Fiber reinforced materials (FRMs) can be modeled as bi-phasic materials, where different constitutive behaviors are associated with different phases. The numerical study of FRMs through a full geometrical resolution of the two phases is often computationally infeasible, and therefore most works on the subject resort to homogenization theory, and exploit strong regularity assumptions on the fibers distribution. Both approaches fall short in intermediate regimes where lack of regularity does not justify a homogenized approach, and when the fiber geometry or their numerosity render the fully resolved problem numerically intractable. In this paper, we propose a distributed Lagrange multiplier approach, where the effect of the fibers is superimposed on a background isotropic material through an independent description of the fibers. The two phases are coupled through a constraint condition, opening the way for intricate fiber-bulk couplings as well as allowing complex geometries with no alignment requirements between the discretisation of the background elastic matrix and the fibers. We analyze both a full order coupling, where the elastic matrix is coupled with fibers that have a finite thickness, as well as a reduced order model, where the position of their centerline uniquely determines the fibers. Well posedness, existence, and uniquess of solutions are shown both for the continuous models, and for the finite element discretizations. We validate our approach against the models derived by the rule of mixtures, and by the Halpin-Tsai formulation.
Multiscale modeling of fiber reinforced materials via non-matching immersed methods / Alzetta, Giovanni; Heltai, Luca. - In: COMPUTERS & STRUCTURES. - ISSN 0045-7949. - 239(2020).
|Titolo:||Multiscale modeling of fiber reinforced materials via non-matching immersed methods|
|Autori:||Alzetta, Giovanni; Heltai, Luca|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.compstruc.2020.106334|
|Appare nelle tipologie:||1.1 Journal article|