Cellular automata technique

Now cellular automata technique is widely used in various applications. Rather complete reviews including common non-equilibrium properties of the dynamic processes are given in Refs. [233-235],... 

This mechanism was modeled with two different techniques, first by Im-bihl et al. (52) with a set of coupled differential equations, and later by Moller et al. (46,48) using a cellular automata technique. Experimental data could be fit relatively well (Fig. 13) with the obvious exception of the discrepancy in the time scales in the differential equation model. The model equations, however, are quite complex because the authors tried to model the nucleation mechanism for the phase transition. Recently the model of... 

In addition to the direct solution of PDEs corresponding to reaction-diffusion equations, in recent years attention has begun to be focused on the use of coupled lattice methods. In this approach, diffusion is not treated explicitly, but, rather, a lattice of elements in which the kinetic processes occur are coupled together in a variety of ways. The simulation of excitable media by cellular automata techniques has grown in popularity because they offer much greater computational efficiency for the two- and three-dimensional configurations required to study complex wave activity such as spirals and scroll waves. 

Fig. 30. Computer simulation by the cellular automaton technique of spatial pattern evolution during CO oxidation on Pt(100). (From Ref. 32.)...

Two other approaches treat a spatially distributed system as consisting of a grid or lattice. The cellular automaton technique looks at the numbers of particles, or values of some other variables, in small regions of space that interact by set rules that specify the chemistry. It is a deterministic and essentially macroscopic approach that is especially useful for studying excitable media. Lattice gas automata are mesoscopic (between microscopic and macroscopic). Like their cousins, the cellular automata, they use a fixed grid, but differ in that individual particles can move and react through probabilistic rules, making it possible to study fluctuations. 

The Lattice Boltzmann Method (LBM), including the method Cellular Automaton (AC), present a powerful alternative to standard apvproaches known like "of up toward down" and "of down toward up". The first approximation study a continuous description of macroscopic phenomenon given for a partial differential equation (an example of this, is the Navier-Stokes equation used for flow of incompressible fluids) some numerical techniques like finite difference and the finite element, they are used for the transformation of continuous description to discreet it permits solve numerically equations in the compniter. 

Partial differential equations represent one approach (a computationally intensive one) to simulating reaction-diffusion phenomena. An alternative, more approximate, but often less expensive and more intuitive technique employs cellular automata. A cellular automaton consists of an array of cells and a set of rules by which the state of a cell changes at each discrete time step. The state of the cell can, for example, represent the numbers of particles or concentrations of species in that cell, and the rules, which depend on the current state of the cell and its neighbors, can be chosen to mimic diffusion and chemical reaction. 

Original developments in this area stem from the work of Frisch et al. (1986) who employed the technique of lattice gas hydrodynamics in which the fluid is modelled as a cellular automaton and the flow represented by the motion of particles on a lattice. More numerically efficient variants of this method, such as the lattice Boltzmann approach (McNamara and Zanetti, 1988), were subsequently developed. 

The cellular automata lookup table synthesis technique is discussed in Chapter 4. LASy basically works by applpng a cellular automaton to a lookup table containing an initial waveform. At each playback cycle of the lookup table, the cellular automaton algorithm processes the waveform. The intention is to let the samples of the lookup table be in perpetual mutation, but according to a sort of genetic code. ... 

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