Analysis of Atomization Performance of Linear Laval Nozzle under Varied Water Pressures Based on VOF and DPM Models
Bibliographic record
Abstract
Particulate matter from coal and stone operations is a primary air pollution source. The traditional nozzle requires high-pressure conditions, and the atomization droplets are large and uneven. This paper aims to study a linear Laval nozzle and investigate the impact of water pressure on atomization performance. The volume of fluid (VOF) model and discrete phase model (DPM) of Fluent are used to simulate the internal and external fields of the nozzle and analyze the velocity, droplet size, and atomization angle. The results show that the optimized water pressure parameters are 0.1 MPa with an air pressure of 0.5 MPa. Droplets in the middle are smaller, while those on the sides are larger. Compared to traditional nozzles, the water pressure is reduced by over 90%, and the Sauter mean diameter (SMD) decreases by over 50%. Moreover, the theoretical spray angle increases by approximately 150%.
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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 | 0.001 |
| 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".