Optimum design of perforated plug mufflers using a neural network and a genetic algorithm

2008 ◽  
Vol 223 (4) ◽  
pp. 935-952 ◽  
Author(s):  
Y-C Chang ◽  
M-C Chiu ◽  
M-M Cheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Noise Reduction ◽  
Optimum Design ◽  
Transmission Loss ◽  
Real Data ◽  
Wave Theory ◽  
Optimum Process ◽  
Design Data

Research on new techniques of perforated plug silencers has been well addressed. Most researchers have explored noise reduction effects based on a pure plane wave theory. However, the maximum noise reduction of a silencer under a space constraint, which frequently occurs in engineering problems, is rarely addressed. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to save the design time during the flexible optimum process, a simplified mathematical model of a muffler is constructed with a neural network with a series of real data — input design data (muffle dimensions) and output data (theoretical sound transmission loss (STL)) were approximated by a theoretical mathematical model (TMM) in advance. To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to various parameter sets and pure tones (500, 1000, and 2000 Hz) are exemplified and discussed. Before the GA operation is carried out, the approximation between TMM and real data is checked. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM conform. Optimal results reveal that the maximum amount of the STL can be optimally obtained at the desired frequencies. Consequently, the optimum algorithm proposed in this study can provide an efficient method to develop optimal silencers in industry.

Application of Neural Network and Genetic Algorithm to the Optimum Design of Perforated Tube Mufflers

2009 ◽  
Vol 25 (3) ◽  
pp. N7-N16 ◽  
Author(s):  
M.-C. Chiu ◽  
Y.-C. Chang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimum Design ◽  
Transmission Loss ◽  
Research Work ◽  
Sound Transmission ◽  
Optimum Process ◽  
Sound Transmission Loss

AbstractResearch on new techniques of perforated silencers has been well addressed. However, the research work on shape optimization for a volume-constrained silencer within a constrained machine room is rare. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to simplify the optimum process, a simplified mathematical model of the muffler is constructed with a neural network using a series of input design data (muffle dimensions) and output data (theoretical sound transmission loss) obtained by a theoretical mathematical model (TMM). To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to pure tones (500, 1000, 2000Hz) are exemplified and discussed.Before the GA operation can be carried out, the accuracy of the TMM is checked by Crocker's experimental data. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM confirm.The results reveal that the maximum value of the sound transmission loss (STL) can be optimally obtained at the desired frequencies. Consequently, it is obvious that the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers.

Numerical Assessment of Multi-Splitter Mufflers Using Neural Networks, the Boundary Element Method, and the Genetic Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1049-1055 ◽  
Author(s):  
Min Chie Chiu
Keyword(s):  
Genetic Algorithm ◽  
Noise Reduction ◽  
Boundary Element ◽  
Matrix Method ◽  
Transmission Loss ◽  
Real Data ◽  
Element Model ◽  
Design Data ◽  
Input Design ◽  
The Cost

Recently, research on new mufflers lined with sound-absorbing material has been addressed in the industrial field. On the basis of the transfer matrix method and the stiffness matrix method, most researchers have explored noise reduction effects. Yet, the maximum noise reduction of a compact silencer equipped with sound-absorbing splitters within a constrained space, which often occurs in modern industries, has been ignored. Therefore, the optimum design of mufflers becomes essential. In this paper, a one-chamber muffler equipped with multiple sound-absorbing panels within a fixed length is assessed. In order to facilitate the assessment of optimal mufflers having multiple sound-absorbing splitters, an approximated simplified objective function (OBJ) is established in advance by linking the boundary element model (BEM) with a polynomial neural network fitted with a series of real data, input design data (muffler dimensions) and output data obtained by BEM simulation. To assess the optimal mufflers, a genetic algorithm (GA) is applied. Before the GA operation can be carried out, the accuracy of the mathematical models must be checked using the experimental data. On the basis of the fixed total thickness of the splitters, the open area of the flowing channel can be assured. Therefore, not only the influence of the backpressure can be minimized, but also the cost of the sound absorbing splitters can be economically saved. Optimal results reveal that the maximum value of the sound transmission loss (STL) can be improved at the targeted frequencies. Consequently, the optimum algorithm proposed in this study provides an efficient way to find a better silencer for industry.

Shape optimization of mufflers hybridized with multiple connected tubes using the boundary element method, neural networks, and genetic algorithm

2010 ◽  
Vol 224 (4) ◽  
pp. 901-913 ◽  
Author(s):  
Y-C Chang ◽  
M-C Chiu ◽  
L-W Wu
Keyword(s):  
Genetic Algorithm ◽  
Noise Reduction ◽  
Boundary Element ◽  
Matrix Method ◽  
Transmission Loss ◽  
Real Data ◽  
Element Model ◽  
Design Data ◽  
Phase Cancellation ◽  
Input Design

Recently, research on new mufflers hybridized with connected curved tubes using phase cancellation techniques has been well addressed in the industrial field. Most researchers have explored noise reduction effects based on the transfer matrix method and the stiffness matrix method. However, the maximum noise reduction of a silencer within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of mufflers becomes an essential issue. In this article, two kinds of phase-cancellation mufflers (a two-connected tube and a three-connected tube) within a fixed length are assessed. In order to speed up the assessment of optimal mufflers hybridized with multiple connected curved tubes, a simplified objective function (OBJ) is established by linking the boundary element model (BEM; developed by the commercialized software SYSNOISE) with a polynomial neural network fitted with a series of real data: input design data (muffler dimensions) and output data approximated by BEM data in advance. To assess the optimal mufflers, a genetic algorithm is applied. Optimal results reveal that the maximum value of the sound transmission loss can be improved at the desired frequencies. Consequently, the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers for industry.

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

2016 ◽  
Vol 41 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Ying-Chun Chang ◽  
Ho-Chih Cheng ◽  
Min-Chie Chiu ◽  
Yuan-Hung Chien
Keyword(s):  
Noise Reduction ◽  
Boundary Element ◽  
Transmission Loss ◽  
Real Data ◽  
Element Model ◽  
Shape Optimisation ◽  
Design Data ◽  
Input Design ◽  
Speed Up ◽  
Fixed Space

Abstract 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.

scholarly journals An approach to the optimal timber transport scheduling

2020 ◽  
Vol 164 ◽  
pp. 03019 ◽  
Author(s):  
Anton Shabaev ◽  
Anton Sokolov ◽  
Alexander Urban ◽  
Dmitry Pyatin
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Algorithm ◽  
Decomposition Method ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Real Data ◽  
Shop Scheduling ◽  
Wood Harvesting ◽  
Timber Transport

An approach to the optimal timber transport scheduling is described in the paper. A description of this problem is given, a multi-criteria mathematical model is created. It is noted that the problem belongs to the class of General vehicle routing problems (GVRP) associated with the job-shop scheduling. A hybrid algorithm for solving this problem based on the decomposition method using the simplex method and the genetic algorithm is developed. Testing of the proposed approach using real data from wood harvesting enterprises showed its effectiveness. The algorithm was implemented in “Opti-Wood” decision support system for wood harvesting planning and management, developed by Opti-Soft company (Russia).

Genetic Algorithm Optimization on a Venting System With Three-Chamber Hybrid Mufflers Within a Constrained Back Pressure and Space

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Min-Chie Chiu
Keyword(s):  
Genetic Algorithm ◽  
Pressure Drop ◽  
Transmission Loss ◽  
Wave Theory ◽  
Broadband Noise ◽  
Back Pressure ◽  
System Matrix ◽  
Algorithm Optimization ◽  
Relief Valve ◽  
Noise Abatement

Recently, research on new techniques for dissipative mufflers in dealing with the higher frequencies has been addressed. However, the shape optimization of hybrid mufflers in reducing broadband noise within a constrained space as well as a pressure-drop limit which are both concerned with the necessity of operation and system venting in practical engineering work was rarely tackled. Therefore, this study will not only analyze the sound transmission loss (STL) of a space-constrained multichamber hybrid muffler but also optimize the best design shape under a specified pressure drop. In this paper, the generalized decoupling technique and plane wave theory used to solve the coupled acoustical problem of perforated mufflers with/without sound absorbing material are presented. The four-pole system matrix used to evaluate acoustic performance is also introduced in conjunction with a genetic algorithm (GA). A numerical case for eliminating a broadband venting noise emitted from a pressure relief valve using four kinds of hybrid mufflers is also introduced. To verify the reliability of the GA optimization, optimal noise abatement for a pure tone (1000 Hz) is exemplified. Before the GA operation can be carried out, the accuracy of the mathematical models need to be checked using the experimental data. The optimal result in eliminating broadband noise reveals that the overall noise reductions with respect to various mufflers under a maximal allowable pressure drop of 100 Pa can achieve 62.6, 54.8, 32.3 and 87.8 dB. Consequently, the approach used for the optimal design of the multichamber hybrid mufflers under space and back pressure constrained conditions is indeed easy and quite effective.

Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network

2007 ◽  
pp. 80-100
Author(s):  
Eysa Salajegheh ◽  
Ali Heidari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Dynamic Analysis ◽  
Optimum Design ◽  
Low Frequency ◽  
Time History ◽  
Wavelet Neural Network ◽  
Dynamic Responses ◽  
Frequency Content ◽  
Fast Wavelet

Optimum design of structures for earthquake induced loading is achieved by a modified genetic algorithm (MGA). Some features of the simulated annealing (SA) are used to control various parameters of the genetic algorithm (GA). To reduce the computational work, a fast wavelet transform is used. The record is decomposed into two parts. One part contains the low frequency of the record, and the other contains the high frequency of the record. The low-frequency content is used for dynamic analysis. Then using a wavelet neural network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure becomes unnecessary in the process of optimisation. The wavelet neural networks have been employed as a general approximation tool for the time history dynamic analysis. A number of structures are designed for optimal weight and the results are compared to those corresponding to the exact dynamic analysis.

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