Coupled Cellular Automata Research Articles

Coupling cellular automata and What If? models for residential expansion simulation: A case study of Southwest Sydney, Australia

AbstractThe impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally.

CA single track simulation of laser conduction welding with stainless steel 316L (1.4404)

The study presents a coupled cellular automata (CA) approach for a single track microstructure simulation for laser conduction welding. A high-power CO2 laser beam (1000 W) traverses the substrate, with the beam shaped by conventional optics, which produces a Gaussian profile. The process parameters produce a shallow melt phase maintaining a conduction limited weld seam. The stainless steel material 1.4404 was used as substrate material with a grain size of approximately 20 µm without any filler material. The cooling rates and initial grain size influence the grain morphology and were determined. The size of a single crystal was simulated for different cooling rates. The simulation outputs of the melt pool boundary are qualitatively and quantitatively compared to experimental results. The micrograph results have shown that fine sub cells are developed. This fine cellular microstructure was only satisfactorily resolved experimentally along the melt pool boundary. The simulation results are in acceptable agreement with the experiments at the melt pool boundary. They match in terms of cooling rate, grain angle, and primary dendrite spacing. However, the simulated grains are longer which indicates the necessity of homogenous nucleation in the liquid melt pool.

Prediction of softening kinetics and recrystallization texture in non-isothermally annealed bulged tubes using CPFEM and CA models

A hierarchically coupled cellular automata (CA) model, crystal plasticity finite element method (CPFEM), and thermal finite element (FE) model is developed to predict the softening kinetics of the bulged steel tube during non-isothermal annealing. Through the developed model, the kinetics of softening mechanisms including static recovery (SRV) and static recrystallization (SRX), as well as the recrystallization texture are predicted. Later, the Johnson-Mehl-Avrami-Kohnogorov (JMAK) model based on the predicted SRX data is developed to interpret the recrystallization behavior of the material. To perform this study, diverse experimental tests including tube hydroforming (THF), annealing, uniaxial tensile test, hardness test, as well as microstructure observations through optical microscopy and Electron Backscatter Diffraction (EBSD) tests on steel tube are performed. The obtained experimental data are utilized to calibrate and verify the implemented CPFEM model for simulation of THF process, thermal FE model for prediction of the local temperature over annealing time, and CA algorithm for modeling of the softening kinetics and texture evolution throughout the annealing process. The study shows that the predicted deformation characteristics, softening kinetics, recrystallization texture and temperature profile during non-isothermal annealing are in good agreement with experimental data. During the annealing process, a total of four stages for the kinetics of softening mechanisms is observed: No softening; SRV only; SRV dominant; and SRX dominant. During the progress of SRX, the behavior of recrystallization is abruptly changed, confirming that two different mechanisms are controlling the kinetics of transformation.

Numerical prediction of grain structure formation during laser powder bed fusion of 316 L stainless steel

Additive Manufacturing (AM) processes enable the reduction of manufacturing time, material waste, and allows for the creation of complex structures. However, anisotropic mechanical behaviour is frequently observed in additively manufactured parts, and it is directly linked to the component's grain structure characteristics, which itself is dependent on the process parameters. The formation of grain structure in 316 L stainless steel fusion lines is investigated in this paper, combining experimental results and numerical simulations. Experimentally, fusion lines are built on a 316 L substrate, using an instrumented LPBF process. The high-speed camera recordings combined with the characterization of the samples enables capturing of melt-pool sizes and grain characteristics. The numerical modelling is based on a three-dimensional “CAFE” model, coupling Cellular Automata and Finite Element models to predict grain formation. The thermal model is defined and calibrated using the experiments. The experimental and numerical grain characteristics are compared. Numerical results are discussed with regards to the growth models and the process parameters. The growth model defined here is compared to existing models and is well fitted to capture grain formation in single-track configurations. Finally, the average grain size and aspect ratio of the grains increase with an increase of the process' linear energy.

CA single track microstructure simulation of nickel base alloy CM247LC and stainless steel S316L, including experimental validation of S316L

Based on powder-bed fusion, a coupled cellular automata (CA) approach to simulate the microstructure and concentration distribution of two different materials, stainless steel S316 L and nickel base alloy CM247LC, in the middle and high scan speed range is presented. Local non-equilibrium models for rapid solidification are considered in this study and described. The simulation outputs from S316L and CM247LC are qualitatively and quantitatively compared with each other and validated with experiments in case of S316L. The melt pool geometry defines the grain morphology. Thin columnar grains and therefore a larger number can be found in the case of CM247LC compared to S316L. The temperature history, cooling rates and diffusivities have a tremendous impact on the grain morphology, based on SLM microstructure cross-section and single crystal simulations. Furthermore, concentration maps are analysed for both materials. In case of CM247LC, concentration maps are suggested as a possibility to predict hot cracks. Temperature dependent diffusivity coefficients and atomic spacing parameters are suggested. Simulations and experimental results are in good agreement.

Modeling urban sprinkling with cellular automata

This paper presents a spatiotemporal analysis to simulate and project urban sprinkling with coupled cellular automata (CA) and multinomial logistic regression (MLR) model. Our case study, the Basilicata region, south of Italy, is characterized by urban sprinkling - literally "a small amount of urban territory distributed in scattered particles". The region is witnessing a decoupled growth between demographic trend and urban expansion. We applied a coupled approach based on CA and MLR for urban sprinkling modeling and simulation. From three regional building datasets (1989, 1998 and 2013) building density maps were created and used to calibrate and validate the model and to project future urban expansion. Built-up causative factors were identified through an analysis of 19 articles that were compared and discussed according to their main features (methods, case studies, drivers, urbanization dynamics and demographic growth). The transition probability for the first period (1989–1998) was calibrated with MLR for built-up causative factors and with the multi-objective genetic algorithm (MOGA) for CA neighborhood effects. The calibrated model was used to simulate the 2013 urban pattern which was compared with the actual map of 2013 (validation). We then used our calibrated model to simulate urban expansion in 2030. The results of the 2030 forecast show the largest variations in class 1 (low density built-up patches) that correspond to urban sprinkling.

Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.

Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin

In any watershed, many factors influence land use/cover change (LUCC). The nonlinear relationships between these factors and LUCC are very complicated and make it difficult to build a model that is capable of accurately simulating the range of physical processes. The aim of this study was: (1) to simplify the structure of a simulation model and improve its simulation speed, and (2) evaluate the impact of land use/cover change on surface runoff and evapotranspiration. Firstly, a coupled cellular automata (CA) and artificial neural network (ANN) watershed land use/cover change simulation model, calibrated and validated using 1980 and 2000 land use data, respectively, was developed. It was used to simulate land use/cover type in the upper reaches of the Hanjiang Basin for 2020. Results indicate that the area of paddy field, dry land, shrubbery and construction land by 2020 will have increased; however, woodland, grassland and water areas will have decreased. Secondly, hydrological processes in the upper reaches of the Hanjiang Basin were simulated using the SWAT model. Finally, variations in watershed surface runoff and evapotranspiration for the LUCC 1980 and 2000 scenarios and the simulated 2020 scenario were analyzed. Results show that there is an increasing trend in the annual average runoff flowing into the Danjiangkou Reservoir, and that land use change has more influence on runoff throughout the year than during the flood season. The annual average evapotranspiration, annual runoff variation coefficient and annual runoff distribution coefficient were predicted to increase. Results confirm that (1) the ANN–CA model is capable of simulating land use/cover change for multiple classes, (2) the SWAT model facilitates sustainable land management planning, and (3) coupling the two models provides a new method for assessing the potential redistribution of land use types in the future.

Simulating multiple class urban land-use/cover changes by RBFN-based CA model

Land use systems are complex adaptive systems, and they are characterized by emergence, nonlinearity, feedbacks, self organization, path dependence, adaptation, and multiple-scale characteristics. Land use/cover change has been recognized as one of the major drivers of global environmental change. This paper presents a coupled Cellular Automata (CA) and Radial Basis Function Neural (RBFN) Network model, which combines Geographic Information Systems (GIS) to contribute to the understanding of the complex land use/cover change process. In this model, GIS analysis is used to generate spatial drivers of land use/cover changes, and RBFN is trained to extract model parameters. Through the RBFN-CA model, the conversion probabilities of each cell from its initial land use state to the target type can be generated automatically. Future land use/cover scenarios are projected by using generated parameters in the model training process. This RBFN-CA model is tested based on the comparison of model output and the real data. A BPN-CA model is also built and compared with the RBFN-CA model by using a variety of calibration metrics, including confusion matrix, figure of merit, and landscape metrics. Both the location and landscape metrics based assessment for model simulation indicate that the RBFN-CA model performs better than the BPN-CA model for simulating land use changes in the study area. Therefore the RBFN-CA model is capable of simulating multiple classes of land use/cover changes and can be used as a useful communication environment for stakeholders involved in land use decision-making.

Traveling excitable waves successively generated in a nonlinear proliferation system

We study the dynamics of spatiotemporal pattern formation in a nonlinear proliferation system (e.g., cell division supported on a field of nutrition), in which the mechanism of activation and its self-suppression is simultaneously implemented. This dynamical model has been numerically realized with coupled cellular automata (CA), and various long-standing spatiotemporal patterns have been observed. Among others, a successive generation of traveling waves by implanting a spot of cells onto the field consisting of nutrition and activator is particularly interesting. This takes place despite the fact that the present reaction network has a stable fixed point and therefore autonomous temporal oscillatory does not exist in the mean field. Indeed, the reaction-diffusion equation method (RD) applied to this network reproduces only a single excitable wave and soon falls into a steady state (a fixed point) without the following propagating waves. This system, having a stable fixed point, is an excitable system of different kind from the FitzHugh-Nagumo model in that it can generate a pulse propagating outwards by adding only a single cell onto it from outside the system. The present excitation upon dropping a cell is amplified to macroscopic level by a hidden dynamics of oscillation between the activation and its self-suppression. A pulse thus generated is propagated in space time with the help of diffusion. Through a precise comparison between CA and RD, it is found that a very small amount of residue of the cells and activators, which are left unburned in the stochastic treatment of reactions by the CA, becomes a seed to excite the system and generate the next pulse wave. This newly born wave can leave another seed of reaction in the field after its propagation. Based on this analysis, we account for the appearance of other patterns observed. A possible control of these patterns by varying the spatial distribution of initial concentration of the relevant agents such as the activator is also discussed.

The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery

The inherent complexity of biomedical systems is well recognized; they are multiscale, multiscience systems, bridging a wide range of temporal and spatial scales. While the importance of multiscale modelling in this context is increasingly recognized, there is little underpinning literature on the methodology and generic description of the process. The COAST (complex autonoma simulation technique) project aims to address this by developing a multiscale, multiscience framework, coined complex autonoma (CxA), based on a hierarchical aggregation of coupled cellular automata (CA) and agent-based models (ABMs). The key tenet of COAST is that a multiscale system can be decomposed into N single-scale CA or ABMs that mutually interact across the scales. Decomposition is facilitated by building a scale separation map on which each single-scale system is represented according to its spatial and temporal characteristics. Processes having well-separated scales are thus easily identified as the components of the multiscale model. This paper focuses on methodology, introduces the concept of the CxA and demonstrates its use in the generation of a multiscale model of the physical and biological processes implicated in a challenging and clinically relevant problem, namely coronary artery in-stent restenosis.

Simulation of Traffic Regulation and Cognitive Developmental Processes: Coupling Cellular Automata with Artificial Neural Nets

This paper deals with the advantages of so-called hybrid systems, that is, systems consisting of two or more different models. We present here two cellular automata coupled with artificial neural networks (neural CA). The first example is the model of a traffic regulating system. The cells of the CA represent different cars, and a certain neural net regulates the traffic. The second example shows a model of cognitive learning in dependency of social contexts. The cells of a CA consist of two types of neural networks. First results demonstrate the fruitfulness of this approach.

Coupled-cellular-automata study on stochastic and pattern-formation dynamics under spatiotemporal fluctuation of temperature

Overdamped stochastic process under spatio-temporal fluctuation of temperature is investigated in terms of coupled cellular automata (CA). With this scheme, we study clustering of particles and its pattern formation on a flat substrate. To materialize such a CA, the heat conduction on the surface is represented in terms of hypothetical heat corpuscles (Bosons) that undergo diffusion. Chemical bonding emitts heat corpuscles to the substrate and dissociation absorbs, and thereby they interact with each other in a nonlinear manner. This nonlinear dynamics is treated by means of an interactive set of different CPU’s. The timing of message-exchange between the CPU’s practically determine a diffusion constant of the particles relative to that of the heat corpuscles. The first half of this paper is devoted to establishing the present coupled CA by examining the temperature dependence of the resultant diffusion constant and other quantities. Then, by comparing clustering dynamics on the substrates of fast and slow heat conductivity, we clarify and quantify the effects of spatiotemporal fluctuation of temperature on stochastic dynamics. Besides the spontaneous fluctuation of temperature, we explore the effects of rather artificial heat sources and sinks, the configurations of which can be varied in space-time to control the pattern formation.

A Coupled Cellular Automata Representation of Nanoscale Transport Across Semiconductor Interfaces

ABSTRACTThin-film devices pose unique challenges to device simulators. Relaxation approximations to the Boltzmann Transport Equation may be vitiated as the width and length of the thin-film components approach the mean free path lengths of electrons and phonons. Concomitant with these reduced lengths, surface and other interface scattering mechanisms exert greater influences on the transport processes. Consequently, it may not be effective to use transport models that depend upon analytic or other pre-ordained representations of electron, phonon, and photon distribution functions. Finally, thin-film simulations must account for tunneling processes, local defects, nanoscale dopant gradients, nanoscale roughness, and nanoscale variations in local geometries. Consequently, it is desirable to develop a nanoscale simulation technique that (1) can arbitrarily vary material properties in real space while (2) tracking reciprocal space scattering for arbitrary and volatile distributions of electrons, phonons, and photons. The Discrete State Simulation (DSS) forms the backbone of such a simulation. Written with the run-time flexibility of object-oriented code, the Discrete State Simulation (DSS) is a coupled cellular automata (CA) simulator that builds upon the objects and rules of quantum mechanics. The DSS represents global non-equilibrium processes as patterns that emerge through an ensemble of scattering events that are localized at vibronic nodes. The run-time flexibility of the DSS enables dynamic rule construction - computational algorithms that evolve in response to the conditions that are being simulated. The reported DSS effort simulated nanoscale transport processes at dopant-defined junctions by coupling electron-phonon and electron-photon scattering mechanisms. Using electronic band structures, phonon band structures, and deformation potentials analogous to silicon <100> material properties, the DSS generated data depicted the dynamic band bending induced by femtosecond variations in applied electric fields.