Coupled flow-polymer dynamics via statistical field theory: Modeling and computation

Field-theoretic models, which replace interactions between polymers with interactions between polymers and one or more conjugate fields, offer a systematic framework for coarse-graining of complex fluids systems. While this approach has been used successfully to investigate a wide range of polymer formulations at equilibrium, field-theoretic models often fail to accurately capture the non-equilibrium behavior of polymers, especially in the early stages of phase separation. Here the “two-fluid” approach serves as a useful alternative, treating the motions of fluid components separately in order to incorporate asymmetries between polymer molecules. In this work we focus on the connection of these two theories, drawing upon the strengths of each of the approaches in order to couple polymer microstructure with the dynamics of the flow in a systematic way. For illustrative purposes we work with an inhomogeneous melt of elastic dumbbell polymers, though our methodology will apply more generally to a wide variety of inhomogeneous systems. First we derive the model, incorporating thermodynamic forces into a two-fluid model for the flow through the introduction of conjugate chemical potential and elastic strain fields for the polymer density and stress. The resulting equations are composed of a system of fourth order PDEs coupled with a non-linear, non-local optimization problem to determine the conjugate fields. The coupled system is severely stiff and with a high degree of computational complexity. Next, we overcome the formidable numerical challenges posed by the model by designing a robust semi-implicit method based on linear asymptotic behavior of the leading order terms at small scales, by exploiting the exponential structure of global (integral) operators, and by parallelizing the non-linear optimization problem. The semi-implicit method effectively removes the fourth order stability constraint associated with explicit methods and we observe only a first order time-step restriction. The algorithm for solving the non-linear optimization problem, which takes advantage of the form of the operators being optimized, reduces the overall simulation time by several orders of magnitude. We illustrate the methodology with several examples of phase separation in an initially quiescent flow.