Teaching-learning-based optimization of an ultra-broadband parallel sound absorber

Nansha Gao, Baozhu Wang, Kuan Lu, Hong Hou

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

The average sound-absorption coefficient of an ultra-broadband parallel sound absorber, between 0 and 2000 Hz, is optimized in certain conditions. This is accomplished using a teaching–learning-based optimization algorithm, where the geometric parameters are used as optimization variables. The optimized ultra-broadband parallel sound absorber has an average sound-absorption coefficient of 0.9255. The surface impedance of its structure is almost perfectly matched with the surrounding air – thanks to the optimized lengths of both the lateral and micro-perforated plates. The effective-medium model, the transfer-matrix method, and the finite-element method are applied and show consistent results. The acoustic-impedance tube measurements confirm that the optimized ultra-broadband parallel sound absorber shows high sound-absorption from 200 to 1715 Hz. The investigated passive acoustic design opens new possibilities for new absorber types in the future.

Original languageEnglish
Article number107969
JournalApplied Acoustics
Volume178
DOIs
StatePublished - Jul 2021

Keywords

  • Broadband sound absorption
  • Effective medium model
  • TLBO algorithm
  • Transfer matrix method

Fingerprint

Dive into the research topics of 'Teaching-learning-based optimization of an ultra-broadband parallel sound absorber'. Together they form a unique fingerprint.

Cite this