Dynamics of Entangled Polyethylenes Under Shear Via Single-Chain In Mean-Field Simulations

The simulation of long-chain entangled polymers under flow conditions, either using nonequilibrium Monte Carlo (NEMC) or molecular dynamics (NEMC), on top of a detailed atomistic representation of the polymer melt is generally too computationally intensive to be tractable using contemporary hardware and modeling algorithms. Thus, to date, only relatively short-chain, unentangled fluids have been simulated using either NEMC or NEMD. In this presentation, issues related to computational tractability are avoided by employing the recently developed Single-Chain-in-Mean-Field (SCMF) simulation approach [K. Ch. Daoulas and M. Müller, J. Chem. Phys., 2006, 125, pp. 184904], in conjunction with NEMC simulation methodology. Specifically, the polymer melt is represented on a mesoscopic level via a system of bead-spring polymer chains in the (n,V,T,a) ensemble [V. G. Mavrantzas and D. N. Theodorou, Macromolecules, 1998, 31, pp. 6310]. The non-bonded interactions are captured via a quadratic compressibility term, originally introduced by Helfand and Tagami [E. Helfand and Y. Tagami, J. Chem. Phys., 1972, 56, pp. 3592]. The number of entanglements per chain is controlled by properly selecting the equilibrium invariant degree of polymerization.

The entanglement behavior as a function of chain length, as obtained from SCMF simulations of the coarse-grained model under equilibrium conditions, was compared with the predictions of atomistic simulations available in the literature. Afterwards, NEMC simulations under an applied shear flow were performed to obtain steady-state polymer configurations at varying field strengths, and then these configurations were analyzed using the primitive path analysis scheme developed by Kroger [Comput. Phys. Commun., 2005, 168, pp. 209] to estimate the degree of chain entanglement. This presentation will discuss the results of this analysis and potential improvements to it which are currently under development.