Fitting stress relaxation experiments with fractional Zener model to predict high frequency moduli of polymeric acoustic foams - Le Mans Université Accéder directement au contenu
Article Dans Une Revue Mechanics of Time-Dependent Materials Année : 2016

Fitting stress relaxation experiments with fractional Zener model to predict high frequency moduli of polymeric acoustic foams

Résumé

This paper presents a time domain method to determine viscoelastic properties of open-cell foams on a wide frequency range. This method is based on the adjustment of the stress–time relationship, obtained from relaxation tests on polymeric foams’ samples under static compression, with the four fractional derivatives Zener model. The experimental relaxation function, well described by the Mittag–Leffler function, allows for straightforward prediction of the frequency-dependence of complex modulus of polyurethane foams. To show the feasibility of this approach, complex shear moduli of the same foams were measured in the frequency range between 0.1 and 16 Hz and at different temperatures between −20 °C and 20 °C. A curve was reconstructed on the reduced frequency range (0.1 Hz–1 MHz) using the time–temperature superposition principle. Very good agreement was obtained between experimental complex moduli values and the fractional Zener model predictions. The proposed time domain method may constitute an improved alternative to resonant and non-resonant techniques often used for dynamic characterization of polymers for the determination of viscoelastic moduli on a broad frequency range.
Fichier non déposé

Dates et versions

hal-02470926 , version 1 (07-02-2020)

Identifiants

Citer

Xinxin Guo, Guqi Yan, Lazhar Benyahia, Sohbi Sahraoui. Fitting stress relaxation experiments with fractional Zener model to predict high frequency moduli of polymeric acoustic foams. Mechanics of Time-Dependent Materials, 2016, 20 (4), pp.523-533. ⟨10.1007/s11043-016-9310-3⟩. ⟨hal-02470926⟩
29 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More