Multiscale modeling of PVDF matrix carbon fiber composites

Self-sensing carbon fiber reinforced composites have the potential to enable structural health monitoring that is inherent to the composite material rather than requiring external or embedded sensors. It has been demonstrated that a self-sensing carbon fiber reinforced polymer composite can be created by using the piezoelectric polymer polyvinylidene difluoride (PVDF) as the matrix material and using a Kevlar layer to separate two carbon fiber layers. In this configuration, the electrically conductive carbon fiber layers act as electrodes and the Kevlar layer acts as a dielectric to prevent the electrical shorting of the carbon fiber layers. This composite material has been characterized experimentally for its effective d ₃₃ and d ₃₁ piezoelectric coefficients. However, for design purposes, it is desirable to obtain a predictive model of the effective piezoelectric coefficients for the final smart composite material. Also, the inverse problem can be solved to determine the degree of polarization obtained in the PVDF material during polarization by comparing the effective d ₃₃ and d ₃₁ values obtained in experiment to those predicted by the finite element model. In this study, a multiscale micromechanics and coupled piezoelectric-mechanical finite element modeling approach is introduced to predict the mechanical and piezoelectric performance of a plain weave carbon fiber reinforced PVDF composite. The modeling results show good agreement with the experimental results for the mechanical and electrical properties of the composite. In addition, the degree of polarization of the PVDF component of the composite is predicted using this multiscale modeling approach and shows that there is opportunity to drastically improve the smart composite's performance by improving the polarization procedure.