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Convex duality and entropy-based moment closure: Characterizing degenerate densities

Cory D. Hauck (cdhauck***at***lanl.gov)
C. David Levermore (lvrmr***at***math.umd.edu)
Andre L. Tits (andre***at***umd.edu)

Abstract: A common method for constructing a function from a finite set of moments is to solve a constrained minimization problem. The idea is to find, among all functions with the given moments, that function which minimizes a physically motivated, strictly convex functional. In the kinetic theory of gases, this functional is the kinetic entropy; the given moments are macroscopic densities; and the solution to the constrained minimization problem is used to formally derive a closed system of partial differential equations which describe how the macroscopic densities evolve in time. Moment equations are useful because they simplify the kinetic, phase-space description of a gas, and with entropy-based closures, they retain many of the fundamental properties of kinetic transport.

Unfortunately, in many situations, macroscopic densities can take on values for which the constrained minimization problem does not have a solution. Essentially, this is because the moments are not continuous functionals with respect to the L1 topology. In this paper, we give a geometric description of these so-called degenerate densities in the most general possible setting. Our key tool is the complementary slackness condition that is derived from a dual formulation of a minimization problem with relaxed constraints. We show that the set of degenerate densities is a union of convex cones and, under reasonable assumptions, that this set is small in both a topological and measure theoretic sense. This result is important for further assessment and implementation of entropy-based moment closures.

Keywords: convex duality, convex optimization, optimization in function spaces, kinetic theory, entropy-based closures, moment equations, gas dynamics

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Infinite Dimensional Optimization

Citation: Technical report ISR 2007-9, Institute for Systems Research, University of Maryland, MD 20742, May 2007.

Download: [PDF]

Entry Submitted: 05/26/2007
Entry Accepted: 05/27/2007
Entry Last Modified: 11/20/2007

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