and a camera were used to test the droplet size and atomization angle. \nThese results were compared with numerical analysis. The findings indicate that as the water pressure increases \nfrom 0.1 MPa to 0.5 MPa, the dropletsʼ Sauter Mean Diameter (SMD) increases almost linearly. At the same \ntime, the spray angle tends to decrease. Both experimental and numerical analyses show the same trend. At \na water pressure of 0.1 MPa, the atomization performance of the Linear Laval nozzle is optimal. Compared \nto traditional nozzles, the water pressure is significantly reduced, and the D(3,2) droplet diameter is notably \nsmaller. Moreover, the atomization angle is considerably increased. The spray effect has been significantly \nimproved
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".