Boris Baeumer

Subordinated advection‐dispersion equation for contaminant transport

A mathematical method called subordination broadens the applicability of the classical advection-dispersion equation for contaminant transport. In this method the time variable is randomized to represent the operational time experienced by different particles. In a highly heterogeneous aquifer the operational time captures the fractal properties of the medium. This leads to a simple, parsimonious model of contaminant transport that exhibits many of the features (heavy tails, skewness, and non-Fickian growth rate) typically seen in real aquifers. We employ a stable subordinator that derives from physical models of anomalous diffusion involving fractional derivatives. Applied to a one- dimensional approximation of the MADE-2 data set, the model shows excellent agreement.

Tempered stable Lévy motion and transient super-diffusion

The space-fractional diffusion equation models anomalous super-diffusion. Its solutions are transition densities of a stable Levy motion, representing the accumulation of power-law jumps. The tempered stable Levy motion uses exponential tempering to cool these jumps. A tempered fractional diffusion equation governs the transition densities, which progress from super-diffusive early-time to diffusive late-time behavior. This article provides finite difference and particle tracking methods for solving the tempered fractional diffusion equation with drift. A temporal and spatial second-order Crank-Nicolson method is developed, based on a finite difference formula for tempered fractional derivatives. A new exponential rejection method for simulating tempered Levy stables is presented to facilitate particle tracking codes.

Numerical solutions for fractional reaction-diffusion equations

Fractional diffusion equations are useful for applications in which a cloud of particles spreads faster than predicted by the classical equation. In a fractional diffusion equation, the second derivative in the spatial variable is replaced by a fractional derivative of order less than two. The resulting solutions spread faster than the classical solutions and may exhibit asymmetry, depending on the fractional derivative used. Fractional reaction-diffusion equations combine the fractional diffusion with a classical reaction term. In this paper, we develop a practical method for numerical solution of fractional reaction-diffusion equations, based on operator splitting. Then we present results of numerical simulations to illustrate the method, and investigate properties of numerical solutions. We also discuss applications to biology, where the reaction term models species growth and the diffusion term accounts for movements.

Brownian subordinators and fractional Cauchy problems

A Brownian time process is a Markov process subordinated to the absolute value of an independent one-dimensional Brownian motion. Its transition densities solve an initial value problem involving the square of the generator of the original Markov process. An apparently unrelated class of processes, emerging as the scaling limits of continuous time random walks, involves subordination to the inverse or hitting time process of a classical stable subordinator. The resulting densities solve fractional Cauchy problems, an extension that involves fractional derivatives in time. In this paper, we will show a close and unexpected connection between these two classes of processes and, consequently, an equivalence between these two families of partial differential equations.

SPACE-TIME FRACTIONAL DERIVATIVE OPERATORS

Evolution equations for anomalous diusion employ fractional deriva- tives in space and time. Linkage between the space-time variables leads to a new type of fractional derivative operator. This paper develops the mathe- matical foundations of those operators.

Modeling mixed retention and early arrivals in multidimensional heterogeneous media using an explicit Lagrangian scheme

This study develops an explicit two-step Lagrangian scheme based on the renewal-reward process to capture transient anomalous diffusion with mixed retention and early arrivals in multidimensional media. The resulting 3-D anomalous transport simulator provides a flexible platform for modeling transport. The first step explicitly models retention due to mass exchange between one mobile zone and any number of parallel immobile zones. The mobile component of the renewal process can be calculated as either an exponential random variable or a preassigned time step, and the subsequent random immobile time follows a Hyper-exponential distribution for finite immobile zones or a tempered stable distribution for infinite immobile zones with an exponentially tempered power-law memory function. The second step describes well-documented early arrivals which can follow streamlines due to mechanical dispersion using the method of subordination to regional flow. Applicability and implementation of the Lagrangian solver are further checked against transport observed in various media. Results show that, although the time-nonlocal model parameters are predictable for transport with retention in alluvial settings, the standard time-nonlocal model cannot capture early arrivals. Retention and early arrivals observed in porous and fractured media can be efficiently modeled by our Lagrangian solver, allowing anomalous transport to be incorporated into 2-D/3-D models with irregular flow fields. Extensions of the particle-tracking approach are also discussed for transport with parameters conditioned on local aquifer properties, as required by transient flow and nonstationary media.

Particle tracking for fractional diffusion with two time scales

Abstract Previous work [Y. Zhang, M.M. Meerschaert, B. Baeumer, Particle tracking for time-fractional diffusion, Phys. Rev. E 78 (2008) 036705] showed how to solve time-fractional diffusion equations by particle tracking. This paper extends the method to the case where the order of the fractional time derivative is greater than one. A subordination approach treats the fractional time derivative as a random time change of the corresponding Cauchy problem, with a first derivative in time. One novel feature of the time-fractional case of order greater than one is the appearance of clustering in the operational time subordinator, which is non-Markovian. Solutions to the time-fractional equation are probability densities of the underlying stochastic process. The process models movement of individual particles. The evolution of an individual particle in both space and time is captured in a pair of stochastic differential equations, or Langevin equations. Monte Carlo simulation yields particle location, and the ensemble density approximates the solution to the variable coefficient time-fractional diffusion equation in one or several spatial dimensions. The particle tracking code is validated against inverse transform solutions in the simplest cases. Further applications solve model equations for fracture flow, and upscaling flow in complex heterogeneous porous media. These variable coefficient time-fractional partial differential equations in several dimensions are not amenable to solution by any alternative method, so that the grid-free particle tracking approach presented here is uniquely appropriate.

Reflected spectrally negative stable processes and their governing equations

This paper explicitly computes the transition densities of a spectrally negative stable process with index greater than one, reflected at its infimum. First we derive the forward equation using the theory of sun-dual semigroups. The resulting forward equation is a boundary value problem on the positive half-line that involves a negative Riemann-Liouville fractional derivative in space, and a fractional reflecting boundary condition at the origin. Then we apply numerical methods to explicitly compute the transition density of this space-inhomogeneous Markov process, for any starting point, to any desired degree of accuracy. Finally, we discuss an application to fractional Cauchy problems, which involve a positive Caputo fractional derivative in time.

Existence, uniqueness and regularity for a class of semilinear stochastic Volterra equations with multiplicative noise

Abstract We consider a class of semilinear Volterra type stochastic evolution equation driven by multiplicative Gaussian noise. The memory kernel, not necessarily analytic, is such that the deterministic linear equation exhibits a parabolic character. Under appropriate Lipschitz-type and linear growth assumptions on the nonlinear terms we show that the unique mild solution is mean- p Holder continuous with values in an appropriate Sobolev space depending on the kernel and the data. In particular, we obtain pathwise space–time (Sobolev–Holder) regularity of the solution together with a maximal type bound on the spatial Sobolev norm. As one of the main technical tools we establish a smoothing property of the derivative of the deterministic evolution operator family.

A subordinated advection model for uniform bed load transport from local to regional scales

Sediment tracers moving as bed load can exhibit anomalous dispersion behavior deviating from Fickian diffusion. The presence of heavy-tailed resting time distributions and thin-tailed step length distributions motivate adoption of fractional-derivative models (FDMs) to describe sediment dispersion, but these models require many parameters that are difficult to quantify. Here we propose a considerably simplified FDM for anomalous transport of uniformly sized grains along straight channels, the subordinated advection equation (SAE), which is based on the concept of time subordination. Unlike previous FDM models with time index γ between 0 and 1, our SAE model adopts a value of γ between 1 and 2. This γ describes random velocities deviating significantly from the mean velocity and models both long resting periods and relatively fast displacements. We show that the model quantifies the dynamics of four bed load transport experiments recorded in the literature. In addition to γ, SAE model parameters—velocity and capacity coefficient—are related to the mean and variance of particle velocities, respectively. Successful application of the SAE model also implies a universal probability density for the heavy-tailed waiting time distribution (with finite mean) and a relatively lighter tailed step length distribution for uniform bed load transport from local to regional scales.