@ARTICLE{Chang_Ying-Chun_Shape_2016,
 author={Chang, Ying-Chun and Cheng, Ho-Chih and Min-Chie Chiuminchie and Chien, Yuan-Hung},
 volume={vol. 41},
 number={No 1},
 journal={Archives of Acoustics},
 pages={43-53},
 howpublished={online},
 year={2016},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={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.},
 type={Artykuły / Articles},
 title={Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM,Neural Networks, and GA Method},
 URL={http://www.journals.pan.pl/Content/101325/PDF/aoa-2016-0004.pdf},
 doi={10.1515/aoa-2016-0004},
 keywords={boundary element method, plenum, centre-opening baffle, polynomial neural network model, group method of data handling, optimisation, genetic algorithm},
}