Search

Models of kinetic theory provide a coarse-grained description of molecular configurations wherein atomistic processes are ignored. The Fokker-Planck equation related to the kinetic theory descriptions must be solved for the distribution function in both physical and configuration spaces. When the model involves high dimensional spaces (including physical and conformation spaces and time) standard discretization techniques fail due to excessive computation requirements. In this paper, we revisit some model reduction techniques recently proposed to circumvent those difficulties, exploring other new application areas related to entangled polymer models as well as the use of such reduced models for treating complex flows in which the distribution function involves both the physical and the conformation coordinates.

Abstract: Kinetic theory models described within the Fokker–Planck formalism involve high-dimensional spaces (including physical and conformation spaces and time). One appealing strategy for treating this kind of problems lies in the use of separated representations and tensor product approximations basis. This technique that was introduced in a former work [A. Ammar, B. Mokdad, F. Chinesta, R. Keunings, A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids, J. Non-Newtonian Fluid Mech. 139 (2006) 153–176] for treating steady state kinetic theory models is extended here for treating transient models. [Copyright &y& Elsevier]

Abstract: Kinetic theory models involving the Fokker–Planck equation can be accurately discretized using a mesh support (finite elements, finite differences, finite volumes, spectral techniques, etc.). However, these techniques involve a high number of approximation functions. In the finite element framework, widely used in complex flow simulations, each approximation function is related to a node that defines the associated degree of freedom. When the model involves high dimensional spaces (including physical and conformation spaces and time), standard discretization techniques fail due to an excessive computation time required to perform accurate numerical simulations. One appealing strategy that allows circumventing this limitation is based on the use of reduced approximation basis within an adaptive procedure making use of an efficient separation of variables. [Copyright &y& Elsevier]