Numerical studies on constrained venting system with reactive mufflers by GA optimization

Yeh, Long-Jyi; Chang, Yeong‐Hwa; Chiu, Min‐Chie

doi:10.1002/nme.1476

Cited by 31 publications

(17 citation statements)

References 6 publications

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“…By using the formula of Equations (5a), (5b), (11a), (11b), (13), the objective function used in SA optimization with respect to each type of muffler was established.…”

Section: Objective Functionmentioning

confidence: 99%

“…In previous work [11][12][13], the shape optimizations of straight simple-expansion mufflers have been discussed. In order to efficiently improve the performance of the noise control device, two kinds of mufflers-a one-chamber perforated plug muffler and a one-chamber perforated non-plug muffler-arrived at by using the novel scheme of simulated annealing (SA) are presented.…”

Section: Introductionmentioning

confidence: 99%

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Shape optimization of one‐chamber perforated plug/non‐plug mufflers by simulated annealing method

Chang

Chiu

2007

Numerical Meth Engineering

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SUMMARYTo economically and efficiently lower the venting noise, the development of a high-quality muffler with compact volume has become crucial in the modern industrial field. The research work of shape optimization of straight silencers in conjunction with plug/non-plug perforated ducts which may noticeably increase the acoustical performance is rarely addressed; therefore, the main purpose of this paper is not only to analyze the sound transmission loss (STL) of a one-chamber plug/non-plug perforated muffler but also to optimize the best design shape under a limited space. In this paper, on the basis of plane wave theory, the four-pole system matrix in evaluating the acoustic performance is derived by using the decoupled numerical method. Moreover, a simulated annealing (SA) algorithm searching for the global optimum by imitating the softening process of metal has been adopted during the muffler's optimization. To assure SA's correctness, the STL's maximization of one-chamber perforated plug mufflers at a targeted frequency of 500 Hz is exemplified first. Furthermore, a numerical case in dealing with a broadband noise emitted from a fan by using one-chamber plug/non-plug mufflers has been introduced and fully discussed. To achieve a better optimization in SA, various SA parameter sets of cooling rate and iteration parameter values were used. Before the SA operation can be carried out, the accuracy check of the mathematical models with respect to plug/non-plug perforated mufflers has to be supported by experimental data. The optimal result in eliminating broadband noise reveals that the muffler with a plug acoustical mechanism has a better noise reduction than that of a non-plug muffler. Consequently, the approach used for the optimal design of the noise elimination proposed in this study is certainly easy, economical, and quite effective.

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“…By using the formula of Equations (5a), (5b), (11a), (11b), (13), the objective function used in SA optimization with respect to each type of muffler was established.…”

Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shape optimization of one‐chamber perforated plug/non‐plug mufflers by simulated annealing method

Chang

Chiu

2007

Numerical Meth Engineering

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“…To overcome this draw-back, a compact silencing device connected at the inlet/outlet is used. Research on simple shaped mufflers has been examined using theoretical derivations (Chang et al, 2004;Yeh et al, 2006). However, because of the complicated geometry of the commercial reciprocating compressor, the acoustical assessment using theoretical analysis is not accessible.…”

Section: Introductionmentioning

confidence: 99%

Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method

Chang

Chiu

Xie

2017

Archives of Acoustics

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Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer.Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.Keywords: finite element method; polynomial neural network model; genetic algorithm; group method of data handling; reciprocating compressor; optimization. NotationsIn this paper the following notations are used:

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“…Yet, the need to investigate the optimal acoustical plenum within a constrained space is rarely approached. In previous work Yeh et al, 2004;Chang et al, 2005a;Yeh et al, 2006), the shape optimisations of reactive mufflers have been discussed using a simple theoretical model in conjunction with a genetic algorithm (GA). However, the mathematical model for a multi-chamber plenum lined with sound absorbing material is complicated and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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

Chang¹,

Cheng²,

Chiu³

et al. 2016

Archives of Acoustics

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Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. 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.Keywords: boundary element method; plenum; centre-opening baffle; polynomial neural network model; group method of data handling; optimisation; genetic algorithm. NotationsThroughout the paper the following notations are used:bit -bit length of chromosome, itermax -maximum iteration during GA optimisation, L 1 , L 2 -design parameters of a two-chamber plenum [m], LL 1 , LL 2 -design parameters of a three-chamber plenum [m], pc -crossover ratio, pm -mutation ratio, pop -number of population, T L -sound transmission loss [dB].

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Numerical studies on constrained venting system with reactive mufflers by GA optimization

Cited by 31 publications

References 6 publications

Shape optimization of one‐chamber perforated plug/non‐plug mufflers by simulated annealing method

Shape optimization of one‐chamber perforated plug/non‐plug mufflers by simulated annealing method

Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method

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

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