Coupled electro-mechanical models of fiber-distributed active tissues. Issue 12 (16th August 2016)

• Record Type: Journal Article
• Authors: Pandolfi, Anna; Gizzi, Alessio; Vasta, Marcello
• Is Part Of: Journal of biomechanics. Volume 49:Issue 12(2016)
• Page Start: 2436
• Page End: 2444
• Publication Date: 2016-08-16
• Subjects: Statistical fiber distribution -- Second order approximation -- Electromechanics -- Multiplicative decomposition of deformation gradient -- Constitutive modeling; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2016.01.038
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: We discuss a constitutive model for stochastically distributed fiber reinforced tissues, where the active behavior of the fibers depends on the relative orientation of the electric field. Unlike other popular approaches, based on numerical integration over the unit sphere, or on the use of second order structure tensors, for the passive behavior we adopt a second order approximation of the strain energy density of the distribution. The purely mechanical quantities result to be dependent on two (second and fourth order, respectively) averaged structure tensors. In line with the approximation used for the passive behavior, we model the active behavior accounting for the statistical fiber distribution. We extend the Helmholtz free energy density by introducing a directional active potential, dependent on a stochastic permittivity tensor associated to a particular direction, and approximate the total active potential through a second order Taylor expansion of the permittivity tensor. The approximation allows us to derive explicitly the active stress and the active constitutive tensors, which result to be dependent on the same two averaged structure tensors that characterize the passive response. Active anisotropy follows from the distribution of the fibers and inherits its stochastic parameters. Examples of passive and active behaviors predicted by the model in terms of response to biaxial testing are presented, and comparisons with passive experimental data are provided.