Research on noise reduction performance of supersonic power spraying based on multimaterial composite sound absorption
Bibliographic record
Abstract
Pneumatic spray dust reduction technology is widely used in dust pollution control in the coal industry due to its advantages of high spray concentration, small droplet size, and fast movement speed. However, supersonic power spray dust reduction will be accompanied by severe high-frequency noise, which restricts the promotion of technology. To address this problem, this research develops a multi-material composite sound-absorbing device adapted to the high-frequency noise of supersonic power spray. It uses multi-field coupling simulations and experiments such as high Mach number and pressure acoustics to explore the noise characteristics of single- and multi-layer sound-absorbing chambers, verify the feasibility of the device, screen the optimal structure, and reveal the noise reduction mechanism. Research shows that the flow field velocity of the Laval nozzle in single- and multi-layer sound-absorbing chambers decreases outward along the central axis, and the flow field velocity inside the multi-layer chamber is even lower; the core area where sound energy is converted into heat energy is the sound-absorbing material in the inner layer near the nozzle. The three-dimensional network micropores of the porous fiber material can convert sound energy into heat energy and dissipate it, greatly reducing the sound pressure level of radial propagation. In a single-layer chamber, the noise reduction effect is optimal when the cavity diameter is 56 mm. The sound pressure level at the sound source is reduced by 10.9 % ∼ 13.4 %, and the sound radiation direction is reduced by 7.3 % ∼ 10.8 %. Under different materials, airgel has the best noise reduction effect, with corresponding reductions of 13.4 % and 10.8 %. Among the multi-layer chambers, composite method 5 has the best noise reduction effect, with a 21.2 % reduction at the sound source and a 12.4 % reduction in the sound radiation direction, which is better than the single-layer airgel chamber. When the aerodynamic pressure increases, the sound pressure level in each frequency band increases, and when the water flow increases, the sound pressure level in the middle and high frequency bands decreases. Under the same working conditions, the particle size of 50 % of the droplets is about 11 μm, and the dust reduction efficiency exceeds 88 % in 3 min. This study not only ensures the efficiency of atomization and dust reduction, but also reduces the high-frequency noise at the sound source to below the national standard (GB12348–2008) 85 dB, laying a theoretical and technical foundation for the collaborative control of dust and spray noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".