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.

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.

Optimization of Rectangular Multi-Chamber Plenums Equipped with Multiple Extended Tubes Using the BEM, Neural Networks, and the Genetic Algorithm

2014 ◽  
Vol 30 (6) ◽  
pp. 571-584 ◽  
Author(s):  
Y.-C. Chang ◽  
M.-C. Chiu
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Transmission Loss ◽  
Focal Point ◽  
Analytical Data ◽  
Element Model ◽  
The Neural Network ◽  
Numerical Assessment ◽  
Pure Tones ◽  
Rectangular Tubes

AbstractThe focal point of this paper is to uncover, by analyzing the higher order wave effect, an improved mechanism for space-constrained rectangular plenums using a simplified objective function in conjunction with a genetic (GA). Three kinds of rectangular mufflers hybridized with extended tubes will be assessed: Plenum A: A two-chamber plenum equipped with an extended tube; plenum B: A three-chamber plenum with two extended tubes; and plenum C: A two-chamber plenum equipped with three extended tubes. In order to shorten the numerical assessment, a simplified objective function (OBJ) is established using a boundary element model (BEM) in conjunction with the neural network model (NNM). To expediently approach an optimal plenum, the best OBJ will be numerically searched using a genetic algorithm (GA). However, before the GA operation is performed, the accuracy of the BEM is verified using analytical data. And, because the simplified objective function (OBJ) is seen to be in agreement with the BEM, the numerical cases of sound elimination relative to the various parameter sets and pure tones (300, 750, and 1300Hz) can be carried out.Results reveal that the maximum value of the sound transmission loss (STL) can be accurately obtained at the desired frequencies. Additionally, the acoustical performance of the lower frequencies will be improved if the number of chambers and rectangular tubes are increased. However, the acoustical performance of the higher frequencies will decrease when the number of chambers and rectangular tubes are decreased. Consequently, the algorithms proposed in this study will efficiently develop optimal rectangular plenums with multiple rectangular extended tubes.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

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