Study on structure optimization of supersonic aerodynamic spray device and synergistic control effect of dust and noise
Bibliographic record
Abstract
Supersonic coaxial air spray dust removal technology is good in the treatment of respirable dust. It has the advantages of high spray concentration, small droplet size and fast movement speed, but it will bring serious noise pollution, especially high-frequency noise. In order to solve this problem, the research group optimized the structure of the technical device. The velocity distribution and sound distribution of the flow field in Laval nozzle before and after optimization were studied by COMSOL Multiphysics software, and the feasibility was verified. Combined with the experiment, YSD130 noise analyzer, laser particle size analyzer and dust sampler were used to measure the spray noise characteristics and change rules under different pneumatic pressure and water flow, as well as the droplet size and dust removal efficiency of the two nozzles. The results show that in Laval nozzle, the sound pressure level of both nozzles decreases gradually along the central axis. The thickness of supersonic layer of optimized nozzle is smaller than that of optimized nozzle, and the corresponding sound pressure level is smaller. When the water flow rate is 10 L/h, with the increase of aerodynamic pressure, the sound pressure level of high frequency band at the sound source of two nozzles shows an increasing trend, and the trend of increasing first and then decreasing at the propagation direction changes to an increasing trend before optimization. Compared with the optimized nozzle, the optimized nozzle sound pressure level at the sound source is reduced by about 16.7%, the peak sound pressure level is reduced by 8.5%−9.3%, and the sound pressure level at the propagation direction is reduced by about 18%. When the pressure is 0.4 MPa, with the increase of water flow, the sound pressure level of the nozzle before optimization increases at the sound source, and increases first and then decreases at the propagation direction. After optimization, the sound pressure level of the nozzle at the sound source increases first and then decreases, the sound pressure level at the middle and high frequency band increases, and the sound pressure level at the propagation direction decreases. Compared with the optimized nozzle, the optimized nozzle sound pressure level at the sound source is reduced by about 9.8%, the peak sound pressure level is reduced by 19.2%−20.9%, and the sound pressure level at the propagation direction is reduced by about 12.7%. When the pressure is 0.4 MPa and the water flow rate is 12 L/h, the particle size of the droplets with 50% of the number of droplets in the two nozzles is about 11 μm, which can effectively capture micron dust. With the increase of test time, the dust removal effect increased linearly, and the dust removal efficiency of the two nozzles reached more than 84%. The research not only ensures the dust removal effect, but also reduces the noise pressure level in the atomization process through structural optimization, which provides theoretical and technical support for the safe application of supersonic aerodynamic dust removal spray and the collaborative control of dust and noise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".