Random walks simple models

Various mathematical concepts and techniques have been used to derive the functions that describe the different types of dispersion and to simplify further development of the rate theory two of these procedures will be discussed in some detail. The two processes are, firstly, the Random Walk Concept [1] which was introduced to the rate theory by Giddings [2] and, secondly, the mathematics of diffusion which is both critical in the study of dispersion due to longitudinal diffusion and that due to solute mass transfer between the two phases. The random walk model allows the relatively simple derivation of the variance contributions from two of the dispersion processes that occur in the column and, so, this model will be the first to be discussed. 

Equation (1) can be used in a general way to determine the variance resulting from the different dispersion processes that occur in an LC column. However, although the application of equation (1) to physical chemical processes may be simple, there is often a problem in identifying the average step and, sometimes, the total number of steps associated with the particular process being considered. To illustrate the use of the Random Walk model, equation (1) will be first applied to the problem of radial dispersion that occurs when a sample is placed on a packed LC column in the manner of Horne et al. [3]. 

We close these introductory remarks with a few comments on the methods which are actually used to study these models. They will for the most part be mentioned only very briefly. In the rest of this chapter, we shall focus mainly on computer simulations. Even those will not be explained in detail, for the simple reason that the models are too different and the simulation methods too many. Rather, we refer the reader to the available textbooks on simulation methods, e.g.. Ref. 32-35, and discuss only a few technical aspects here. In the case of atomistically realistic models, simulations are indeed the only possible way to approach these systems. Idealized microscopic models have usually been explored extensively by mean field methods. Even those can become quite involved for complex models, especially for chain models. One particularly popular and successful method to deal with chain molecules has been the self-consistent field theory. In a nutshell, it treats chains as random walks in a position-dependent chemical potential, which depends in turn on the conformational distributions of the chains in... 

When the random-walk model is expanded to take into account the real structures of solids, it becomes apparent that diffusion in crystals is dependent upon point defect populations. To give a simple example, imagine a crystal such as that of a metal in which all of the atom sites are occupied. Inherently, diffusion from one normally occupied site to another would be impossible in such a crystal and a random walk cannot occur at all. However, diffusion can occur if a population of defects such as vacancies exists. In this case, atoms can jump from a normal site into a neighboring vacancy and so gradually move through the crystal. Movement of a diffusing atom into a vacant site corresponds to movement of the vacancy in the other direction (Fig. 5.7). In practice, it is often very convenient, in problems where vacancy diffusion occurs, to ignore atom movement and to focus attention upon the diffusion of the vacancies as if they were real particles. This process is therefore frequently referred to as vacancy diffusion... 

In this relatively simple random walk model an ion (e.g., a cation) can move freely between two adjacent active centres on an electrode (e.g., cathode) with an equal probability A. The centres are separated by L characteristic length units. When the ion arrives at one of the centres, it will react (e.g., undergoes a cathodic reaction) and the random walk is terminated. The centres are, therefore absorbing states. For the sake of illustration, L = 4 is postulated, i.e., Si and s5 are the absorbing states, if 1 and 5 denote the positions of the active centres on the surface, and s2, s3, and s4 are intermediate states, or ion positions, LIA characteristic units apart. The transitional probabilities (n) = Pr[i-, —>, Sj in n steps] must add up to unity, but their individual values can be any number on the [0, 1] domain. 

To show the relationship between pn(m) expressing the probabilities of numbers and p x) describing a continuous spatial distribution of a quantity like concentration, we make use of the analogy between the integers n and m, which describe the simple random walk model shown in Fig. 18.1, and the time and space coordinates t and x, that is t = n At and x = m Ax. The incremental quantities, At and Ax, are characteristic for random motions the latter is the mean free path which is commonly denoted as X = Ax, the former is associated with the mean velocity ux= Ax/At = XIAt. Thus, we get the following substitution rules ... 

Below we will show that diffusivity D can be interpreted in the framework of a random walk model (see Eqs. 18-16 and 18-17). Particularly, D is related to the random walk parameters, mean free path X and mean velocity ux, by the simple relation ... 

In order to fully appreciate the consequences of the rather simple mathematical rules which describe the random walk, we move one step further and combine Fick s first law with the principle of mass balance which we used in Section 12.4 when deriving the one-box model. For simplicity, here we just consider diffusion along one spatial dimension (e.g., along the x-axis.)... 

Now, everything falls into place We set out to study the laws of random walk by using the simple model of Fig. 18 and found the Bernoulli coefficients. We then saw that for large n (which is equivalent to large times), the Bernoulli coefficients can be approximated by a normal distribution whose standard deviation, a, grows in proportion to the square root of time, tm (Eq. 18-3). And now it turns out that the solution of the Fick s second law for unbounded diffusion is also a normal distribution. In fact, the analogy between Eqs. 18-3b and 18-17 gave the basis for the law by Einstein and Smoluchowski (Eq. 18-17) that we used earlier (Eq. 18-8). The expression (2Dt)U2 will also show up in other solutions of the diffusion equation. 

In Figure 1, a is plotted vs. ax for a face-centered lattice (a close-packed lattice) and for a simple cubic lattice (a loose-packed lattice). We notice that (1) the dependence of a on ax can be regarded as being practically the same for both lattices and that, (2) tx undergoes a rapid change around x = xc, which is the point at which a = 0 (Fig. 1). However, p/a does not attain the value it would have for the case of the unrestricted random walk model at x = xc, since at this point, p/a > 1 (Fig. 2), while for unrestricted chain pja = 1. Moreover, the dependence of p/a on ax is not the same for the two lattices while a as a function of ax is practically independent of the lattice. 

It is not surprising that the diffusion equation appears, because physical diffusion is nothing but a random walk with small steps, although not necessarily all of the same size as in this simple model. 

The simplest mechanisms leading to the dispersion (spreading) of a zone s molecules can be described by the classical random-walk model [9], as noted in Section 5.3. However this model does not fully account for the complexities of migration. It gives, instead, a simple approximation which inherits the most essential and important properties (foremost of all the randomness) of the real migration process. The random-walk model has been used in a similar first-approximation role in many fields (chemical kinetics, diffusion, polymer chain configuration, etc.) and is thus important in its own right. 

In a second model II, more emphasis is given to the motion toward the output and less to the other directions. The probabilities for motion in the different directions are now defined differently. While in the simple random walk the probability for motion in a specific direction is 1/z, here the probability for motion in the output direction is (1/z) + e, while the probability in any of the other five directions is... 

In physics, the random walk method has already been in use for decades to understand and model diffusion processes. Prickett et al. (1981) developed a simple model for groundwater transport to calculate the migration of contamination. An essential advantage of the methods of random walk and particle tracking is that they are free of numeric dispersion and oszillations (Abbot 1966). 

A simple model demonstrating the essential ideas of the theory of random walk is illustrated in Fig. 9.1 (Csanady, 1973). Let us assume that a particle is located at x = 0 at time t= 0. Its movements, which... 

SF theory is a statistical thermodynamic model in which chain conformations are formulated as step-weighted random walks in an interfacial lattice (Figure 2). A simple case involves the adsorption of a flexible, linear, homo-disperse, uncharged molecule at a uniform planar surface. Interactions among... 

---