Cellular Automata Simulation Research Articles

Cooperative U-Turn Merging Behaviors and Their Impacts on Road Traffic in CVIS Environment

U-turn behavior of vehicle is one of the main causes of urban traffic congestion and accidents. A collaborative U-turn merging control algorithm is studied with collision avoidance and delay minimization for vehicles under Cooperative Vehicle Infrastructure System (CVIS) environment. Two control strategies, zip merging and platoon merging control, are proposed. The applicability of these two strategies is compared from the perspective of efficiency and driving comfort. The cellular automaton simulation system composed of a two-way four-lane traffic flow with a U-turn facility in middle of road is established with cooperative control algorithm imbedded. The influence of cooperative U-turn merging behaviors on traffic performance is evaluated by analyzing the arrival rates of main lane and U-turn vehicles and their relationship between one another. The simulation results show that the arrival rate of vehicles on target lane has a great impact on traffic delay. The cooperative control can improve the traffic flow only in the condition that the arrival rate of vehicles on target lane is less than 0.7. It provides some practical references for transportation agencies to meet efficiency requirements of the U-turn section when they apply cooperative control strategy.

The delineation of urban growth boundaries in complex ecological environment areas by using cellular automata and a dual-environmental evaluation

Urban growth boundaries are an efficient tool for protecting non-urban land uses from being dominated by urban sprawl and can promote sustainable urban development. Previous studies have explored the use of a cellular automata simulation and its visualization products in delimiting future UGBs. However, existing CA-UGB delineation models lack a transcendental component for ecological evaluation and are difficult to apply to areas with complex ecological environments or heterogeneous resource endowments. Therefore, this paper introduces an innovative model by coupling the dual-environmental evaluation and the Future Land-use Simulation CA model for delimiting future UGBs. To prioritize ecological protection, some intermediate products of the DEE are processed as pivotal components in the CA simulations. Strict urban simulation constraints are also applied in the FLUS model to meet the DEE regulations and prioritize ecological protection. This model was implemented to plan future UGBs in Chungking, China. After quantifying and comparing the proposed UGB scheme with other UGB delineations of Chungking by using various landscape metrics, the DEE-UGBs show a stronger ability to manage urban patches via aggregation with the simultaneous reconciliation of contradictions among land uses.

Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India

Geographical Information System (GIS) and remote sensing have become necessary tools in finding out land use land cover (LULC) change integrated with their associated driving factors. The utilization of satellite imagery made it easy to interpret the highly urbanized Warangal City that has experienced a lot of change in LULC during the last few decades. This paper discusses the ability of the integration of cellular automata (CA) and Markov chain–based 2D land use simulation module conjunction with GIS techniques. Markov chain algorithm is used for calibration and optimization by considering LULC of appropriate set of images for the years 2004, 2006, and 2018. Transitional change in LULC from one class to another is simulated using an artificial neural network (ANN) while cellular automata simulation is carried out to predict the plausible future LULC for the year 2052 after validating the model using the LULC of the year 2018. Analysis of the multi-temporal LULC maps indicated that the biophysical and socio-economic factors have greatly influenced the rise in built up while a decrease in agriculture in the year 2052. In conclusion, this technique is a powerful tool for monitoring and modeling change in land cover. Further suggestions for government officials are provided for an effective policymaking and to protect the land resource.

Recrystallization at Crack Surfaces as a Specific Fracture Mechanism at Elevated Temperatures—Cellular Automata Simulation

Recrystallization at Crack Surfaces as a Specific Fracture Mechanism at Elevated Temperatures—Cellular Automata Simulation

Continuous cellular automaton simulation of focused ion beam-induced deposition for nano structures

With the assistance of a gas injection system, focused ion beam-induced deposition (FIBID) can be realized to fabricate complicated three-dimensional structures in nanoscale. The growth rate of the deposited structure depends on the flux of precursor gas and incident ions. In this study, a Continuous Cellular Automaton (CCA) model is put forward to simulate the FIBID process, with four rules established to initiate and dominate the interface evolution, which can calculate the deposited structure profile under different fabrication conditions. In this model, the sputtering and diffusion effects are taken into account to establish a more accurate relationship between the deposition rate and precursor gas flux based on the continuous model, and the influences imposed by precursor gas and incident ions are clarified. In order to verify the precursor gas distribution and the growth mechanism of the FIBID, experiments are carried out with different local precursor gas flux on the substrate surface with a 7 pA and 30 keV Ga ions, using C9H16Pt as the precursor gas. The experimental and simulation results indicate that the CCA model features a high accuracy in predicting the deposited structure profile, and prove that the proposed model can be utilized in fabrication parameters optimization.

Experimental study and cellular automaton simulation on solidification microstructure of Mg–Gd–Y–Zr alloy

The solidification microstructure of Mg–Gd–Y–Zr alloy was investigated via an experimental study and cellular automaton (CA) simulation. In this study, step-shaped castings were produced, and the temperature variation inside the casting was recorded using thermocouples during the solidification process. The effects of the cooling rate and Zr content on the grain size of the Mg–Gd–Y–Zr alloy were studied. The results showed that the grain size decreased with an increase in the cooling rate and Zr content. Based on the experimental data, a quantitative model for calculating the heterogeneous nucleation rate was developed, and the model parameters were determined. The evolution of the solidification microstructure was simulated using the CA method, where the quantitative nucleation model was used and a solute partition coefficient was introduced to deal with the solute trapping in front of the solid–liquid (S/L) interface. The simulation results of the grain size were in good agreement with the experimental data. The simulation also showed that the fraction of the eutectics decreased with an increasing cooling rate in the range of 2.6–11.0 °C·s−1, which was verified indirectly by the experimental data.

Three-dimensional cellular automaton simulation of austenite grain growth of Fe-1C-1.5Cr alloy steel

A three-dimensional (3D) cellular automaton model describing the austenite grain growth process of Fe-1C-1.5Cr alloy steel is established. This model proposes a discrete representation of the three-dimensional grain boundary curvature, and calculates the 26 neighbor lattice points to the center lattice point in three-dimensional space. The strength of neighbor lattice point’s influence is determined by the distance from the center lattice point. The paper presents the calculation steps of the three-dimensional cellular automaton model and the calculation methods of key parameters such as grain boundary curvature and grain growth speed, and validates the model calculation results. The results show that the model established in this paper can well describe the austenite grain growth process of Fe-1C-1.5Cr alloy steel at different temperatures. The model can be used to study the grain growth process of Fe-1C-1.5Cr alloy steel under three-dimensional conditions.

Integrating pattern-based modelling and political ecology in land-use change research: the case of Mexican dry tropics

ABSTRACT Agricultural and extractive frontiers experiment rapid landscape transformation. Land-Use Sciences and Political Ecology are complementary approaches for analysing how landscape transformations are related to biophysical conditions, and socioeconomic, cultural and political processes developed at global, national and local scales. This study examines such relationships in a long-standing agrarian and resource frontier in southern Mexico for the 1986–2015 period. We combine insights from: (1) a quantitative land use/land cover change pattern-based model, involving a weights of evidence and cellular automata simulations, and (2) a qualitative content analysis of literature and of local actors’ perspectives. Two grand frontier processes have developed in this region: NAFTA-related agrarian transition toward intensification and the establishment of wind farms. Both were triggered by global forces and new forms of land and resources use, but mediated by national-to-local ecological, socioeconomic and political processes, producing particular landscape transformations.

Simulacija dinamične rekristalizacije zlitine TC4 med vročim stiskanjem na osnovi celičnih avtomatov

Simulacija dinamične rekristalizacije zlitine TC4 med vročim stiskanjem na osnovi celičnih avtomatov

Cellular automata simulation on the corrosion behavior of Ni-base alloy in chloride molten salt

Chloride molten salt is required to study its corrosion behavior since it is recommended to be used as heat transfer and heat storage material in high-temperature thermal power generation system. In this paper, a simplify model was established by the reaction of diffusive corrosive gas and metal substrate in a chloride molten salt. A cellular automaton model based on the stochastic approach is adopted to simulate the uniform corrosion and the migration of metallic element. The corrosion behavior is also simulated considering the practical working condition, such as oxygen, moisture and so on. By comparing the simulation results with experimental data, it proves that the cellular automata simulation is an effective alternative method to describe the high-temperature corrosion process of molten salts. Simulation results of 25,000 time steps is taken as a comparison of the 21-day corrosion which reflects the Cr distribution, and the growth of corrosion layer with the migration of Cr element was reproduced. The change of corrosion layer thickness is similar to the mass loss. The simulation method provides insight on the distribution of corrosion products, corrosion kinetics and morphologies for the long-term application.

Three-Dimensional Cellular Automata Simulation of the Austenitizing Process in GCr15 Bearing Steel.

On the basis of the two-dimensional cellular automaton model, a three-dimensional cellular automaton model of austenitizing process was established. By considering the orientation of pearlite layer and the direction of austenite grain growth, the velocity of the interface was calculated during the austenitizing process. The austenitizing process of GCr15 steel was simulated, and the anisotropy of grain growth rate during austenitization was demonstrated by simulation results. By comparing the simulation results with the experimental data, it was found that the calculated results of the three-dimensional cellular automaton model established in this paper were in good agreement with the experimental results. By using this model, the three-dimensional austenitizing process of GCr15 steel at different temperatures and under different processing times can be analyzed, and the degree of austenitization can be predicted.

Three-dimensional cellular automaton simulation of coupled hydrogen porosity and microstructure during solidification of ternary aluminum alloys

Hydrogen-induced porosity formed during solidification of aluminum-based alloys has been a major issue adversely affecting the performance of solidification products such as castings, welds or additively manufactured components. A three-dimensional cellular automaton model was developed, for the first time, to predict the formation and evolution of hydrogen porosity coupled with grain growth during solidification of a ternary Al-7wt.%Si-0.3wt.%Mg alloy. The simulation results fully describe the concurrent nucleation and evolution of both alloy grains and hydrogen porosity, yielding the morphology of multiple grains as well as the porosity size and distribution. This model, successfully validated by X-ray micro-tomographic measurements and optical microscopy of a wedge die casting, provides a critical tool for minimizing/controlling porosity formation in solidification products.

Identifying the pattern of breakdown in a laminar-turbulent transition via binary sequence statistics and cellular-automaton simulations.

The laminar-turbulent transition induced by two-dimensional steplike roughness is investigated focusing on the pattern of breakdown. The statistics of the turbulent burst rate is found to be significantly different from the prediction of the classical theory. A systematic investigation of the pattern of breakdown is motivated by this phenomenon. It is identified with heuristic analysis that a pattern of distributed breakdown is responsible for the deviation, in contrast to the concentrated breakdown hypothesis in the classical theory. The pattern indicates that the steps probably induced a bypass transition in present experimental setup, which is different from the current understanding about the step-induced transition. Cellular-automaton simulations are carried out to validate the heuristic analysis. The influences of quasiconcentration and non-Poisson process in spot generation on the breakdown statistics are also discussed based on the simulation results.

Evaluation of the kinetics of recrystallization based on scaling 1D cellular automaton by DSC measurements

There are several methods to study the kinetics of the recrystallization process. DSC measurement is one of the most effective measurement methods. Cellular automata simulations are frequently used in computational studies. Both methods give and use kinetic constants. The aim of this study to find the relation between the kinetic constants. Additionally an evaluation method is introduced based on the DSC evaluation and cellular automaton which can compute a more detailed kinetic description of recrystallization.

Comparison between experimental data and a cellular automata simulation of martensite formation during cooling

Computer simulations of steel microstructural development provide a powerful tool, which can form the basis of mechanical property predictions. However, in order to create detailed understanding of the factors affecting the properties, the model should predict microstructural evolution during cooling. The present study compares the results of cellular automata simulations with experimental data for two distinct austenite conditions, recrystallized and deformed. Detailed microstructural features were studied using a laser scanning confocal microscope, FESEM and FESEM-EBSD. The two-dimensional cellular automata (CA) model for simulating the formation of lath martensite was parameterized using fitted Johnson-Mehl-Avrami-Kolmogorov and Koistinen-Marburger equations. The parent austenite microstructure for the CA model was determined from the final martensitic microstructure using austenite grain reconstructions based on the use of MATLAB software and the MTEX toolbox. The results of this cellular automata simulation can be used to estimate the shapes and sizes of martensite blocks, which offers new possibilities for the qualitative estimation of the mechanical properties of high-strength steels formed from recrystallized or deformed austenite.

A design framework for optimizing forming processing parameters based on matrix cellular automaton and neural network-based model predictive control methods

• A matrix CA method is developed to quickly describe microstructure variations with processing parameters. • Neural network-based model predictive control (NNMPC) method is used to online optimize processing parameters. • Based on matrix CA and NNMPC method methods, a material design framework is developed. • Ideal microstructures of a Ni-based superalloy can be obtained by hot deformation based on this design framework. The advanced modelling/simulating method and the effective optimization/controlling strategy relying on knowledge-based systems are highly demanded in industrial manufacturing of alloy components. In this work, based on the advantages of cellular automaton (CA) simulation and neural network-based model predictive control (NNMPC) methods, a material design framework is developed to optimize processing parameters for the designed target microstructures of alloys. In this framework, a matrix CA simulation method is developed to accurately and quickly describe the variations of microstructures with processing parameters. NNMPC, which is an effective control method for nonlinear and multi-objective system, is utilized to online optimize processing parameters according to the designed target microstructures. Based on the optimized processing parameters, the hot compressive deformation tests of a Ni-based superalloy are conducted to verify the effectiveness of the developed framework. The experimental results well agree with the simulated/designed ones, which implies that the developed material design framework can effectively optimize processing parameters for the designed target microstructures. Also, the developed material design framework is used to obtain the uniform and fine microstructures of a Ni-based superalloy.

Auto-generation of centerline graphs from geometrically complex roadmaps of real-world traffic systems using hierarchical quadtrees for cellular automata simulations

This paper proposes a method for auto-generating centerline graphs from geometrically complex roadmaps of real-world traffic systems for cellular automata simulations, using hierarchical quadtrees. Our method is summarized as follows: First, we store the binary values of the monochrome image of the target roadmap (where one and zero represent the road and other areas, respectively) in a two-dimensional square map. Second, we recursively divide the square map into sub-leaves using a quadtree, until the sum of the values of pixels included inside each leaf becomes less than or equal to one. Third, we successively remove the distal leaves for which adjacent leaves have shallower depths. Fourth, we trace the remaining distal leaves of the tree using Morton’s space-filling curve, while selecting the leaves among those selected previously that preserve a certain distance as the nodes of the graph. Finally, each selected node searches the neighboring nodes, and these are stored as the edges of the graph. We demonstrate our method by generating a centerline graph from a complex roadmap of a real-world airport, and by performing a typical network analysis using Dijkstra’s method.

Integrated Simulation Framework for Additively Manufactured Ti-6Al-4V: Melt Pool Dynamics, Microstructure, Solid-State Phase Transformation, and Microelastic Response

To accelerate the establishment of fundamental understanding of the additive manufacturing (AM) process and its influence on microstructural evolution and related properties, we develop a multiphysics and multiscale modeling framework that integrates: (1) a high-fidelity powder-scale three-dimensional simulation of transient heat transfer and melt flow dynamics, (2) cellular automaton simulation of solidification grain structure and texture, (3) phase-field modeling of precipitation and dissolution of second-phase precipitate during repeated thermal cycles, and (4) microstructure-based micro- and mesoscopic elastic response calculation. Using Ti-6Al-4V as a model system, we demonstrate the application of the integrated framework to simulate complex microstructure evolution during a single-track laser powder bed fusion process and the associated mechanical response. Our modeling framework successfully captures the solidification β grain structure as a function of laser power and scanning speed, α precipitation upon subsequent cooling with different rates, and elastic response of the resulting (α + β) two-phase microstructure. The key features of solidification and second-phase precipitate microstructures, and their dependence on processing parameters, agree well with existing experimental observations. The established modeling framework is generally applicable to other metallic materials fabricated by AM.

Unpredictability in pedestrian flow: The impact of stochasticity and anxiety in the event of an emergency

Random interactions among pedestrians, usually produced by psychological effects (e.g. anxiety), can produce collective nontrivial phenomena in their flow dynamics. Here, we improve our understanding on these phenomena by implementing cellular automaton (CA) simulations in a simplified one-dimensional corridor model. We consider different types of randomness that could be used to parametrize social, cultural or psychological behaviors; their incidence on the average evacuation time and standard deviation are studied. The randomness in the pedestrians’ motion allows separating between different phases of movement, which in the end, drives the dynamics. Our study allows to distinguish among emergent collective phenomena, unpredictability, and unexpected Brownian type behavior in certain regimes, that occur in one-dimensional hallways, such as exists in airplanes, cinemas, massive sports events, etc. These findings may be relevant to evacuation procedures in the event of an emergency, where anxiety may compromise people’s performance and the effectiveness of the evacuation plan. Therefore, it could be highly useful to introduce these aspects in the simulation of pedestrian dynamics.

Effect of Lattice Size on the Critical Temperature of Ising Nanofilms and Nanotubes on the Square Lattice: Cellular Automata Simulation

Lattice size dependency of critical temperature of Ising nanofilms and nanotubes on the square lattice have been investigated Using Cellular Automata simulation approach. The obtained values of critical temperature for single-layer nanofilm on the infinite size of lattice is equal to 2.36, the corresponding value for single-layer nanofilm on the honeycomb lattice was 1.45. The effects of length and diameter of nanotube on the critical temperature were also studied.