On the well-posedness of a nonlocal kinetic model for dilute polymers with anomalous diffusion
Marvin Fritz, Endre Süli, Barbara Wohlmuth
TL;DR
This work establishes global-in-time existence and, under regularity, uniqueness of weak solutions to a nonlocal-in-time Navier–Stokes–Fokker–Planck system modeling dilute polymers with memory. The authors employ energy–entropy methods, maximum-principle arguments for nonnegativity, and a novel nonlocal Aubin–Lions compactness framework to handle memory effects driven by a Prabhakar–Caputo type kernel. A multi-level Galerkin truncation scheme is developed, with careful passes to the limits in velocity and configuration-space discretizations, ensuring strong convergence and nonnegativity of the probability density. The results provide a rigorous well-posedness foundation for subdiffusive polymeric flows with memory and suggest avenues for structure-preserving numerical schemes and future research on the impact of different temporal derivatives.
Abstract
In this work, we study a class of nonlocal-in-time kinetic models of incompressible dilute polymeric fluids. The system couples a macroscopic balance of linear momentum equation with a mezoscopic subdiffusive Fokker-Planck equation governing the evolution of the probability density function of polymer configurations. The model incorporates nonlocal features to capture subdiffusive and memory-type phenomena. Our main result asserts the existence of global-in-time large-data weak solutions to this nonlocal system. The proof relies on an energy estimate involving a suitable relative entropy, which enables us to handle the critical general non-corotational drag term that couples the two equations. As a side result, we prove nonnegativity of the probability density function. A crucial step in our analysis is to establish strong convergence of the sequence of Galerkin approximations by a combination of techniques, involving a novel compactness result for nonlocal PDEs. Lastly, we prove the uniqueness of weak solutions with sufficient regularity.
