On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm

Abstract: Porous material (PM) shows good sound absorption performance, however, the sound absorbing property of PM with different parameters are greatly different. In order to match the most suitable absorbing materials with the most satisfactory sound-absorbing performance according to the noise spectrum in different practical applications, multi-population genetic algorithm is used in this paper to optimize the parameters of porous sound absorbing structures that are commonly used according to the actual demand of noise reduction and experimental verification. The results shows that the optimization results of multi-population genetic algorithm are obviously better than the standard genetic algorithm in terms of sound absorption performance and sound absorption bandwidth. The average acoustic absorption coefficient of PM can reach above 0.6 in the range of medium frequency, and over 0.8 in the range of high frequency through optimization design. At a mid-to-high frequency environment, the PM has a better sound absorption effect and a wider frequency band than that of micro-perforated plate. However, it has a poor sound absorption effect at low frequency. So it is necessary to select suitable sound absorption material according to the actual noise spectrum.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm ; volume:20 ; number:1 ; year:2020 ; pages:122-132 ; extent:11
e-Polymers ; 20, Heft 1 (2020), 122-132 (gesamt 11)

Urheber
Wang, Yonghua
Liu, Shengfu
Wu, Haiquan
Zhang, Chengchun
Xu, Jinkai
Yu, Huadong

DOI
10.1515/epoly-2020-0014
URN
urn:nbn:de:101:1-2412151555231.906398266798
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:30 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Beteiligte

  • Wang, Yonghua
  • Liu, Shengfu
  • Wu, Haiquan
  • Zhang, Chengchun
  • Xu, Jinkai
  • Yu, Huadong

Ähnliche Objekte (12)