A new algorithm to rapidly create representative volume elements (RVE) with random fibers distribution of high volume fractional was proposed. Derived from the classical hard core algorithm, and the "molecular force" effect was introduced to eliminate the irreversibility of the generation process. The new algorithm can create fiber microstructures with constant or random radius, and the volume fraction can reach 80% and 85% respectively. The stochastic statistical analysis of the generated fiber distribution shows that the generated microstructure is completely consistent with the spatial random distribution. Micro-flow analysis was performed using the generated RVE, predicting the transverse permeability of the fiber tow, and the predicted results were in good agreement with the experimental results. A simple method for creating RVE with random distribution of fibers was provided for the micro-mechanical analysis of composite materials.

A geometric modeling platform based on the virtual fiber-voxel element is proposed to characterize textile reinforcement in a cost-effective way, which could reflect the systemic local changes of the tow path and tow cross-section. The finite volume method is used to solve the governing equation of the boundary value problem, and the permeability of the fabric is calculated by using the flow field data obtained from the model. The flow field analysis and pressure drop in flow direction of different thickness unit cells suggest that the size of inter-tow channels play a vital role in determining the overall in-plane permeability values. The flow channels of the inter tow in warp direction were found to be greater than in the weft direction, which causes the overall permeability to be anisotropic.

It is costly to optimize the location of multiple injection gates through a trial and error-based method in the liquid composite molding, even though there are high fidelity physics-based numerical models. A hybrid optimization method called the Simulated Annealing Genetic Algorithm is proposed in this article, which uses the genetic algorithm to provide a global search for a predetermined time and then is further improved by the simulated annealing algorithm. The optimization results of multiple injection gates show that the number of convergence iterations using the Simulated Annealing Genetic Algorithm is less than that using the genetic algorithm, and the phenomenon becomes more obvious as the number of injection gates increases. The case shows that the Simulated Annealing Genetic Algorithm can solve the multiple injection gate configuration problems of highly anisotropic laminates without extra work. The optimization results are in good agreement with the experimental results.