Study on structure optimization of supersonic aerodynamic spray device and synergistic control effect of dust and noise
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».