Non-ambiguous recovery of Biot poroelastic parameters of cellular panels using ultrasonicwaves

Ogam, Erick; Fellah, Zine El Abiddine; Sebaa, Naima; Groby, Jean‐Philippe

doi:10.1016/j.jsv.2010.09.032

Cited by 27 publications

(17 citation statements)

References 41 publications

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“…The presence of several local minima in the cost functionals can complicate the choice of the correct parameter values [19] but in this study the measured wave velocity and density allowed an unambiguous determination of the parameters. The densities of the films were determined from their precisely measured weights (using Lab Scale Analytical Balance, Sartorius ME235P, Goettingen Germany) and volumes (using a 250 ml graduated cylinder, tolerance ± 1.5 ml, Witeg, Germany).…”

Section: The Inverse Problem For the Retrieval Of Mechanical Parametementioning

confidence: 96%

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Identification of the mechanical moduli of flexible thermoplastic thin films using reflected ultrasonic waves: Inverse problem

et al. 2017

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A method for the identification of the mechanical moduli and density of flexible, supple thermoplastic thin films placed on elastic substrates using ultrasonic waves has been developed. The composite medium immersed in a fluid host medium (water) was excited using a 50MHz transducer operating at normal incidence in reflection mode. Inverse problems involving experimental data pertaining to elastic wave propagation in the thin films on their substrates and theoretical fluid-solid interaction models for stratified media using elasticity theory were solved. Two configurations having different interface boundary conditions (BC) were modeled, transverse slip for the sliding contact interface in the case where the thin films were placed on the substrate without bonding; a bonded interface condition. The inverse problem for the recovery of the mechanical parameters were solved for the thin films under the bonded and slip BCs. Substrates made of different elastic materials having different geometries were also evaluated and their advantages discussed.

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Section: The Inverse Problem For the Retrieval Of Mechanical Parametementioning

confidence: 96%

“…plotted with respect to one of the three problem variables, the others being fixed at their computationally-optimal values, i.e, as 2-D plots [19].…”

Section: The Inverse Problem For the Retrieval Of Mechanical Parametementioning

confidence: 99%

Identification of the mechanical moduli of flexible thermoplastic thin films using reflected ultrasonic waves: Inverse problem

et al. 2017

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“…It is diffusive at low frequencies and propagative at high frequencies. In this section, the direct and inverse scattering problems for the ultrasonic propagation in porous material having an elastic frame are solved [42][43][44][45][46]. An experimental application on human cancelous bone is discussed [42,43].…”

Section: Modified Biot Theorymentioning

confidence: 99%

Transient Acoustic Wave Propagation in Porous Media

Fellah¹,

Fellah²,

Dépollier³

2013

Modeling and Measurement Methods for Acoustic Waves and for Acoustic Microdevices

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“…Indeed, some parameters can be measured, such as flow resistivity, porosity and tortuosity. Moreover, for a given material it is possible to inversely estimate these parameters, as shown for example in [ 22 , 23 ]. For the case of an available geometric description of the material on the micro-scale, in [ 24 ] the computation of the flow resistivity of a porous sample is computed using the Lattice–Boltzmann method.…”

Section: Introductionmentioning

confidence: 99%

Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

et al. 2021

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Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

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Non-ambiguous recovery of Biot poroelastic parameters of cellular panels using ultrasonicwaves

Cited by 27 publications

References 41 publications

Identification of the mechanical moduli of flexible thermoplastic thin films using reflected ultrasonic waves: Inverse problem

Identification of the mechanical moduli of flexible thermoplastic thin films using reflected ultrasonic waves: Inverse problem

Transient Acoustic Wave Propagation in Porous Media

Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

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