PROGRESSIVE DAMAGE RESPONSE OF 3D WOVEN COMPOSITES VIA THE MULTISCALE RECURSIVE MICROMECHANICS SOLUTION WITH TAILORED FIDELITY

Progressive failure simulations have been performed for orthogonal 3D woven composites consisting of RTM6 resin matrix and AS4 carbon fibers. The Multiscale Recursive Micromechanics approach has been used, which, while being computationally efficient, captures the primary effects of the microstructure at each considered length scale. This approach also enables use of any micromechanics theory at any length scale, and herein, the fidelity of the chosen theories across the scales has been tailored to strike a balance with computational efficiency. The Mori-Tanaka method is employed at the lowest length scale, the Generalized Method of Cells is used at intermediate scales, and the High-Fidelity Generalized Method of Cells is used at the highest woven composite repeating unit cell scale. Furthermore, two different damage models, also with different levels of fidelity and efficiency, have been used for the resin material at the lowest length scale. Results for the mechanical behavior in response to loading in various directions are compared for the two damage models and with available test data.

A Parallelized Generalized Method of Cells Framework for Multiscale Studies of Composite Materials

This paper presents a parallelized framework for a multi-scale material analysis method called the generalized method of cells (GMC) model which can be used to effectively homogenize or localize material properties over two different length scales. Parallelization is utlized at two instances: (a) for the solution of the governing linear equations, and (b) for the local analysis of each subcell. The governing linear equation is solved parallely using a parallel form of the Gaussian substitution method, and the subsequent local subcell analysis is performed parallely using a domain decomposition method wherein the lower length scale subcells are equally divided over available processors. The parellization algorithm takes advantage of a single program multiple data (SPMD) distributed memory architecture using the Message Passing Interface (MPI) standard, which permits scaling up of the analysis algorithm to any number of processors on a computing cluster. Results show significant decrease in solution time for the parallelized algorithm compared to serial algorithms, especially for denser microscale meshes. The consequent speed-up in processing time permits the analysis of complex length scale dependent phenomenon, nonlinear analysis, and uncertainty studies with multiscale effects which would otherwise be prohibitively expensive.