Introduction. A flow involving more than a single phase is classified as multiphase or non homogeneous, such as liquid flows in porous Fiber media. We are interested in the dynamics of the evolving interfaz between the distinct phases during such non homogeneous flows in a Fiber Mass. The dynamics of such flow are dominated by surface tensions, porous media Anisotropy and non homogenity, Fiber volume fraction, and Fiber wetting behavior, the uncertain Structural conditions in fibrous media, including the susceptibility todo Even small loads, as well as the tortuous connectivity of their open pores and porly defined boundaries, result in complex local non homogeneous flows and interfacial.

Evolution. This complexity, in Many cases, becomes prohibitive for the development of analytical theories describing these phenomena. The wetting and wicking of Fiber Mass constitute a class of flows that have critical scientific and first of all practical.

Significance.

Idea.

Adapt Montecarlo simulation based on the ising model for a description of the wetting and wicking phenomena in fibrous media. We introduce here a 3-d ising model, incorporated with the estochastic dynamics and the method of importance sampling, which enables us todo interpret the model outputs in terms of wicking dynamics, the essential principle of this model is based on the discreetion of the whole system of a fibrous Mass, a liquid source, and a wetting configuration at any given moment. The continuous media in the system, including the solid, liquid, and gas, are all divided as assemblies of individual cells occupied by the respective medium so that such a discreete system of cells can be manipulated more easily in a computer. The liquid wicking Simulations are then set up from the initial configuration of the liquid.

Layer into which the Fiber Mass with a predefined Fiber orientation is in part vertically dipped, absorbing the liquid.

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Statistical physics in general deals with systems with Many Degrees of Freedom. These Degrees of Freedom, in our case, are represented by the so called ising variables. We assume that we know the hamiltonian (the total internal energy) of the system, the problem is computing the average or equilibrium macroscopic parameters observable (energy and liquid Mass uptake) for a given initial system configuration. Moreover, we Will monitor the kinetics or Even dynamics of the system so as todo simulate the wicking behavior with time, for more detail se [3],[4].

Model.

The auto model (particularly so called ising model) and Montecarlo method were used especially for simula-tion of a liquid droplet in contact with fibrous material. The mecha-nism of this kind of simulation is fully described in [2].

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Methodology.
With the use of an optimized algorithm, the 3-d ising model improves accuracy and eficiency in simulation, this approach is capable of realistically simulating the complicated mechanisms involved in the filtration and separation procesos. The fibrous material is repre-sented by non-woven textile material.

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Interface.

Occupied elements of 3d model are imported in layers as vértices After which a volume effect is applied on them (various color depending on fibre or liquid fluid element). It is further posible todo render or animate structures made by fibres with liquid interaction or Even Cut samples in desired positions with orthographic Camera point of view. This method works properly Even for very large data sets.

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Eventually it Will be posible todo use textured slices in a voxel visualization manner, like a 3d virtual reconstruction of human body from cuts obtained by medicine computer tomography devices.

Visualization.

Furthermore it is a los posible todo present linear or Even real time content in Low cost anaglyph stereoscopy or active virtual reality projection due todo much better immersion, due todo GLSL ofering support for real time Shaders, it is posible todo experiment with scientific computing and visualization using the real time interactive Game Engine.

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Parallel computing architecture is a programming ap-proach for the performing of scientific calculations on the GPU as a data parallel computing device. The pro-gramming interfaz allows us todo implement algorithms using extensiones todo the standard Python lenguaje used inside Blender.

Import Gamelogic.

Cont = Gamelogic, getcurrentcontroller()
Obj = cont, getowner()
Fragmentshader =
Uniform sampler2d color;
Varying vec3 light_vec;
Varying vec3 normal_vec;
Void main() {
Vec3 l = normalize (light_vec),
Vec3 n = normalize (normal_vec),
Float ndotl = Dot(n, l),
Gl_fragcolor = texture2d(color, gl_texcord[0].st)*ndotl;
}
Mesh_index = 0
Mesh = (*.obj), getmesh(mesh_index)
Shader = mat, getshader()
Shader, setsource (fragmentshader,1)
Shader, setsampler(Colormap,0).

Authors a los thank the companies elmarco and cum-mins filtration for their support and interest in this work.