Distribution functions examples

Another approach to the thermodynamic properties of solutions is to calculate them from the solute-solute distribution functions rather than from the virial coefficients. Approximations to these functions, which correspond to the summation of a certain class of terms in the virial series to all orders in the solute concentration (or density), have already been worked out for simple fluids, and the McMillan-Mayer theory states that the same approximations may be applied to the solute particles in solution provided the solvent-averaged potentials are used to determine the solute distribution functions. Examples of these approximations are the Percus-Yevick (PY) (1958), Hypernetted-Chain (HNC), mean-spherical (MS), and Born-Green-Yvon (BGY) theories. Before discussing them we will review some of the properties of distribution functions and their relationship to the observed thermodynamic variables. 

Unlike the solid state, the liquid state cannot be characterized by a static description. In a liquid, bonds break and refomi continuously as a fiinction of time. The quantum states in the liquid are similar to those in amorphous solids in the sense that the system is also disordered. The liquid state can be quantified only by considering some ensemble averaging and using statistical measures. For example, consider an elemental liquid. Just as for amorphous solids, one can ask what is the distribution of atoms at a given distance from a reference atom on average, i.e. the radial distribution function or the pair correlation function can also be defined for a liquid. In scattering experiments on liquids, a structure factor is measured. The radial distribution fiinction, g r), is related to the stnicture factor, S q), by... 

Typical results for a semiconducting liquid are illustrated in figure Al.3.29 where the experunental pair correlation and structure factors for silicon are presented. The radial distribution function shows a sharp first peak followed by oscillations. The structure in the radial distribution fiinction reflects some local ordering. The nature and degree of this order depends on the chemical nature of the liquid state. For example, semiconductor liquids are especially interesting in this sense as they are believed to retain covalent bonding characteristics even in the melt. 

Fig. 8.8 The bond fluctuation model. In this example three bcmds in the polymer arc incorporated into a singk effecti bond between effective moncmers . (Figure adapted from Baschnagel J, K Binder, W Paul, M Laso, U Sutcr, I Batouli [N ]ilge and T Burger 1991. On the Construction of Coarse-Grained Models for Linear Flexible Polymer-Chains -Distribution-Functions for Groups of Consecutive Monomers. Journal of Chemical Physics 93 6014-6025.)...

Most distribution functions contain an average size and a variance parameter typicaUy based on the cumulative droplet number or volume distributions. For example, the Rosin-Rammler function uses the cumulative Hquid volume as a means of expressing the distribution. It can be expressed as... 

The physics and modeling of turbulent flows are affected by combustion through the production of density variations, buoyancy effects, dilation due to heat release, molecular transport, and instabiUty (1,2,3,5,8). Consequently, the conservation equations need to be modified to take these effects into account. This modification is achieved by the use of statistical quantities in the conservation equations. For example, because of the variations and fluctuations in the density that occur in turbulent combustion flows, density weighted mean values, or Favre mean values, are used for velocity components, mass fractions, enthalpy, and temperature. The turbulent diffusion flame can also be treated in terms of a probabiUty distribution function (pdf), the shape of which is assumed to be known a priori (1). 

The distribution function/(x) can be taken as constant for example, I/Hq. We choose variables Xi, X9,. . . , Xs randomly from/(x) and form the arithmetic mean... 

An additional example of Eq. (2.2) is the distribution function commonly used in solvent extraction ... 

Theoretical results of similar quality have been obtained for thermodynamics and the structure of adsorbed fluid in matrices with m = M = 8, see Figs. 8 and 9, respectively. However, at a high matrix density = 0.273) we observe that the fluid structure, in spite of qualitatively similar behavior to simulations, is described inaccurately (Fig. 10(a)). On the other hand, the fluid-matrix correlations from the theory agree better with simulations in the case m = M = S (Fig. 10(b)). Very similar conclusions have been obtained in the case of matrices made of 16 hard sphere beads. As an example, we present the distribution functions from the theory and simulation in Fig. 11. It is worth mentioning that the fluid density obtained via GCMC simulations has been used as an input for all theoretical calculations. 

If it cannot be guaranteed that the adsorbate remains in local equilibrium during its time evolution, then a set of macroscopic variables is not sufficient and an approach based on nonequihbrium statistical mechanics involving time-dependent distribution functions must be invoked. The kinetic lattice gas model is an example of such a theory [56]. It is derived from a Markovian master equation, but is not totally microscopic in that it is based on a phenomenological Hamiltonian. We demonstrate this approach... 

Property 1 indicates tliat tlie pdf of a discrete random variable generates probability by substitution. Properties 2 and 3 restrict the values of f(x) to nonnegative real niunbers whose sum is 1. An example of a discrete probability distribution function (approaching a normal distribution - to be discussed in tlie next chapter) is provided in Figure 19.8.1. 

Distribution functions measure the (average) value of a property as a function of an independent variable. A typical example is the radial distribution function g r) which measmes the probability of finding a particle as a function of distance from a typical ... 

Frequency analysis is an alternative to moment-ratio analysis in selecting a representative function. Probability paper (see Figure 1-59 for an example) is available for each distribution, and the function is presented as a cumulative probability function. If the data sample has the same distribution function as the function used to scale the paper, the data will plot as a straight line. 

On a hexagonal lattice, for example, the two-particle distribution function, is... 

The important point we wish to re-emphasize here is that a random process is specified or defined by giving the values of certain averages such as a distribution function. This is completely different from the way in which a time function is specified i.e., by giving the value the time function assumes at various instants or by giving a differential equation and boundary conditions the time function must satisfy, etc. The theory of random processes enables us to calculate certain averages in terms of other averages (known from measurements or by some indirect means), just as, for example, network theory enables us to calculate the output of a network as a function of time from a knowledge of its input as a function of time. In either case some information external to the theory must be known or at least assumed to exist before the theory can be put to use. 

We now have at our disposal the means for easily constructing examples of distribution functions. The simplest way to do this is to note that condition (iii) forces the derivative of Fx to be non-negative at all points where the derivative exists. We shall adopt the familiar... 

Another instructive example concerns the joint distribution function of the pair of time functions Zx(t) and Z2(t) defined by... 

The complete specification of a random process requires us to have some way of writing down an infinite number of distribution functions. For practical reasons, this is an impossible task unless all the distribution functions can be specified by means of a rule that enables one to calculate any distribution function of interest in terms of a finite amount of prespecified information. The following examples will illustrate these ideas by showing howr some particular stochastic processes of interest can be defined. 

The Poisson process represents only one possible way of assigning joint distribution functions to the increments of counting functions however, in many problems, one can argue that the Poisson process is the most reasonable choice that can be made. For example, let us consider the stream of electrons flowing from cathode to plate in a vacuum tube, and let us further assume that the plate current is low enough so that the electrons do not interact with one another in the... 

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