Percolation is the process that causes a a solvent (e.g. water) to pass through a permeable substance and to extract a soluble constituent. Cellular Automata provide a very powerful tool for the simulation and the analysis of percolation processes. In some cases, however, the most challenging problem is perhaps to reproduce correctly within the automaton the features of the percolation bed, that is, the porous medium the solvent flows through. In this paper we present a computational model for the controlled generation of two–dimensional percolation beds based on stochastic Cellular Automata, and we show how it has been applied to the generation of percolation beds suitable for the simulation of pesticide percolation in the soil. In particular, the approach we present permits to keep under control the shape and the size of the single components of the bed (e.g. grains), and their position. In order to reproduce percolation beds of feasible size, and to manage large automata, the model has been implemented on a cluster of workstations.
Bandini, S., Mauri, G., Pavesi, G. (2001). Parallel generation of percolation beds based on stochastic cellular automata. In Parallel Computing Technologies 6th International Conference, PaCT 2001, Novosibirsk, Russia, September 3-7, 2001 Proceedings (pp.391-400). Springer Verlag [10.1007/3-540-44743-1_40].
Parallel generation of percolation beds based on stochastic cellular automata
Bandini, S;Mauri, G;
2001
Abstract
Percolation is the process that causes a a solvent (e.g. water) to pass through a permeable substance and to extract a soluble constituent. Cellular Automata provide a very powerful tool for the simulation and the analysis of percolation processes. In some cases, however, the most challenging problem is perhaps to reproduce correctly within the automaton the features of the percolation bed, that is, the porous medium the solvent flows through. In this paper we present a computational model for the controlled generation of two–dimensional percolation beds based on stochastic Cellular Automata, and we show how it has been applied to the generation of percolation beds suitable for the simulation of pesticide percolation in the soil. In particular, the approach we present permits to keep under control the shape and the size of the single components of the bed (e.g. grains), and their position. In order to reproduce percolation beds of feasible size, and to manage large automata, the model has been implemented on a cluster of workstations.
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