MULTISCALE MODELING TO PREDICT PROPERTIES OF THERMOPLASTIC NANOCOMPOSITES

S Roy and A Nair, MODELS, DATABASES, AND SIMULATION TOOLS NEEDED FOR THE REALIZATION OF INTEGRATED COMPUTATIONAL MATERIALS ENGINEERING, 115-127 (2011).

Recent mechanical characterization experiments on thermoplastic nanocomposites have indicated significant improvements in compressive strength and shear strength in comparison with baseline thermoplastic properties. While the synergistic reinforcement of nanoparticles is evident, a simple rule-of-mixtures approach fails to quantify the dramatic increase in mechanical properties. Consequently, there is an urgent need to investigate and understand the mechanisms at the nanoscale that are responsible for such unprecedented strength improvements. It is envisioned that a better understanding of the mechanisms at the nanoscale will lead to optimization of materials processing variables at the macroscale, which, in turn, will lead to a more efficient and lower cost manufacture of nanocomposites. hi this paper, an innovative and efficient method is proposed to model nano- structured components in a thermoplastic composite by employing a large- deformation hyperelastic constitutive model. Effort is directed toward finding fundamental answers to the reasons for significant changes in mechanical properties of nanoparticle-reinforced thermoplastic composites, and then using this knowledge to optimize processing variables to further improve the targeted properties of the nanocomposites. The proposed method involves a concurrent simulations approach in which the information from molecular dynamics (MD) is seamlessly exchanged with continuum mechanics based method using the embedded statistical coupling method (ESCM). Simulation results are presented, followed by a discussion of the gaps and barriers to the application of concurrent coupling in integrated computational materials engineering (ICME).

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