Multi-levels inverse identification of physical parameters of porous materials

Hentati, Taissir; Bouazizi, Leila; Taktak, Mohamed; Trabelsi, H.; Haddar, Mohamed

doi:10.1016/j.apacoust.2015.09.013

Cited by 21 publications

(17 citation statements)

References 18 publications

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“…As mentioned above, the success of the proposed stepwise approach appears to be linked to the dominant model parameters and the correlations between these and their variations over the different parts of the frequency spectrum studied. This is in agreement with the findings in [27,10].…”

Section: Discussionsupporting

confidence: 93%

Parameter estimation in modelling frequency response of coupled systems using a stepwise approach

Göransson

Cuenca

Lähivaara

2019

Mechanical Systems and Signal Processing

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This paper studies the problem of parameter estimation in resonant, acoustic fluid-structure interaction problems over a wide frequency range. Problems with multiple resonances are known to be subjected to local minima, which represents a major challenge in the field of parameter identification. We propose a stepwise approach consisting in subdividing the frequency spectrum such that the solution to a low-frequency subproblem serves as the starting point for the immediately higher frequency range. In the current work, two different inversion frameworks are used. The first approach is a gradient-based deterministic procedure that seeks the model parameters by minimising a cost function in the least squares sense and the second approach is a Bayesian inversion framework. The latter provides a potential way to assess the validity of the least squares estimate. In addition, it presents several advantages by providing invaluable information on the uncertainty and correlation between the estimated parameters. The methodology is illustrated on synthetic measurements with known design variables and controlled noise levels. The model problem is deliberately kept simple to allow for extensive numerical experiments to be conducted in order to investigate the nature of the local minima in full spectrum analyses and to assess the potential of the proposed method to overcome these. Numerical experiments suggest that the proposed methods may present an efficient approach to find material parameters and their uncertainty estimates with acceptable accuracy. arXiv:1808.04985v1 [physics.comp-ph]

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Section: Discussionsupporting

confidence: 93%

Parameter estimation in modelling frequency response of coupled systems using a stepwise approach

Göransson

Cuenca

Lähivaara

2019

Mechanical Systems and Signal Processing

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“…Different inverse problems for parameter identifications have been attempted in the past regarding air-saturated porous media, mostly in the audio frequency regime [10,11,12,13,14]. The largest unaddressed issues of these studies are the modeling deficiency at these frequencies and the scarcity of uncertainty considerations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference for the ultrasonic characterization of rigid porous materials using reflected waves by the first interface

Roncen

Fellah

Simon

et al. 2018

The Journal of the Acoustical Society of America

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The purpose of this paper is to present a method for the ultrasonic characterization of air-saturated porous media, by solving the inverse problem using only the reflected waves from the first interface to infer the porosity, the tortuosity, and the viscous and thermal characteristic lengths. The solution of the inverse problem relies on the use of different reflected pressure signals obtained under multiple obliquely incident waves, in the time domain. In this paper, the authors propose to solve the inverse problem numerically with a first level Bayesian inference method, summarizing the authors' knowledge on the inferred parameters in the form of posterior probability densities, exploring these densities using a Markov-Chain Monte-Carlo approach. Despite their low sensitivity to the reflection coefficient, it is still possible to extract the knowledge of the viscous and thermal characteristic lengths, allowing the simultaneous determination of all the physical parameters involved in the expression of the reflection operator. To further constrain the problem and guide the inference, the knowledge of a particular incident angle is used at one's advantage in order to more precisely define the thermal length, by effectively yielding a statistical relationship between tortuosity and characteristic length ratio.

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“…The measured values of the air flow resistivity, porosity and tortuosity of this material were listed in Table 1 . Literature [ 25 ] used the method of multi-levels inverse estimation to obtain the five non-acoustical parameter values for the same material. In this study the MPSO algorithm is also utilized to estimate the five parameters of the same material.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm

Lin

2021

PLoS ONE

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Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is an active research area and many researchers have devoted contributions on it. In this study, a modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters of the jute fiber felt. Firstly, the sound absorption model used to predict the sound absorption coefficient of the porous materials is introduced. Secondly, the model of non-acoustical parameter identification of porous materials is established. Then the modified particle swarm optimization algorithm is introduced and the feasibility of the algorithm applied to the parameter identification of porous materials is investigated. Finally, based on the sound absorption coefficient measured by the impedance tube the modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters involved in the sound absorption model of the jute fiber felt, and the identification performance and the computational performance of the algorithm are discussed. Research results show that compared with other identification methods the modified particle swarm optimization algorithm has higher identification accuracy and is more suitable for the identification of non-acoustical parameters of the porous materials. The sound absorption coefficient curve predicted by the modified particle swarm optimization algorithm has good consistency with the experimental curve. In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. When the population size is larger, modified particle swarm optimization algorithm has more advantages in the running speed. In addition, this study demonstrates that the jute fiber felt is a good acoustical green fibrous material which has excellent sound absorbing performance in a wide frequency range and the peak value of its sound absorption coefficient can reach 0.8.

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Multi-levels inverse identification of physical parameters of porous materials

Cited by 21 publications

References 18 publications

Parameter estimation in modelling frequency response of coupled systems using a stepwise approach

Parameter estimation in modelling frequency response of coupled systems using a stepwise approach

Bayesian inference for the ultrasonic characterization of rigid porous materials using reflected waves by the first interface

Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm

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