Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM,Neural Networks, and GA Method

Journal title

Archives of Acoustics




vol. 41


No 1

Publication authors


boundary element method; plenum; centre-opening baffle; polynomial neural network model; group method of data handling; optimisation; genetic algorithm

Divisions of PAS

Nauki Techniczne


Archives of Acoustics is an English-language peer-reviewed quarterly journal publishing original research papers from all areas of acoustics and abstracts from some specialised acoustical conferences. It gives free internet access to its full content (abstracts of research papers) to current issues.

Archives of Acoustics, the peer-reviewed quarterly journal publishes original research papers from all areas of acoustics like:

  • acoustical measurements and instrumentation,
  • acoustics of musics,
  • acousto-optics,
  • architectural, building and environmental acoustics,
  • bioacoustics,
  • electroacoustics,
  • linear and nonlinear acoustics,
  • noise and vibration,
  • physical and chemical effects of sound,
  • physiological acoustics,
  • psychoacoustics,
  • quantum acoustics,
  • speech processing and communication systems,
  • speech production and perception,
  • transducers,
  • ultrasonics,
  • underwater acoustics.

Earlier issues are available on the old website


Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.


Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society


ISSN 0137-5075 ; eISSN 2300-262X