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Public Shared Function Create(ByVal purchID As Integer) _ As Purchase Dim purch As Purchase ' Create a new instance purch = New Purchase() ' Populate the object If purchID = 1 Then purchPurchaseDate = #4/4/2006# purchQuantity = 5 End If If purchID = 2 Then purchPurchaseDate = #4/6/2006# purchQuantity = 12 End If ' Reset the entity's state purchDataStateChanged(EntityStateEnumUnchanged) Return purch End Function

where l)~~)i is gi\'cll by (8.4.49). 'rhus for the mi~ture case, we ha\'e to sLore three arrays of probabilities, (8.4.56), (8.4.47), and (BAAS), to select i\ candidate particle i, pair (i,j), and triplet (i,i,/) to frOIll siugle-. double-, and triple-bond, respectively, with the test particle. In Figs. .4.10 and 8.4.11, we illustrate lobe Monte Carlo results of pair distribution functions for a medium with adhesive binnry rnb:t.ures of parti. cles. Por Fig. 8.4.10 we placed .N = 1144 spheres inside a unit cell ill which N'l = 1000 of them are smaller particles {\nd .'''i1 = Il14 Are If\1"ger in si:r.c with 02 = 0.6a.1 The volume fractions are 11 = 0.1 and h = 0.15. They have t.he stickiDess with Til = 712 = -'P,!2 = 0.2. In Fig. 8.4.11 we consider the cru;;c of

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particles having different adhesive forces. This system has a high concentration of larger particles and a much less volume fraction of smaller particles. The parameters are N = 885, N 1 = 200, N 2 = 685, a2 = 0.4a1, h = 0.2, 12 = 0.04, and the stickiness parameters are T11 = 1, T12 = 0.2, and 722 = 00. The Monte Carlo results for both cases are obtained by averaging over 12,000 passes. In general, the Monte Carlo results are in good agreement with the Percus-Yevick approximation calculations. In Fig. 8.4.12, we show a typical configuration for a 3-D sample of sticky spheres with T = 0.2 and volume fraction f = 0.35 [Zurk, 1995]. The generated realizations of random media can be used for the numerical solutions of Maxwell's equations.

In this section we apply Metropolis shuffling process to generate the positions and orientations of a system of densely packed spheroids. For nonspherical particles such as spheroids, rotations need to be performed in addition to displacements. To simplify the simulation process, we consider N spheroidal particles of the same shape that are randomly placed in a simple cubic cell. Periodic boundary conditions are employed to minimize the finite size effect. A central primary cell with side length L is set up to contain the N spheroids, and this central cell is surrounded by the periodic images of itself. The state of each spheroid is specified by the location 'P = (x, y, z) of its center and the orientation 0 = ((), ) of its symmetry axis. Given the particle concentration, size, and shape, the N spheroids are initially aligned along the i-direction, and are placed in close contact with each other but without overlapping. In such an initial set-up, we are able to create configurations of higher fractional volumes. The shuffling process consists of a succession of trial moves in which we attempt to change the locations and orientations of particles by the combination of random displacements and rotations. For the spheroid j, the new location 'Pj is determined from its present location 'Pj by

(8.5.1) (8.5.2) (8.5.3)

where ~ is the maximum displacement allowed in each spatial movement, and 'f]x, 'f]y, and 'f]z are random numbers uniformly distributed between -1 and 1. Each spheroid has an axis of symmetry. We also perform random

NOTE: The code in the Create method that hard-codes the values is temporary until the data can be retrieved from a database, as described in 8, Building the Data Access Layer

j =

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