This study investigates the influence of fiber spatial distribution on the transverse mechanical properties of unidirectionally reinforced continuous-fiber composites. A Swelling & Random Migration algorithm was employed to generate representative volume elements with controlled fiber arrangements, ranging from clustered to equilibrium configurations. Finite element homogenization with periodic boundary conditions was used to estimate effective elastic properties. To characterize fiber randomness and assess statistical equivalence with experimental microstructures, several descriptors are employed, including nearest neighbor distance, Ripley's K-function, pair distribution function, and local fiber volume fraction. Results reveal that, at constant fiber volume fraction, clustered fiber distributions yield significantly higher transverse stiffness but lower transverse tensile strength compared to the equilibrium distributions. For glass/epoxy composites, transverse stiffness varies by up to 20% depending on the degree of fiber clustering. A single scalar descriptor, the mean nearest neighbor distance, was shown to efficiently characterize sufficiently random fiber distributions: effective stiffness decreases, whereas transverse tensile strength increases linearly with mean nearest neighbor distance. The findings highlight the critical role of microstructural characteristics in tailoring composite performance and provide a robust framework for predictive modeling of fiber reinforced materials.

翻译：本研究探讨了纤维空间分布对单向连续纤维增强复合材料横向力学性能的影响。采用膨胀与随机迁移算法生成具有可控纤维排列（从团簇态到平衡态）的代表性体积单元，并结合周期性边界条件的有限元均匀化方法估算有效弹性性能。为表征纤维随机性并评估与实验微观结构的统计等效性，采用了包括最近邻距离、Ripley's K函数、对分布函数及局部纤维体积分数在内的多个描述符。结果表明：在恒定的纤维体积分数下，与平衡分布相比，团簇态纤维分布虽能显著提升横向刚度，但会导致横向拉伸强度降低。对于玻璃/环氧复合材料，横向刚度随纤维团簇程度变化，差异可达20%。单一标量描述符——平均最近邻距离——可有效表征充分随机的纤维分布：有效刚度随平均最近邻距离增大而递减，而横向拉伸强度则呈线性递增趋势。这些发现突显了微观结构特征在调控复合材料性能中的关键作用，并为纤维增强材料的预测性建模提供了稳健框架。