Inverse estimation of the non-acoustical parameters of loose granular absorbers by Simulated Annealing

Cobo, Pedro; Moraes, Elcione L.; Simón, Francisco

doi:10.1016/j.buildenv.2015.05.009

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…Materials 2020, 13,1936 3 of 12 acoustic impedance or absorption coefficient of the material is known, the relevant parameters of the material can be calculated using the inverse strategy. In [7,26,28,29] the non-acoustic parameters of porous materials are evaluated using inverse acoustic characterization, and the results show that this method is robust and reliable. Here, taking the minimum difference between the sound absorption coefficient measured by the impedance tube and calculated by the JCA model as the objective function, five non-acoustic parameters of molecular sieve are obtained by using simulated annealing method.…”

Section: Experimental Equipment and Samplesmentioning

confidence: 99%

“…Meanwhile, the scattering of granules will also affect the absorption of sound energy in the material. References [4][5][6][7][8][9][10] studied the sound absorption of some granular porous materials such as rubber chips, gravel, perlite, aerogel and concrete. The results show that these granular porous materials play a role in sound absorption and noise reduction of road noise barriers and buildings.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation on the Sound Absorption Performance of Granular Molecular Sieves under Room Temperature and Pressure

Zhou

Zhang

et al. 2020

Materials

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The sound absorption of granular silica-aluminate molecular sieve pellets was investigated in this paper. The absorption coefficients of molecular sieve pellets with different pore sizes, pellet sizes, and layer thicknesses were measured through impedance tubes under room temperature and pressure conditions. The effects of pore size, pellet size, layer thickness were compared and explained. The comparisons show that at room temperature and pressure, the sound absorption of molecular sieve pellets is not a result of the crystalline structure, but rather it mainly changes with the pellet size and layer thickness. In addition, the five non-acoustical parameters of molecular sieve pellets were obtained by an inverse characterization method based on impedance tube measurements. The measurement by impedance tubes is in good agreement with the calculation of Johnson-Champoux-Allard (JCA) model, proving that the JCA model can be effectively used to predict the sound absorption of molecular sieve pellets.

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Section: Experimental Equipment and Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Investigation on the Sound Absorption Performance of Granular Molecular Sieves under Room Temperature and Pressure

Zhou

Zhang

et al. 2020

Materials

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“…The first configuration is chosen randomly within the ranges of parameters of equation (17). To change from one configuration to another, just one parameter is changed randomly, which insures some proximity between sequential configurations, what, in turn, allows the optimal solution to be reached faster.…”

Section: Simulated Annealingmentioning

confidence: 99%

“…Simulated annealing was applied to estimate the non-acoustical parameters of the aquarium gravel and glass beads samples using the Miki and Champoux-Stinson models. The above described simulated annealing algorithm was used with T initial = 1, T final = 10 -5 , and β = 0.95, to minimize the objective function defined in equation 16with the constraints defined by equation (17). The algorithm finalizes either when the final temperature is reached or when not a better configuration is found after 50 consecutive changes.…”

Section: Estimated Non-acoustical Parametersmentioning

confidence: 99%

“…15 Simulated annealing 16 has demonstrated to be an appropriate tool for the inverse estimation of non-acoustical parameters of absorbing materials. 17,18 The main goal of this article is to review the use of simulated annealing as an inverse technique to estimate the non-acoustical parameters of sound absorbing materials. Although the method will be illustrated by its application to two granular materials and two impedance models, it could be applied to any other sound absorber and impedance model.…”

Section: Introductionmentioning

confidence: 99%

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Using simulating annealing for the inverse estimation of the non-acoustical parameters of sound absorbers

Cobo

Simón

2017

Building Acoustics

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Acoustic characterization of sound absorbing materials requires the measurement of one to five nonacoustical parameters, depending on the assumed impedance model. The measurement of five non-acoustical parameters, in the case of more refined impedance models, is sophisticated and time-consuming. As an alternative, a much simpler measurement of the normal incidence sound absorption in an impedance tube can be carried out, and the non-acoustical parameters can be estimated by minimizing the difference between the measured and modeled absorption curves. This inverse procedure requires the choice of the impedance model and the inversion method. The aim of the article is to review the goodness of simulating annealing for estimating the non-acoustical parameters of Miki and Champoux-Stinson models of two granular absorbers. Three of these non-acoustical parameters, common to the two models, are also measured, so that a comparison between measured and estimated parameters can be performed.

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Advances of metaheuristic algorithms in training neural networks for industrial applications

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Inverse estimation of the non-acoustical parameters of loose granular absorbers by Simulated Annealing

Cited by 13 publications

References 23 publications

An Investigation on the Sound Absorption Performance of Granular Molecular Sieves under Room Temperature and Pressure

An Investigation on the Sound Absorption Performance of Granular Molecular Sieves under Room Temperature and Pressure

Using simulating annealing for the inverse estimation of the non-acoustical parameters of sound absorbers

Advances of metaheuristic algorithms in training neural networks for industrial applications

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