Open AccessMonte Carlo simulations of nanotube filler in composite material: code optimization
Author(s) -
N Kerdkaen,
Thana Sutthibutpong,
Saree Phongphanphanee,
S. Boonchui,
Jirasak Wong-ekkabut
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1234/1/012026
Subject(s) - monte carlo method , electrical conductor , percolation (cognitive psychology) , percolation threshold , flexibility (engineering) , materials science , composite number , computer science , monte carlo algorithm , composite material , electrical resistivity and conductivity , mathematics , engineering , electrical engineering , statistics , neuroscience , biology
The electrically conductive polymer composites (CPCs) have attracted intensive attention for several decades due to their flexibility and unique electrical properties. CPCs are potentially used in many applications such as flexible electrodes, batteries, and strain sensors. The percolated conductive pathways are formed by conductive filler in polymer matrix which is a major effect on the electrical behavior of CPCs. Computational simulations have been used to study the percolation phenomena of CPCs. The simulation algorithms need to be developed and optimized for reducing the simulation time-consuming. In this study, the in-house Monte Carlo simulation that used to estimate percolation threshold is optimized. To simulate in the large-scale system, cut-off distance will be defined to avoid unnecessary complex calculations. The calculation sequence within the code has been rearranged to omit the unnecessary calculation processes. Results show that the optimized software takes less processing time than the previous version around 5 times. Therefore, we can perform the large system to investigate the percolation phenomenon with less lattice confinement effect.