Existence of global weak solutions to compressible isentropic finitely extensible nonlinear bead-spring chain models for dilute polymers (1407.3763v3)
Abstract: We prove the existence of global-in-time weak solutions to a general class of models that arise from the kinetic theory of dilute solutions of nonhomogeneous polymeric liquids, where the polymer molecules are idealized as bead-spring chains with finitely extensible nonlinear elastic (FENE) type spring potentials. The class of models under consideration involves the unsteady, compressible, isentropic, isothermal Navier-Stokes system in a bounded domain $\Omega$ in $\mathbb{R}d$, $d = 2$ or $3$, for the density, the velocity and the pressure of the fluid. The right-hand side of the Navier-Stokes momentum equation includes an elastic extra-stress tensor, which is the sum of the classical Kramers expression and a quadratic interaction term. The elastic extra-stress tensor stems from the random movement of the polymer chains and is defined through the associated probability density function that satisfies a Fokker-Planck-type parabolic equation, a crucial feature of which is the presence of a centre-of-mass diffusion term. We require no structural assumptions on the drag term in the Fokker-Planck equation; in particular, the drag term need not be corotational. With a nonnegative initial density for the continuity equation; a square-integrable initial velocity datum for the Navier-Stokes momentum equation; and a nonnegative initial probability density function for the Fokker-Planck equation, which has finite relative entropy with respect to the Maxwellian associated with the spring potential in the model, we prove, via a limiting procedure on certain discretization and regularization parameters, the existence of a global-in-time bounded-energy weak solution to the coupled Navier-Stokes-Fokker-Planck system, satisfying the prescribed initial condition.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.