Browsing by Author "Noguera, DR"
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- ItemBitwise implementation of a two-dimensional cellular automata biofilm model(ASCE-AMER SOC CIVIL ENGINEERS, 2005) Pizarro, GE; Teixeira, J; Sepulveda, M; Noguera, DRMathematical modeling using the cellular automata (CA) approach is an attractive alternative to models based on partial differential equations when the domains to be simulated have complex boundary conditions. The computational efficiency of CA models is readily observed when using parallel processors but implementations in personal computers are, although feasible, not quite efficient. In an effort to improve the computational efficiency of CA implementations in personal computers, we introduce in this paper a bitwise implementation, based on the use,of each bit as a: different CA cell. Thus, in a 32-bit processor, each computer word stores information about 32 different CA cells. We illustrate the bitwise implementation with a biofilm model that simulates substrate diffusion and microbial growth of a single-species, single-substrate, structurally heterogeneous biofilm. The efficiency of the bitwise implementation was evaluated by comparing the computational time-of equivalent CA biofilm models that used more common low-level implementations, namely, if-then operators and look-up-tables. The processing speed of the bitwise implementation was over an order of magnitude higher than the processing speed of the other two implementations. Regarding the biofilm simulations, the. CA model exhibited self-organization of the biofilm morphology as a function of kinetic and physical parameters.
- ItemComparing biofilm models for a single species biofilm system(2004) Morgenroth, E; Eberl, HJ; van Loosdrecht, MCM; Noguera, DR; Pizarro Puccio, Gonzalo Ernesto; Picioreanu, C; Rittmann, BE; Schwarz, AO; Wanner, O
- ItemQuantitative cellular automaton model for biofilms(ASCE-AMER SOC CIVIL ENGINEERS, 2001) Pizarro, G; Griffeath, D; Noguera, DRA fully quantitative cellular automaton (CA) biofilm model was developed. The model describes substrate and biomass as discrete particles existing and interacting in a specified physical domain. Substrate particles move by random walks, simulating molecular diffusion. Microbial particles grow attached to a surface or to other microbial particles, consume substrate particles, and duplicate if a sufficient amount of substrate is consumed. The dynamics of the system are simulated using stochastic processes that represent the occurrence of specific events, such as substrate diffusion, substrate utilization, biofilm growth, and biofilm decay and detachment. The ability of the CA model to predict substrate gradients and fluxes was evaluated by comparing model simulations to predictions from a traditional differential equations model. One and 2D CA models were evaluated. In general, CA model predictions of steady-state flux, biofilm thickness, and substrate gradients inside the biofilm fitted well the differential equations model results; the 2D model had a better agreement at high substrate concentrations. Fully quantitative CA biofilm models offer an alternative approach to simulate biofilm activity and development. Specific advantages of CA modeling include the ability to simulate growth of heterogeneous biofilms with irregular boundary conditions, and the possibility of developing computationally efficient parallel processing algorithms for the quantitative simulation of biofilms in two and three dimensions.