Modeling and Simulation of 3D Random Nanowire Network Based on Modified Kd-tree Algorithm

Random nanowire network has become an important material foundation of many emerging interdisciplinary subjects. In this study, a specific simulation method using kd-tree algorithm with scapegoat tree algorithm in 3D conductive nanowire network is proposed. This model differs from the traditional model based on kd-tree algorithm as the scapegoat tree algorithm is used to optimize the imbalance caused by kd-tree algorithm in the process of nanowire network generation to improve the efficiency of simulation. The results show the efficiency of the model with scapegoat tree algorithm is 1.2-1.4 times higher than that without scapegoat tree algorithm. In addition, the percolation characteristic is the basis of mechanical and electrical properties of nanowire networks which is greatly influenced by the volume fraction and the shape of conductive fillers in nanowire networks, therefore, the effects of volume fraction and aspect ratio (AR) of nanowires on the percolation probability of the network are investigated in our work. It finds that there is a nonlinear increasing trend of percolation probability with the increase of volume fraction of conductive nanowires, and a positive correlation between percolation probability and AR. The simulation results indicate that it is effective to obtain the percolation probability under different volume fraction and AR of conductive nanowires by using such simulation model based on the kd-tree algorithm modified by scapegoat tree algorithm.