Design and Evaluation of Modified Laval Nozzle for Atomization Performance
Bibliographic record
Abstract
Particulate matter generated during the mining, processing, and transportation of coal and stone materials has become one of the primary sources of air pollution, especially fine particles with small particle sizes. The traditional nozzle requires high-pressure conditions, and the atomization droplets are large and uneven. This study aims to design a nozzle with a simple structure and good atomization performance and to study the influence of nozzle structure parameters and operating conditions on atomization performance. First, a linear Laval nozzle was designed. Then, the VOF (Volume of Fluid) model and DPM (Discrete Phase Model) of Fluent are used to simulate the internal and external fields of the nozzle and analyze the velocity, pressure, droplet size, and atomization angle. Finally, the droplet size and angle of the Laval nozzle spray were measured by experiments, and comparisons with numerical analysis were also conducted. The results show that the flow inside the linear Laval nozzle can reach supersonic speed. The optimized water pressure parameters are 0.1 MPa, air pressure of 0.5 MPa, nozzle outlet diameter of 2.5 mm, and oscillator distance of 4 mm. Compared to traditional nozzles, the water pressure is reduced by over 90%, and the D (3,2) droplet diameter decreases by over 50%. Moreover, the spray angle increases by approximately 67.5%. Some fitted curves have been plotted, enabling the prediction of velocity and atomization angle within a specific range. The linear Laval nozzle studied in this research is easy to manufacture, exhibits excellent atomization performance, operates at low pressure, and is cost-effective. Particularly, a combination-type nozzle is proposed, which expands the atomization angle. The linear nozzles are applicable in diverse enclosed environments such as mines, construction sites, and processing plants. They can improve dust removal efficiency effectively, thus significantly contributing to human health and environmental protection.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".