Cory D. Hauck

Sparse dynamics for partial differential equations

We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms.

High-order entropy-based closures for linear transport in slab geometry II: A computational study of the optimization problem

We present a numerical algorithm to implement entropy-based (

Convex Duality and Entropy-Based Moment Closures: Characterizing Degenerate Densities

M_N

Positive

) moment models in the context of a simple, linear kinetic equation for particles moving through a material slab. The closure for these models---as is the case for all entropy-based models---is derived through the solution of a constrained, convex optimization problem. The algorithm has two components. The first component is a discretization of the moment equations which preserves the set of realizable moments, thereby ensuring that the optimization problem has a solution (in exact arithmetic). The discretization is a second-order kinetic scheme which uses MUSCL-type limiting in space and a strong-stability-preserving, Runge--Kutta time integrator. The second component of the algorithm is a Newton-based solver for the dual optimization problem, which uses an adaptive quadrature to evaluate integrals in the dual objective and its derivatives. The accuracy of the numerical solution to the dual problem plays a key role in the time step restric...

P_N

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

Closures

L^{1}

Adaptive change of basis in entropy-based moment closures for linear kinetic equations

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 a measure-theoretic sense. This result is important for further assessment and implementation of entropy-based moment closures.

A Comparison of Moment Closures for Linear Kinetic Transport Equations: The Line Source Benchmark

We introduce a modification to the standard spherical harmonic closure used with linear kinetic equations of particle transport. While the standard closure is known to produce negative particle concentrations, the modification corrects this defect by requiring that the ansatz used to close the equations itself be a nonnegative function. We impose this requirement via explicit constraints in a quadratic optimization problem.

Radiation transport modeling using extended quadrature method of moments

Entropy-based ( M N ) moment closures for kinetic equations are defined by a constrained optimization problem that must be solved at every point in a space-time mesh, making it important to solve these optimization problems accurately and efficiently. We present a complete and practical numerical algorithm for solving the dual problem in one-dimensional, slab geometries. The closure is only well-defined on the set of moments that are realizable from a positive underlying distribution, and as the boundary of the realizable set is approached, the dual problem becomes increasingly difficult to solve due to ill-conditioning of the Hessian matrix. To improve the condition number of the Hessian, we advocate the use of a change of polynomial basis, defined using a Cholesky factorization of the Hessian, that permits solution of problems nearer to the boundary of the realizable set. We also advocate a fixed quadrature scheme, rather than adaptive quadrature, since the latter introduces unnecessary expense and changes the computationally realizable set as the quadrature changes. For very ill-conditioned problems, we use regularization to make the optimization algorithm robust. We design a manufactured solution and demonstrate that the adaptive-basis optimization algorithm reduces the need for regularization. This is important since we also show that regularization slows, and even stalls, convergence of the numerical simulation when refining the space-time mesh. We also simulate two well-known benchmark problems. There we find that our adaptive-basis, fixed-quadrature algorithm uses less regularization than alternatives, although differences in the resulting numerical simulations are more sensitive to the regularization strategy than to the choice of basis.

Temporal Regularization of the

We discuss several moment closure models for linear kinetic equations that have been developed over the past few years as alternatives to classical spectral and collocation methods. We then present numerical simulation results for a challenging benchmark problem known as the line source and observe the relative strengths and weaknesses of each closure. The results should prove useful for application scientists in making decisions about which methods are best suited to a given problem. They also provide a guide to help numerical experts who are interested in developing new, improved methods.