Grain growth for astrophysics with Discontinuous Galerkin schemes

Depending on their sizes, dust grains store more or less charges, catalyse more or less chemical reactions, intercept more or less photons and stick more or less efficiently to form embryos of planets. Hence the need for an accurate treatment of dust coagulation and fragmentation in numerical modelling. However, existing algorithms for solving the coagulation equation are over-diffusive in the conditions of 3D simulations. We address this challenge by developing a high-order solver based on the Discontinuous Galerkin method. This algorithm conserves mass to machine precision and allows to compute accurately the growth of dust grains over several orders of magnitude in size with a very limited number of dust bins.Comment: 17 pages, 22 figures, Accepted for publication in MNRA

CADS:Cantera Aerosol Dynamics Simulator.

This manual describes a library for aerosol kinetics and transport, called CADS (Cantera Aerosol Dynamics Simulator), which employs a section-based approach for describing the particle size distributions. CADS is based upon Cantera, a set of C++ libraries and applications that handles gas phase species transport and reactions. The method uses a discontinuous Galerkin formulation to represent the particle distributions within each section and to solve for changes to the aerosol particle distributions due to condensation, coagulation, and nucleation processes. CADS conserves particles, elements, and total enthalpy up to numerical round-off error, in all of its formulations. Both 0-D time dependent and 1-D steady state applications (an opposing-flow flame application) have been developed with CADS, with the initial emphasis on developing fundamental mechanisms for soot formation within fires. This report also describes the 0-D application, TDcads, which models a time-dependent perfectly stirred reactor

Coagulation equations with gelation

Smoluchowski's equation for rapid coagulation is used to describe the kinetics of gelation, in which the coagulation kernel K ij models the bonding mechanism. For different classes of kernels we derive criteria for the occurrence of gelation, and obtain critical exponents in the pre- and postgelation stage in terms of the model parameters; we calculate bounds on the time of gelation t c , and give an exact postgelation solution for the model K ij =( ij ω ) (ω>1/2) and K ij =a i+j ( a >1). For the model K ij = i ω + j ω ( ω <1, without gelation) initial solutions are given. It is argued that the kernel K ij ∼ ij ω with ω≃1−1/d ( d is dimensionality) effectively models the sol-gel transformation in polymerizing systems and approximately accounts for the effects of cross-linking and steric hindrance neglected in the classical theory of Flory and Stockmayer ( Ω =1). For all Ω the exponents, t=Ω +3/2 and σ=Ω −1/2, γ =(3/2− Ω)/(Ω − 1/2) and Β =1, characterize the size distribution, at and slightly below the gel point, under the assumption that scaling is valid.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45146/1/10955_2005_Article_BF01019497.pd

Scaling Theory and Exactly Solved Models In the Kinetics of Irreversible Aggregation

The scaling theory of irreversible aggregation is discussed in some detail. First, we review the general theory in the simplest case of binary reactions. We then extend consideration to ternary reactions, multispecies aggregation, inhomogeneous situations with arbitrary size dependent diffusion constants as well as arbitrary production terms. A precise formulation of the scaling hypothesis is given as well as a general theory of crossover phenomena. The consequences of this definition are described at length. The specific issues arising in the case in which an infinite cluster forms at finite times (the so-called gelling case) are discussed, in order to address discrepancies between theory and recent numerical work. Finally, a large number of exactly solved models are reviewed extensively with a view to pointing out precisely in which sense the scaling hypothesis holds in these various models. It is shown that the specific definition given here will give good results for almost all cases. On the other hand, we show that it is usually possible to find counterexamples to stronger formulations of the scaling hypothesis.Comment: 160 pp. 1 figure, submitted to Physics Report

Numerical approximations of population balance equations in particulate systems

Magdeburg, Univ., Fak. für Mathematik, Diss., 2006von Jitendra Kuma

A Coupled Stochastic-Deterministic Method for the Numerical Solution of Population Balance Systems

In this thesis, a new algorithm for the numerical solution of population balance systems is proposed and applied within two simulation projects. The regarded systems stem from chemical engineering. In particular, crystallization processes in fluid environment are regarded. The descriptive population balance equations are extensions of the classical Smoluchowski coagulation equation, of which they inherit the numerical difficulties introduced with the coagulation integral, especially in regard of higher dimensional particle models. The new algorithm brings together two different fields of numerical mathematics and scientific computing, namely a stochastic particle simulation based on a Markov process Monte—Carlo method, and (deterministic) finite element schemes from computational fluid dynamics. Stochastic particle simulations are approved methods for the solution of population balance equations. Their major advantages are the inclusion of microscopic information into the model while offering convergence against solutions of the macroscopic equation, as well as numerical efficiency and robustness. The embedding of a stochastic method into a deterministic flow simulation offers new possibilities for the solution of coupled population balance systems, especially in regard of the microscopic details of the interaction of particles. In the thesis, the new simulation method is first applied to a population balance system that models an experimental tube crystallizer which is used for the production of crystalline aspirin. The device is modeled in an axisymmetric two-dimensional fashion. Experimental data is reproduced in moderate computing time. Thereafter, the method is extended to three spatial dimensions and used for the simulation of an experimental, continuously operated fluidized bed crystallizer. This system is fully instationary, the turbulent flow is computed on-the-fly. All the used methods from the simulation of the Navier—Stokes equations, the simulation of convection-diffusion equations, and of stochastic particle simulation are introduced, motivated and discussed extensively. Coupling phenomena in the regarded population balance systems and the coupling algorithm itself are discussed in great detail. Furthermore, own results about the efficient numerical solution of the Navier—Stokes equations are presented, namely an assessment of fast solvers for discrete saddle point problems, and an own interpretation of the classical domain decompositioning method for the parallelization of the finite element method