(268a) Strategy for Reducing Molecular Ensemble Size for Efficient Rheological Modeling of Commercial Polymers

We develop a method for quantitative predicting the linear and nonlinear rheology of polydisperse polymers by selecting a small number of representative polymer molecules from the large ensemble of chains comprising the overall polymer. Specifically, we use a numerical inversion of the Double Reptation model to select a representative molecular ensemble composed of less than ten molecular species by optimally fitting the linear rheology of polydisperse polymers, and then use the regressed parameters to compute the nonlinear rheology via the Rolie-Double-Poly (RDP) model [1]. After successfully capturing the linear rheology of linear low-density polyethylene (LLDPE) and of low-density polyethylene (LDPE), the method then successfully predicts the linear rheology of blends of the two using the same representative molecules found by fitting each of the pure polymers. Further fitting the extensional rheology of the LDPE required using the “priorities” and stretch relaxation times of the representative molecules as adjustable parameters, which were then held fixed when predicting the extensional rheology of blends of the LDPE with the LLDPE roughly as successfully as does the “Branch-on-Branch” model [3]. The reduction in the number of representative polymer species offers new opportunities for faster simulations of flowing polymers, as well as for prediction of segmental orientation to be used in the modeling of flow-induced crystallization.

[1] V. A. H. Boudara, J. D. Peterson, L. G. Leal, and D. J. Read, Nonlinear Rheology of Polydisperse Blends of Entangled Linear Polymers: Rolie-Double-Poly Models, Journal of Rheology 63, 71 (2018).

[2] H. Münstedt, Dependence of the Elongational Behavior of Polystyrene Melts on Molecular Weight and Molecular Weight Distribution, Journal of Rheology 24, 847 (1980).

[3] C. Das, D. J. Read, D. Auhl, M. Kapnistos, J. den Doelder, I. Vittorias, and T. C. B. McLeish, Numerical Prediction of Nonlinear Rheology of Branched Polymer Melts, Journal of Rheology 58, 737 (2014).