Issue 
Mechanics & Industry
Volume 19, Number 1, 2018



Article Number  101  
Number of page(s)  10  
DOI  https://doi.org/10.1051/meca/2017036  
Published online  24 August 2018 
Regular Article
Optimization of doublelayer sound absorber in a broadband frequency range using transfer matrix method and Evolution Strategies algorithm
Department of Marine Technology, Amirkabir University of Technology,
Tehran,
Iran
^{*} email: pghadimi@aut.ac.ir
Received:
11
June
2016
Accepted:
10
June
2017
Curtailment of the ambient noise level for providing a better living environment is immensely important. Accordingly, acoustic isolation via different combinations of porous materials is the most widely used passive soundproofing system. The present study focuses on the optimization of single and doublelayer absorbers in different frequencies. To this end, the transfer matrix and the Evolution Strategy (ES) method are firstly explained. Afterward, the optimization of single and doublelayer absorbers is considered for up to 10 parameters (material porosity, air gap, perforated plate characteristics among others) at 350 Hz frequency and has been compared with the results obtained through other methods (Genetic Algorithm among others). It has been illustrated that ES algorithm provides better optimization in this field. Subsequently, since the incident sound in most cases is a correlation of different frequencies, the broadband optimization of the single and doublelayer absorbers is considered in three frequency ranges (100–800 Hz, 800–1600 Hz, 1600–3000 Hz), with an increment of 1 Hz, for three different materials (polyester, fiber and foam). After the optimization, the resulting optimum parameters are presented in form of characteristics charts of the optimized materials for different frequency ranges, as a reference for material designers and manufacturers. Also, the absorption coefficient of all optimized cases are calculated and presented in range of 100 Hz to 3 kHz as a reference for the absorber selection for different situations. Finally, by presenting the improvement chart of double layer versus single layer combinations, it has been shown that double layer combination can improve the absorption coefficient of different materials up to 4% in different frequencies depending on the material (4% for polyester and foam for under 800 Hz, 3–4% for polyester and fiber for 800–1600 Hz and 2.6% for foam in 1600–3000 Hz).
Key words: Doublelayer sound absorber / optimization / foam / fiber / polyester / transfer matrix / Evolution Strategy (ES) Algorithm / broadband frequency range
© AFM, EDP Sciences 2018
1 Introduction
In 1970, the World Health Organization (WHO) carried out an assessment about the global disease burden from the occupational noise in which the noise characteristics and their relevance to workers' health were studied and quantified. It was proved that high level of environmental noise in a working area is the main cause of worker's psychological and physiological diseases [1]. It has always been a challenge for engineers to design an optimized sound absorber in order to obtain suitable absorption in a broadband frequency range [2]. To achieve this goal, a combination of multilayered absorbers has been proved to be very effective [2,3]. While porous materials have many applications in sound absorption, they only exhibit suitable absorptive properties at high noise frequencies. Therefore, for an appropriate noise reduction and sound absorption in low frequency range, a resonator absorber is used as a coating for the porous material. This type of covering improves the absorption properties at low frequency noises while protecting the porous material. The absorption mechanism for this type of coating is based on the Helmholtz resonator function. The resonators can be of various types such as membrane, perforated plate, microperforated plate and slot plate while a perforated plate resonator is used in the present study as the coating for the porous material.
As far as porous sound absorbers are concerned, different models have been introduced which offer sound absorption coefficients using three different methods [4]; Empirical Model (EM), Phenomenological Model (PM) which is based on the cylindrical tube, and Microstructure Model (MM). The latter is based on a set of cylinders while air passes through them. Formulations of Delany and Bazley [5] are among many examples of the application of empirical models [6]. In the past couple of decades, great efforts have been exerted to use phenomenological models [7–16], while major contributions have been made to the application of microstructure models [17–21].
In order to achieve an optimum design, different optimization tools have been developed over the years. In the case of optimal noise control, sound absorption and transfer loss have been studied using Genetic Algorithm (GA), Simulated Annealing (SA), and Topology methods. As an example, Yoon [22] optimized fiber materials using topology method, while Duhring et al. [23] developed an acoustical design using this method of optimization. Different arrangements of porous layers have been studied by Lee et al. [24] in order to achieve maximum transfer loss based on topology method. In another attempt, Lee et al. [25] optimized twodimensional foams for maximum sound absorption. In a similar approach, the efficiency of a muffler was improved using topology method [26], while Yoon et al. [27] optimized the acousticstructure interaction problems using topology method. Simulated annealing method was also used by Ruiz et al. [3] in order to optimize the absorption properties of microperforated multilayered panel. Also, the Genetic Algorithm (GA) optimization tool was used to improve the absorption characteristics of metamaterials by Meng et al. [28].
In the present study, first the singlelayer absorber with a particular arrangement is optimized using Evolution Strategy (ES) algorithm and the obtained results are compared against those by Genetic Algorithm (GA) and Gradient methods at a particular noise frequency [29,30]. Subsequently, a doublelayer sound absorber is optimized under thickness limitations at a particular frequency using ES and GA methods [31,32]. Accordingly, different characteristics of singlelayer and doublelayer sound absorbers are optimized to obtain the best absorption coefficients. This is done at three different frequency bands for three different porous materials of foam, fiber and polyester, using transfer matrix and ES optimization method. Later, by comparing the absorption coefficients of single and doublelayer absorbers, the effectiveness of doublelayer layout is evaluated for these materials.
In the next sections, the mathematical formulation of transfer matrix method and the Evolution Strategy is presented, followed by the optimization of single and double layer combinations of foam, fiber and polyester.
2 Mathematical formulation
Transfer matrix is a powerful method which is capable of modeling sound propagation in porous materials with or without resonator coatings. By this method, the assumption of plane wave is applied to the incident and transmitted waves through the absorber layers.
Impedance of any arbitrary surface is given by the relation [2] (1) in which z_{si+1} and z_{si} are impedances at X_{i+1} and X_{i}, respectively (as shown in Fig. 1), while k_{i} is wave number at the ith layer. When a hard backing is placed behind the absorber, the above formula is then reduced to equation (2) [2]. (2)
It is now possible to change equation (2) to conform to a singlelayer absorber shown in Figure 2, as in (3) where (4)
Here, z_{0} and k_{0} are air impedance and wave number, respectively [2]. In the meantime, impedance for medium 2 can be written as [2] (5)
Now, with the addition of z_{2} to z_{p}, the impedance of the absorber's surface can be evaluated as [2] (6)
Using empirical formulas [5], it is possible to calculate the impedance and wave number for the porous material by (7) (8)
Coefficients c_{i} should be defined for each of the materials.
Additionally, equation (9) is used to evaluate the impedance on the surface of the perforated panel [33]. (9)
Consequently, equations of the absorption coefficient using transfer matrix approach for the single and doublelayer sound absorbers can be formulated.
Fig. 1
Transfer matrix methodology for multilayered absorbers. 
Fig. 2
Arrangement for a singlelayer absorber. 
2.1 Absorption coefficient for singlelayer absorbers
Absorption coefficient for a singlelayer absorber (as shown in Fig. 2) is defined as follows: (10) where p% and L are (11) (12)
2.2 Absorption coefficient for doublelayer absorber
For a double or multilayer sound absorber (shown in Fig. 3), the optimization parameters are defined as follows: (13) (14)
It should be noted that an absorption coefficient of unity stands for maximum possible sound absorption or total absorption of sound.
Fig. 3
Arrangement for a doublelayer absorber. 
3 Evolution Strategy (ES) algorithm
The algorithm of Evolution Strategy (ES) was firstly introduced by Rochenberg [34] in 1973 as an innovative method. Steps that are performed in most of the ES algorithms:
Step 1: Determination of population size, maximum number of generations, and mutation rate.
Step 2: Generation of the initial population (as parents) using random numbers (which gives a set of chromosomes).
Step 3: Calculation of fitness function for each chromosome.
Step 4: Evaluation of termination criterion, moving to Step 5 in case the criterion is not satisfied.
Step 5: Production of next generation using following methods:

using elitism (selection of a particular number of elite chromosomes from the community);

applying mutation to a particular set of community members (mutants) and the generation of children for the next generation;
Step 6: Returning to Steps 3 and 4.
For applying the ES algorithm to any desired problem, the operators and design parameters for the considered problem should be established, first. Optimization of a single or doublelayer absorber using the ES algorithm is done in the same way and hence the operators of the absorption problem are introduced and discussed in this section.
A chromosome in ES algorithm is given as a set of (x_{1}, x_{2}, … , x_{n}, σ) in which x_{i} are the problem variables which are given as real numbers and σ is the step length for the mutation. The value of σ is determined using the one fifth success rule during the execution of the algorithm. Rochenberg [31] mathematically proved that, when the number of successful mutations accounts for one fifth of the unsuccessful mutations, the speed of convergence to the optimum solution increases. Based on this rule, in the present study, five variables are introduced. Each chromosome of the population has five genes which stand for the following parameters; Gene 1 for the porous material thickness (L_{1}), Gene 2 for the specific resistance of porous material (R), Gene 3 for the diameter of the holes on the perforated panel (d), Gene 4 for the porosity of the perforated panel (ϵ) and Gene 5 for the total thickness of the absorber (L). Therefore continuous random numbers are used for production of the initial generation (parents). It must be noted that these random numbers should not violate the limitations of the problem at hand. After producing the initial generation, a fitness function should be assigned to each chromosome. Based on this fitness function, some elite chromosomes will be chosen as new parents in order to be used for the mutation process which leads to production of children for the next generation. In the present study, normal mutation method is applied to the parent chromosomes to produce mutants. In this method of mutation, a random and normalized number is added to all genes, as shown in equation (15). (15)
It is noteworthy that after this mutation process, generated chromosomes that are theoretically impossible to be created, ought to be fixed. This basically means that the generated genes should not violate the limitation of the problem. If this situation does occur, the respective chromosome should be repaired. In order to repair the chromosome, the value of the violated gene should be set as the allowable limit for that parameter.
The process of elitism is used to generate the next generation. In other words, a particular number of elite chromosomes are chosen which will be transferred to the next generation. The rest of the required chromosomes for the next generation will be selected randomly from the chromosomes of the previous generation and the newly generated chromosomes resulting from the mutation process. The Evolution Strategy algorithm is continued until the termination criterion is fulfilled. The flowchart of the applied ES algorithm is illustrated in Figure 4.
Fig. 4
Flowchart of the applied Evolution Strategy (ES) algorithm. 
4 Validation tests
In order to validate the results of optimization using Evolution Strategy algorithm, the obtained results are compared against the results of other methods in similar condition. Accordingly, the optimization results for both the single and doublelayer absorbers are compared with the results of Chang et al. [30, 31].
4.1 Singlelayer absorber
The following limitations are set as the design and optimization parameters for the case of singlelayer sound absorber which is intended to be optimized at a noise frequency of 350 Hz. (16)
Thickness of the perforated panel is kept fixed at q = 0.01(m). Therefore, to assess the validity of the Evolution Strategy optimization algorithm, the obtained results using ES are compared against the results of reference [29] for similar design parameters, but using Genetic Algorithm (GA) and Gradient methods. The results of comparison are displayed in Table 1.
It is shown that by applying ES algorithm, higher absorption coefficient can be achieved, while other design parameters and limitations are respected. Therefore, the results of ES optimization are proved to be favorable.
4.2 Doublelayer absorber
The design parameters and limitations of optimization for the doublelayer absorber based on the studies of Chang et al. [31] are given as follows: (17) (18)
It should be noted that in the present study, thickness of the perforated plate for both layers are fixed at q = 0.0006(m) = 0.6(mm), and other design parameters, as introduced in section 2, are as follows: (19)
The results of optimization using Genetic Algorithm (GA) [31] and Evolution Strategy (ES) at a particular frequency of 350 Hz are displayed in Table 2.
Optimization results in the case of doublelayer absorber using Evolution Strategy and genetic algorithms.
5 Broadband optimization ES algorithm
Evolution Strategy (ES) algorithm has been proved to be a robust and effective tool for sound absorption optimization at a predefined frequency for both single and double layer sound absorbers. The algorithm is now implemented for optimization of single and doublelayer absorbers in a broadband frequency range extending from 100 Hz to 3 kHz for three different materials of foam, fiber, and polyester.
This range is divided into three bands; low frequency from 100 Hz to 800 Hz, medium frequency from 800 Hz to 1600 Hz, and relatively high frequency from 1600 Hz to 3000 Hz. In each frequency range, the initial design parameters for both single and doublelayer absorbers are selected randomly in the framework of design limitations and criteria. Subsequently, by a step of 1 Hz and using the transfer matrix method presented in Section 2, the absorption coefficients at each frequency are evaluated and ultimately the mean absorption coefficient in the entire range is calculated. This averaged parameter is then used as the fitness function for the algorithms of elitism and mutation in order to generate the next set of offspring for the next generation. The ultimate goal is to achieve a set of optimized design parameters which will result in the highest possible averaged absorption coefficient in the desired frequency range.
5.1 Broadband optimization of single and doublelayer materials
As described in the previous section, design parameters are initially selected randomly to give an initial averaged absorption coefficient for the selected frequency range, based on the formulas of transfer matrix approach. This averaged absorption coefficient is then maximized using the ES algorithm.
Before proceeding to the analysis and optimization, the coefficients (c_{i}) for each material should be identified as in Table 3.
By substituting the values of these three materials in the single and doublelayer optimization code and by defining the frequency range of the optimization, the best material properties for three ranges of frequency are determined. Therefore, eighteen optimized materials are obtained.
The resulting optimized parameters (defined in Sect. 2) and the mean absorption coefficients for different materials in different ranges are listed in Table 4.
Coefficients (c_{i}) for fiber, foam and polyester.
The optimized parameters and the corresponding average absorption coefficients.
5.2 Characteristics charts
To obtain a more comprehensive comparison of the optimized materials, a new “at a glance” representation is introduced in this section in which all material characteristics could be presented in a single chart, called the Characteristic Chart.
To illustrate all characteristics in one chart, the first difficulty is the large difference in the range of values for different characteristics. As an example, the values of flow resistivity are in the ten thousands scale, while the porosity is always under unity. Therefore, the values of all characteristics should be normalized first, i.e., a value between zero to one should be assigned to each characteristic. Here, the values of each parameter have been normalized using its maximum value over all materials. For example, in singlelayer absorbers, all values of flow resistivity and porosity are divided by 23138 and 0.43 respectively, which are the maximum values of flow resistivity and porosity in all singlelayer absorbers. Same operation is performed on other characteristics.
Aerward, a radar chart is used to plot the values of each material in one chart. In these charts, there are as many as as the characteristics of the materials, i.e., 5 axes for single layer materials and 10 for doublelayer absorbers. The characteristics chart of single and double layer foam optimized for 100–800 Hz is illustrated in Figure 5.
It is observed in Figure 5 that all characteristics of a material are shown in one chart and a good notion of the material characteristics is perceived.
In Figures 6 and 7, the optimized characteristics charts of each single and doublelayer absorber in different frequency ranges are plotted together.
It is quite evident in these characteristics charts that parameters of a material may vary dramatically to obtain the best performance in the three ranges of frequency, especially the porosity, the flow resistivity, and the thickness of the absorber.
Although the average absorption coefficient is used as the target for optimization, it does not offer a good description of the performance of the absorber. Therefore, the absorption coefficient of the absorbers has been plotted vs. the frequency ranges in Figure 8.
As evidenced in Figure 8, for mid and high frequency ranges, double layer formation seems to improve the overall behavior of the absorber. However, for low frequencies, one may conclude that single layer formation is a better choice.
Fig. 5
Characteristics chart of single and double layer Foam optimized for 100–800 Hz. 
Fig. 6
Optimized characteristics chart of singlelayer absorbers in three frequency ranges. 
Fig. 7
Optimized characteristics chart of doublelayer absorbers in three frequency ranges. 
Fig. 8
Single and doublelayer absorption coefficient versus frequency for different materials, optimized for three ranges of frequency. 
5.3 Efficacy analysis of double layer formation
For better assessment of the efficacy of the double layer formation in different frequencies and for different materials, the Improvement Percentage is defined as in equation (20). (20)
The Improvement Percentage has been calculated for each formation and illustrated in Figure 9.
As observed in Figure 9, the maximum improvement obtained is slightly more than 4%. Also, it is observed that at low frequencies, foam is the only material improved by double layer formation. It is therefore deduced from Figure 9 that, although there is a slight improvement of the absorption coefficient, using double layer porous materials for the frequency ranges specified here requires careful design and optimization.
Fig. 9
Improvement percentage of the optimized materials for three ranges of frequency. 
6 Conclusions
In the present study, Transfer Matrix approach and Evolution Strategy (ES) algorithm are used in order to achieve an optimized design for single and doublelayer formations for Foam, Fiber and Polyester porous absorbers with maximum sound absorption.
Because of the fact that the main goal in many engineering and industrial applications is to obtain optimal absorption in a particular range of frequencies, the focus of the present study is to optimize the absorbers in order to have appropriate absorption properties in a broadband range of frequency. Single and doublelayer absorbers are first optimized at a particular frequency using the ES algorithm and the obtained results are compared with those using Genetic Algorithm (GA) and Gradient methods. This comparison proves that the ES algorithm offers favorable results based on the limitations that are imposed on the design problem with superiority over the absorption coefficient obtained by the widely used method of Genetic Algorithm.
Single and doublelayer absorbers are then optimized in three different frequency ranges extending from 100 Hz to 3000 Hz divided into three bands of low, medium and relatively high frequencies for three porous materials. The averaged absorption coefficients in each range of frequency are maximized using ES algorithm for both single and doublelayer sound absorbers. To show the optimized parameters of absorbers, the characteristic charts of absorbers are introduced. Also, for a better assessment of the efficacy of the double layer formation, an Improvement percentage parameter has been defined and estimated for each optimized material and formation in each frequency range. The obtained results indicate that maximum improvement reached by the use of doublelayer formation is slightly higher than 4%. Furthermore, it is shown that materials are differently affected by double layering. Accordingly, the improvement of absorption coefficient is 4% for polyester and foam below 800 Hz frequency, 3–4% for the polyester and fiber for 800 Hz–1600 Hz frequency and 2.6% for the foam for 1600 Hz–3000 Hz frequency. Overall, the presented findings indicate that, although there is a slight improvement of the absorption coefficient, using double layer porous materials for the frequency ranges specified here requires careful design and optimization.
The method of Evolution Strategy (ES) algorithm is also proved to be robust in the case of single and multilayer sound absorber optimization problems.
Compliance with ethical standards
Authors of this study received no specific grant from any funding agency in the public, commercial, or notforprofit sectors. The authors also declare that they have no conflict of interest.
References
 T.S. Lan, Numerical optimization on plant noise control by gradient method, Int. J. Comput. Math. 86 (2009) 897–913 [CrossRef] [Google Scholar]
 T.J. Cox, P. D'antonio, Acoustic absorbers and diffusers: theory, design and application, CRC Press, Llc, New York, US, 2009 [Google Scholar]
 H. Ruiz, P. Cobo, F. Jacobsen, Optimization of multiplelayer microperforated panels by simulated annealing, Appl. Acoust. 72 (2011) 772–776 [CrossRef] [Google Scholar]
 Z. Bo, C. Tianning, Calculation of sound absorption characteristics of porous sintered fiber metal, Appl. Acoust. 70 (2009) 337–346 [CrossRef] [Google Scholar]
 M.E. Delany, E.N. Bazley, Acoustical properties of fibrous absorbent materials, Appl. Acoust. 3 (1970) 105–116 [CrossRef] [Google Scholar]
 F.P. Mechel, Formulas of acoustics, Springer, Berlin, Germany, 2002 [Google Scholar]
 M.A. Biot, Theory of Propagation of Elastic Waves in a FluidSaturated Porous Solid I. LowFrequency Range, Acoust. Soc. Am. 28 (1955) 168–178 [CrossRef] [MathSciNet] [Google Scholar]
 K. Attenborough, Acoustical characteristics of porous materials, Phys. Rep. 82 (1982) 179–227 [CrossRef] [Google Scholar]
 K. Attenborough, Acoustical characteristics of rigid fibrous absorbents and granular materials, Acoust. Soc. Am. 73 (1983) 785 [CrossRef] [Google Scholar]
 J. Allard, N. Atalla, Propagation of sound in porous media: modelling sound absorbing materials, Wiley, 2009 [CrossRef] [Google Scholar]
 C. Zwikker, C.W. Kosten, Sound absorbing materials, Elsevier, 1949 [Google Scholar]
 Y. Champoux, M.R. Stinson, Experimental investigation of models of sound wave propagation in air‐saturated porous media, Acoust. Soc. Am. 88 (1990) S121 [CrossRef] [Google Scholar]
 Y. Champoux, M.R. Stinson, On acoustical models for sound propagation in rigid frame porous materials and the influence of shape factors, Acoust. Soc. Am. 92 (1992) 1120 [CrossRef] [Google Scholar]
 M.R. Stinson, The propagation of plane sound waves in narrow and wide circular tubes, and generalization to uniform tubes of arbitrary cross‐sectional shape, Acoust. Soc. Am. 89 (1991) 550 [CrossRef] [Google Scholar]
 D. Wilson, Simple, relaxational models for the acoustical properties of porous media, Appl. Acoust. 50 (1997) 171–188 [CrossRef] [Google Scholar]
 D. Lafarge, P. Lemarinier, J.F. Allard, V. Tarnow, Dynamic compressibility of air in porous structures at audible frequencies, Acoust. Soc. Am. 102 (1997) 1995 [CrossRef] [Google Scholar]
 Y. Kawasima, Sound propagation in a fiber block as a composite medium, Acust. 10 (1960) 208–217 [Google Scholar]
 V. Tarnow, Compressibility of air in fibrous materials, Acoust. Soc. Am. 99 (1996) 3010 [CrossRef] [Google Scholar]
 V. Tarnow, Calculation of the dynamic air flow resistivity of fiber materials, Acoust. Soc. Am. 102 (1997) 1680 [CrossRef] [Google Scholar]
 K. Attenborough, L. Walker, Scattering theory for sound absorption in fibrous media, Acoust. Soc. Am. 49 (1971) 1331–1338 [CrossRef] [Google Scholar]
 I.D.J. Dupère, A.P. Dowling, T.J. Lu, The absorption of sound in cellular foams, ASME, (2004) [Google Scholar]
 G.H. Yoon, Acoustic topology optimization of fibrous material with DelanyBazley empirical material formulation, J. Sound Vib. 332 (2013) 1172–1187 [CrossRef] [Google Scholar]
 M.B. Dühring, J.S. Jensen, O. Sigmund, Acoustic design by topology optimization, J. Sound Vib. 317 (2008) 557–575 [CrossRef] [Google Scholar]
 E.I. Kim, Y.Y. Kim, J.S. Kim, Y.J. Kang, Optimal poroelastic layer sequencing for sound transmission loss maximization by topology optimization method, Acoust. Soc. Am. 122 (2007) 2097 [CrossRef] [Google Scholar]
 J.S. Lee, Y.Y. Kim, J.S. Kim, Y.J. Kang, Twodimensional poroelastic acoustical foam shape design for absorption coefficient maximization by topology optimization method, Acoust. Soc. Am. 123 (2008) 2094 [CrossRef] [Google Scholar]
 J.W. Lee, Y.Y. Kim, Topology optimization of muffler internal partitions for improving acoustical attenuation performance, Int. J. Numer. Methods Eng. 80 (2009) 455–477 [CrossRef] [Google Scholar]
 G.H. Yoon, J.S. Jensen, O. Sigmund, Topology optimization of acousticstructure interaction problems using a mixed finite element formulation, Int. J. Numer. Methods Eng. 70 (2007) 1049–1075 [CrossRef] [Google Scholar]
 H. Meng, J. Wen, H. Zhao, X. Wen, Optimization of locally resonant acoustic metamaterials on underwater sound absorption characteristics, J. Sound Vib. 331 (2012) 4406–4416 [CrossRef] [Google Scholar]
 Y.C. Chang, L.J. Yeh, M.C. Chiu, G.J. Lai, Shape optimization on constrained singlelayer sound absorber by using GA method and mathematical gradient methods, J. Sound Vib. 286 (2005) 941–996 [CrossRef] [Google Scholar]
 Y.C. Chang, L.J. Yeh, M.C. Chiu, Optimization of constrained composite absorbers using simulated annealing, Appl. Acoust. 66 (2009) 341–352 [CrossRef] [Google Scholar]
 Y.C. Chang, L.J. Yeh, M.C. Chiu, Optimization of double‐layer absorbers on constrained sound absorption system by using genetic algorithm, Int. J. Numer. Methods Eng. 62 (2005) 317–333 [CrossRef] [Google Scholar]
 M.C. Chiu, Y.C. Chang, L.J. Yeh, T.S. Lan, Optimization of perforated doublelayer absorbers using simulated annealing, J. Mar. Sci. Technol. 15 (2007) 351–359 [Google Scholar]
 L.L. Beranek, I.L. Ver, Noise and vibration control engineeringprinciples and applications, Noise Vib. Control Eng. −Princ. Appl. 1 (1992) 814 [Google Scholar]
 I. Rechenberg, Evolutions strategie: optimierung technischer systeme nach prinzipien der biologischen evolution, FrommannHolzboog, Stuttgart, Germany, 1973 [Google Scholar]
 I.P. Dunn, W.A. Davern, Calculation of acoustic impedance of multilayer absorbers, Appl. Acoust. 19 (1986) 321–334 [CrossRef] [Google Scholar]
 M. Garai, F. Pompoli, A simple empirical model of polyester fibre materials for acoustical applications, Appl. Acoust. 66 (2005) 1383–1398 [CrossRef] [Google Scholar]
Cite this article as: P. Ghadimi, M. Gholamipour, M.A. Feizi Chekab, Optimization of doublelayer sound absorber in a broadband frequency range using transfer matrix method and Evolution Strategies algorithm, Mechanics & Industry 19, 101 (2018)
All Tables
Optimization results in the case of doublelayer absorber using Evolution Strategy and genetic algorithms.
All Figures
Fig. 1
Transfer matrix methodology for multilayered absorbers. 

In the text 
Fig. 2
Arrangement for a singlelayer absorber. 

In the text 
Fig. 3
Arrangement for a doublelayer absorber. 

In the text 
Fig. 4
Flowchart of the applied Evolution Strategy (ES) algorithm. 

In the text 
Fig. 5
Characteristics chart of single and double layer Foam optimized for 100–800 Hz. 

In the text 
Fig. 6
Optimized characteristics chart of singlelayer absorbers in three frequency ranges. 

In the text 
Fig. 7
Optimized characteristics chart of doublelayer absorbers in three frequency ranges. 

In the text 
Fig. 8
Single and doublelayer absorption coefficient versus frequency for different materials, optimized for three ranges of frequency. 

In the text 
Fig. 9
Improvement percentage of the optimized materials for three ranges of frequency. 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.