Cellular Automaton Simulation Model Research Articles

Evaluation of Austenite-Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation.

Austenite-ferrite phase transformation is a crucial metallurgical tool to tailor the properties of steels required for particular applications. Extensive simulation and modeling studies have been conducted to evaluate the phase transformation behaviors; however, some fundamental physical parameters still need to be optimized for better understanding. In this study, the austenite-ferrite phase transformation was evaluated in carbon steels with three carbon concentrations during isothermal annealing at various temperatures using a developed cellular automaton simulation model combined with Bayesian optimization. The simulation results show that the incubation period for nucleation is an essential factor that needs to be considered during austenite-ferrite phase transformation simulation. The incubation period constant is mainly affected by carbon concentration and the optimized values have been obtained as 10-24, 10-19, and 10-21 corresponding to carbon concentrations of 0.2 wt%, 0.35 wt%, and 0.5 wt%, respectively. The average ferrite grain size after phase transformation completion could decrease with the decreasing initial austenite grain size. Some other parameters were also analyzed in detail. The developed cellular automaton simulation model combined with Bayesian optimization in this study could conduct an in-depth exploration of critical and optimal parameters and provide deeper insights into understanding the fundamental physical characteristics during austenite-ferrite phase transformation.

Inhibition of lithium dendrite growth with highly concentrated ions: cellular automaton simulation and surrogate model with ensemble neural networks

Our lattice simulation accounts for the significant inhibition of lithium-dendrite growth in ionic liquids. We also show that our ensemble neural networks can further capture this simulation, which significantly reduces statistical sampling in the lattice simulation.

Pedestrian flow in two dimensions: Optimal psychological stress leads to less evacuation time and decongestion.

Collective motion is an innate ability of all living systems, which depends on physiological and psychosocial factors in the case of humans. Such a collective organization is becoming of great interest in collective motion in human crowds. Using a cellular automaton (CA) simulation model, we demonstrate that emergency egress from a two-dimensional corridor with optimal stress leads to less evacuation time and efficient mass evacuations. We study how three types of stress (i.e., mild stress, optimal stress, and anxiety) described in the literature have a significant impact on the collective dynamics. We found that low-stress levels could decrease the evacuation time in an entire occupied room since agents choose alternative routes rather than the shortest path to the exit and display cooperative behavior. Therefore, the combination of mild and optimal stress can lead to efficient evacuations. Also CA simulations may be used to find safer and more efficient ways to conduct mass evacuation procedures.

Diffusion of Innovations in Middle Eastern versus Western Markets: A Mathematical Computation Cellular Automata Simulation Model

Simulation modelling has gained ground over the years since it can provide various scenarios applied to any scientific area. In this study, a stochastic cellular automata model is proposed, in which agents fall into three distinct categories (adopters, non-adopters and denials). Based on Hofstede’s cultural dimension individualism, we characterize three major international markets, as perfectly clustered (collective) to perfectly random (individualistic). We investigate innovation diffusion speed, in each network topology. At each time step, the decision of non-adopters to purchase innovative products, is affected by their immediate neighborhood (von Neumman). The speed of diffusion is evaluated using time at which sales reach 50% of market. Effects of simulation parameters on speed of diffusion, are assessed using a log-normal accelerated failure time model. Results demonstrate that diffusion of innovative products accelerates when innovators of a virtual economic system are placed according to a random network and when amount of innovators and imitators in the economic system increases. Slower innovative products’ diffusion process is a result of a large amount of denials and of how imitators are placed in the in the virtual economic system. Diffusion in small-world virtual economic systems lead to small time inflexion points very close to those of a random networked market.

Cellular automaton model for the simulation of laser cladding profile of metal alloys

Based on the mechanism of laser-powder interaction and droplet forming method, a cellular automaton simulation model of laser cladding layer morphology is introduced. In the model, the influences of thermophysical parameters including specific heat and thermal conductivity are taken into consideration. Meanwhile, the relationships involving laser power, scan speed and powder feed rate and the morphology parameters of the cladding layer are established. Subsequently, in order to verify the applicability and accuracy of the simulation of the cladding layer, single-track experiments of Stellite6, Ni60AA and SUS316, materials with different thermophysical properties, were carried out under different process parameters, respectively. Additionally, the stepwise method is applied to multitrack cladding experiments with different overlap to obtain morphological parameters of different tracks. The morphology of the cladding layer calculated by the model is in good agreement with the experimental results. Both of the relative errors of width and height are less than 5%, and the relative error of the cross-sectional area is less than 9%, which affirms the possibility of the model to predict the cladding morphology of different materials under different process parameters. This research can optimize process parameters and improve the forming quality of laser cladding.

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.

Cellular automaton modeling of pedestrian movement behavior on an escalator**Project supported by the National Natural Science Foundation of China (Grant Nos. 71473207 and 71871189), the Research Grant Council of the Hong Kong Administrative Region, China (Grant No. CityU118909), and the Open Fund of Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention (Grant No. RRC201701).

As a convenient passenger transit facility between floors with different heights, escalators have been extensively used in shopping malls, metro stations, airport terminals, etc. Compared with other vertical transit facilities including stairs and elevators, escalators usually have large transit capacity. It is expected to reduce pedestrian traveling time and thus improve the quality of pedestrian’s experiences especially in jamming conditions. However, it is noticed that pedestrians may present different movement patterns, e.g., queuing on each step of the escalator, walking on the left-side and meanwhile standing on the right-side of the escalator. These different patterns affect the actual escalator traffic volume and finally the passenger spatiotemporal distribution in different built environments. Thus, in the present study, a microscopic cellular automaton (CA) simulation model considering pedestrian movement behavior on escalators is built. Simulations are performed considering different pedestrian movement speeds, queuing modes, and segregation on escalators with different escalator speeds. The actual escalator capacities under different pedestrian movement patterns are investigated. It is found that walking on escalators will not always benefit escalator transit volume improvement, especially in jamming conditions.

Size and Morphology of Gas Porosity in Castings—Insights into Computational Experiments Using a Cellular Automaton Model

Aluminium castings are known to be prone to micro-porosity formation which appears as fine porosity in the inter-dendritic and inter-granular regions of castings. The size, distribution and morphology of such pores significantly affect mechanical and fatigue properties of castings. We use a cellular automaton simulation model as a virtual experimental set-up to study growth of gas bubbles in solidifying aluminium castings. The model assumes that gas porosity originates from pre-existing micro-bubbles that grow by diffusion of hydrogen from the solid–liquid interfaces into the bubbles. The major factors that limit the growth of the bubbles are the finite time available for the diffusion of hydrogen and the space constraint imposed by the growing solid. While the diffusion limitation to pore growth has been studied well, the effect of the space constraint has not received much attention. Our cellular automaton model with growth rules specially adapted for bubble growth tracks the solid–liquid and bubble–liquid interfaces explicitly on a fine grid. Numerical experiments are performed with a eutectic Al–Si alloy solidified with different grain sizes and solidification rates. The micro-structural environment in which a pre-existing bubble finds itself is seen to be the most critical factor that determines the final size and morphology of porosity.

Conservation planning under uncertainty in urban development and vegetation dynamics.

Systematic conservation planning is a framework for optimally locating and prioritizing areas for conservation. An often-noted shortcoming of most conservation planning studies is that they do not address future uncertainty. The selection of protected areas that are intended to ensure the long-term persistence of biodiversity is often based on a snapshot of the current situation, ignoring processes such as climate change. Scenarios, in the sense of being accounts of plausible futures, can be utilized to identify conservation area portfolios that are robust to future uncertainty. We compared three approaches for utilizing scenarios in conservation area selection: considering a full set of scenarios (all-scenarios portfolio), assuming the realization of specific scenarios, and a reference strategy based on the current situation (current distributions portfolio). Our objective was to compare the robustness of these approaches in terms of their relative performance across future scenarios. We focused on breeding bird species in Israel’s Mediterranean region. We simulated urban development and vegetation dynamics scenarios 60 years into the future using DINAMICA-EGO, a cellular-automata simulation model. For each scenario, we mapped the target species’ available habitat distribution, identified conservation priority areas using the site-selection software MARXAN, and constructed conservation area portfolios using the three aforementioned strategies. We then assessed portfolio performance based on the number of species for which representation targets were met in each scenario. The all-scenarios portfolio consistently outperformed the other portfolios, and was more robust to ‘errors’ (e.g., when an assumed specific scenario did not occur). On average, the all-scenarios portfolio achieved representation targets for five additional species compared with the current distributions portfolio (approximately 33 versus 28 species). Our findings highlight the importance of considering a broad and meaningful set of scenarios, rather than relying on the current situation, the expected occurrence of specific scenarios, or the worst-case scenario.

An ex-post evaluation approach to assess the impacts of accomplished urban structure shift on landscape connectivity

Many studies have been conducted to evaluate the effects of different urban structures on landscape connectivity, and most of them rely on the comparison approach or ex-ante scenario analysis. However, we still lack an ex-post method to capture the consequences of accomplished urban structure shift (from monocentric to polycentric), which is guided by the land use planning. To fill this gap, we develop an ex-post evaluation approach which integrates counterfactual analysis and landscape graphs. Counterfactual analysis is combined with cellular automata simulation model, to uncover what the city might look like, if it had continued to expand in a monocentric structure; and the landscape graphs enable us to reveal the possible landscape connectivity in actual and counterfactual scenarios. We select Nanjing city as the study area and 4 target species, to delve into the varying impacts of the urban structure shift on different taxonomic groups. Our case study demonstrates that: (1) the impact of urban structure shift is more relevant to the long disperser; (2) the actual landscape (polycentric) would facilitate the bird's dispersal, while (3) forest mammals have higher connectivity in the counterfactual scenario (monocentric), and the possible reasons are discussed. Finally, we demonstrate that the urban structure shift might not necessarily cause the connectivity decline, on condition that the key connectivity providers are identified by integrating ecological network analysis into the land use planning, and well preserved in the shift.

Simulation of high-rise building evacuation considering fatigue factor based on cellular automata: A case study in China

Stair evacuation plays a crucial role in building evacuation since stairs are generally the only means to evacuate high-rises on fire. To ensure safety stair design, the Life Safety Code suggests using a performance-based design approach, which requires evacuation simulations. Most of existing simulations, however, do not consider the structure of stairs and fatigue of evacuees, and these simulations are not validated by real emergency events or experiments. This paper is on improving the simulation of pedestrian flow in the stairs of high-rises by addressing these issues. A new Cellular Automata simulation model is developed where the simulation map is divided into zones based on the stair structure, and the rule of evacuees’ movement for each zone is appropriately defined to simulate turning behavior. To validate the simulation, a fire drill was held in two high-rise buildings. In this drill, evacuees felt tired after a walk. The simulation results demonstrate that, compared with the simulation without fatigue factor, our simulation can predict the evacuation time more accurately. Building designers can make evacuation plans and strategies based on the new simulation.

A cellular automaton simulation model for pedestrian and vehicle interaction behaviors at unsignalized mid-block crosswalks

At unsignalized crosswalks, interactions between pedestrians and vehicles often lead to traffic safety hazards due to absence of traffic control and unclear right-of-ways. To address this safety problem, there is a need to understand the interaction behaviors of pedestrians and vehicles that are complicated by a variety of traffic and roadway attributes. The prime objective of this study is to establish a reliable simulation model to represent the vehicle yielding and pedestrian crossing behaviors at unsignalized crosswalks in a realistic way. The model is calibrated with detailed behavioral data collected and extracted from field observations. The capability of the calibrated model in predicting the pedestrian-interaction events as well as estimating the driver yielding rate and pedestrian delay are also tested and demonstrated. Meanwhile, the traffic dynamics in the vicinity of the crosswalk can be meaningfully represented with simulation results based on the model. Moreover, with the definitions of the vehicle-pedestrian conflicts, the proposed model is capable to evaluate the pedestrian safety. Thereby, the simulation model has the potential to serve as a useful tool for assessing safety performance and traffic operations at existing facilities. Furthermore, the model can enable the evaluation of policy effectiveness and the selection of engineering treatments at unsignalized crosswalks to improve safety and efficiency of pedestrian crossing.

Crime Theory Evaluation Using Simulation Models of Residential Burglary

This paper provides new insights on the study of crime modeling through the development of a hybrid cellular automaton (CA) and Multi-agent System (MAS) simulation model that is able to combine components of multiple criminological theories to forecast the locations of residential burglary targets: journey to crime (JTC), social disorganization (SD) theory, and routine activity (RA) theory. In order to combine individual factors from each theory into a unified model, Analytic Hierarchy Process (AHP) was employed for hierarchical parameter selection. The model is then evaluated using data on offenders obtained from the Dallas Police Department to examine how different crime theories perform in the prediction of residential burglary. Compared to the SD- and RA-weighted models, the JTC-weighted model performed the best when comparisons were made to actual burglary locations. The findings demonstrate that the simulation models of crime provide test beds for research into the explanatory power of various crime theories.

A simulation-based optimization approach for passenger train timetabling with periodic track maintenance and stops for praying

This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to determine departure and arrival times to or from each station in order to prevent collisions between trains and effective utilization of resources. The two proposed methods are based on integration of a simulation and an optimization method to simulate train traffic flow and generate near optimal train timetable under realistic constraints including stops for track maintenance and praying. The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimization method. In the second proposed approach, a CA simulation model combines with dynamically dimensioned search optimization method. The proposed models are applied to hypothetical case study to demonstrate the merit of them. The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable. The results show the first method is more efficient than the second method to obtain near optimal train timetabling.

Robot Guided Crowd Evacuation

The congregation of crowd undoubtedly constitutes an important risk factor, which may endanger the safety of the gathered people. The solution reported against this significant threat to citizens safety is to consider careful planning and measures. Thereupon, in this paper, we address the crowd evacuation problem by suggesting an innovative technological solution, namely, the use of mobile robot agents. The contribution of the proposed evacuation system is twofold: (i) it proposes an accurate Cellular Automaton simulation model capable of assessing the human behavior during emergency situations and (ii) it takes advantage of the simulation output to provide sufficient information to the mobile robotic guide, which in turn approaches and redirects a group of people towards a less congestive exit at a time. A custom-made mobile robotic platform was accordingly designed and developed. Last, the performance of the proposed robot guided evacuation model has been examined in real-world scenarios exhibiting significant performance improvement during the crucial first response time window.

An Improved Personnel Evacuation Cellular Automata Model Based on the Ant Colony Optimization Algorithm

Nowadays, public safety has already attracted great attention, especially when natural disasters and other emergencies happen more and more frequently. So, personnel evacuation simulation research in the populated areas has become one of the core issues to reduce the social damage. To improve the simulation theory, this paper puts forward an improved cellular automata model using some idea of the classic Ant Colony Optimization Algorithm for reference when making rules for the evacuating personnel. And the improved model takes the interaction among the crowd and the influences exerted by the evacuating personnel upon the environment into account. The new model cares more specific details of both environment and the personnel, so it simulates the crowd psychology successfully and provides a more reliable theory that is to expand and improve the cellular automaton simulation model on personnel evacuation.

An Analysis on Mixed Traffic Flow of Conventional Passenger Cars and Microcars Using a Cellular Automata Model

Characteristics of traffic with microcars in traffic flow are analysed by using a cellular automata simulation model. 400m of urban highway and arterial road segment both with two lanes are supposed respectively, including an intersection delay with a signal cycle in the middle of the latter. Two kinds of vehicles, conventional car and microcar are defined in the traffic stream. The changing lane behaviour which can make vehicles have more opportunity to higher their speed is considered. Traffic flow with different rates of microcars is investigated in the simulation. Traffic flow and average speed are computed for congestion point of view. The results demonstrate that introduction of microcar will relieve traffic congestion to some extent, and vehicles passing through per hour become more than those without microcars.

What enables coexistence in plant communities? Weak versus strong species traits and the role of local processes

Explaining the coexistence of species that basically depend on the same resources has been a brainteaser for generations of ecologists. Different mechanisms have been proposed to facilitate coexistence in plant communities, where space is an important resource. Using a stochastic cellular automaton simulation model we analyze – separately and in combination – the influence of different species traits and processes which alter local competition on the coexistence of plant species over a fixed time horizon. We show that different species traits operate on different time scales in competition. We therefore suggest the concept of weak versus strong traits according to short- or long-term exclusion of species differing in these traits. As a consequence, highly non-linear trade-offs between weak and strong traits can result in communities. Furthermore, we found that trade-offs based on physiological species traits such as plant lifetime, dispersal range and plant growth, did not support broad and long-term coexistence—further processes such as density-dependent mortality and light-dependent colonization were necessary. This suggests that coexistence in plant communities requires (stabilizing) local processes to support the (equalizing) trade-offs in species traits.

Modeling Bicycle Facility Operation

Current concerns surrounding regional air pollution, climate change, rising gasoline prices, and urban congestion could presage a substantial increase in the bicycle mode share. However, state-of-the-art methods for the safe and efficient design of bicycle facilities are based on difficult-to-collect data and potentially dubious assumptions regarding cyclist behavior. Simulation models offer a way forward, but existing bicycling models in the academic literature have not been validated with actual data. These shortcomings are addressed by obtaining real-world bicycle data and implementing a multilane, inhomogeneous cellular automaton simulation model that can reproduce observations. The existing literature is reviewed to inform the data collection and model development. It is found that the model emulates field conditions while possibly underpredicting bike path capacity. Since the simulation model can “observe” individual cyclists, it is ideally suited to determine level of service based on difficult-to-observe cycling events such as passing. Future work on data collection and model development is suggested.

Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

Empirical models designed to simulate and predict urban land‐use change in real situations are generally based on the utilization of statistical techniques to compute the land‐use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non‐parametric approaches. This work introduces a simulation experiment on intra‐urban land‐use change in which a supervised back‐propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land‐use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium‐sized town in the Midwest of São Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985–1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures.