Proposed Cellular Automata Model Research Articles

Methodological Insights into Implementing cellular automata models for simulating seagrass dynamics: Responses to global change effects

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.•This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.•Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.•Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Mesoscale modeling of continuous dynamic recrystallization in Ti-10V-2Fe-3Al alloy

Continuous dynamic recrystallization (CDRX) is the dominant restoration mechanism of near-β titanium alloys during hot deformation. But till now, there is seldom visual modeling of the process which limits its industrial application. Based on CDRX mechanisms characterized by low-angle grain boundaries (LAGBs) formed and partially transformed into high-angle grain boundaries (HAGBs), the present study established a cellular automata (CA) model to investigate the substructure evolution and mechanical response of Ti-10V-2Fe-3Al alloy. Besides, a new set of equations for subgrain rotation considering the effect of the α-phase was integrated into the CA model to improve its applicability in titanium alloy. The model was validated by experimental results and then applied to predict the substructure evolution during hot working. Simulation analysis revealed that, except for high strain rates and temperatures, a high fraction of α/β phase boundaries could also promote the development of CDRX. The proposed CA model provided an effective method for microstructure optimization in hot forging of the Ti-10V-2Fe-3Al alloy.

A cellular automata model for car–truck heterogeneous traffic flow considering drivers’ risky driving behaviors

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.

Modelling the transportation of marine plastics over the ocean surface by Cellular Automata

This article describes a novel Cellular Automata (CA) model to predict the transportation of buoyant marine plastics. The proposed CA model provides a simpler and more affordable approach to a field where the computationally intensive Lagrangian particle-tracking models dominate. The transportation of marine plastics was investigated using well-defined, probabilistic rules governing the advection and diffusion processes. The CA model was applied to evaluate the impact of two input scenarios, namely a “population” and a “river” scenario. Of the sub-tropical gyres, a high percentage of buoyant plastics were found in the Indian gyre (population: 5.0 %; river: 5.5 %) and North Pacific gyre (population: 5.5 %; river: 7 %). These findings show good agreement with previously published results from particle-tracking models. The CA model could be a useful rapid-scenario assessment tool for the estimation marine plastic pollution prior to more in-depth studies on effective mitigation measures to, for example, reduce plastics waste.

AI-Based Secure Undergraduates’ Ideological and Political Public Opinion Strategy

The popularity of the Internet makes everyone have the right to speak. People are no longer passive receivers of information but propagators of information, and for this reason, cyber opinion comes into existence. Some social events that can arouse people’s sympathy and are related to people’s vital interests will eventually form cyber opinions through netizens’ comments, discussions, and forwarding. As members of the society, undergraduates have different interests, political tendencies, and social cognition. When making judgments and comments on controversial hot issues, undergraduates will make their own correct judgments of authenticity according to their own knowledge and social experience, which will make the propagation content of public opinion more diversified. Especially for ideological and political public opinion, it is very important to ensure the safety of the public opinion propagation model intelligently. In this paper, first, cellular automata (CA) of artificial intelligence (AI) is used to establish the propagation model of undergraduates’ ideological and political safety public opinion and the stochastic immune mechanism model is introduced. Then, a CA-based stochastic immune mechanism model is adopted to simulate the real propagation process of undergraduates’ ideological and political public opinion. Finally, the experimental results demonstrate that the moving probability of public opinion subjects has a great impact on the propagation speed and homogenization rate of cyber public opinion and the proposed CA model can well describe the actual propagation process of cyber public opinion. Meanwhile, the proposed propagation model can ensure the propagation of undergraduates’ ideological and political public opinion on safety.

Cellular Automata Modelling of Discontinuous Precipitation.

The fundamentals of discontinuous precipitation (DP) reaction modelling using a cellular automata (CA) method are presented. In the proposed CA model, cell states, internal variables, equations, and transition rules were defined to predict the manner of mass transport during DP reaction and to relate changes in the microstructure with corresponding changes in chemical composition. Furthermore, the concept of digital material representation (DMR) was introduced into the CA model, which allowed schematic images of the microstructure to be used as starting structures in the modelling of the DP reaction. The preliminary assumptions adopted in the proposed CA model for the DP reaction were verified by numerical simulations of the growth of discontinuous precipitates at a steady-state at the example of Al-22 at.% Zn alloy. The outcomes achieved from the CA simulations were presented in a different form than that most commonly used previously (single concentration profiles), namely as the 2D maps showing changes in Zn content accompanying the successive stages of growth of discontinuous precipitates. The model used for the description of the solute diffusion along of the reaction front (RF) allowed two-dimensional systems at the nano-scale to be treated within a reasonable simulation time. The obtained results indicate that the developed CA model was able to realistically simulate the DP reaction, which was confirmed by the visualisation of migrating RFs together with associated chemical composition changes in the microstructure.

Study on the nonlinear performance degradation of reinforced concrete beam under chloride ion corrosion

The chloride-induced corrosion deteriorates the performance of reinforced concrete (RC) structures. This paper proposes a novel method for the nonlinear analysis of RC structures under chloride ion corrosion based on the cellular automata (CA) method and the fiber model of FEM. The CA model is established to simulate the chloride diffusion in cross-section of RC continuous T-beam, then the time-varying chloride ions concentration in cross-section are evolved. The damage indices corresponding to the corrosion of steel bar and deterioration of concrete have been defined and their variations with time are discussed. Following that, the CA model is coupled with the fiber model of FEM to compile an matlab program to analyze the performance of corroded RC continuous T-beam. Thus the deflection-time curve, the load–deflection curve, the strain distribution of cross-section, the bending moment–curvature curve and the time evolution of bending moment for the T-beam are obtained to discuss the time-variant nonlinear performance degradation process of corroded RC beam. It can be concluded that the proposed CA model can effectively and accurately simulate the diffusion process of chloride ions in concrete. Besides, the damage model introduced in this paper can quantifies the relationship between the damage caused by chloride ions and corrosion time. Furthermore, the matlab program established in the present study can be effectively applied to the nonlinear analysis of corroded RC beams. Considering the co-action of geometric and material non-linearity and corrosion, the calculation results can more accurately describe the actual stress process of the RC structure. Finally, the proposed method can be used as a effective theoretical tool for the life assessment of RC structures in corrosive environment.

Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model

Interactions among cities are playing an increasingly significant role in driving urban expansion in urban agglomerations. Many studies have combined the gravitational field model (GFM) with the cellular automata (CA) model to analyze the impact of urban spatial interaction on urban agglomerations. However, previous studies have used threshold-based CA models, which cannot ensure that the amount of simulated urban land (an important control variable) of the proposed CA model and contrasting CA models are consistent during experiments. In addition, previous studies have applied only one or two indicators to represent spatial interactions among cities, which cannot fully reflect the urban spatial field intensity levels within city clusters. Furthermore, previous studies have tended to apply simple mining methods (e.g., logistic regression) to mine the transition rules of CA models. These methods cannot explore the complex relationships between urban growth and driving factors (including urban spatial field intensity). This study proposes an integrated gravitational field model (IGFM) by combining comprehensive economic indicators, time-cost distance and information flow intensity to quantify urban spatial field intensity. The random forest (RF) algorithm, a machine learning method with a strong fitting ability, is adopted to mine the complex transition rules of a demand-driven CA model, which the previously developed simple mining methods are unable to accomplish. The use of demand-driven CA ensures the consistency of urban demand between contrasting CAs, which can help generate more rigorous results. The proposed IGFM-RF-CA is applied to simulate urban growth in the Beijing-Tianjin-Hebei urban agglomeration (BTH). The IGFM-RF-CA can achieve high simulation accuracy not only for a whole area but also for most subdistricts, especially in relatively developed cities. We also find that the intensity of information flow in the simulation can significantly improve the performance of the CA model, particularly in small cities located along the periphery of the BTH, which are characterized by relatively low economic development but high Internet popularity. We suggest that some small cities (e.g., Zhangjiakou and Chengde) can promote their development by increasing their Internet popularity.

Complex Modelling and Design of Catalytic Reactors Using Multiscale Approach—Part 2: Catalytic Reactions Modelling with Cellular Automata Approach

The presented work is devoted to reactions of obtaining 4,4’-Diaminodiphenylmethane (MDA) in the presence of a catalyst model. The work describes the importance of studying the MDA obtaining process and the possibility of the cellular automata (CA) approach in the modelling of chemical reactions. The work suggests a CA-model that makes it possible to predict the kinetic curves of the studied MDA-obtaining reaction. The developed model was used to carry out computational experiments under the following different conditions—aniline:formaldehyde:catalyst ratios, stirrer speed, and reaction temperature. The results of computational experiments were compared with the corresponding experimental data. The suggested model was shown to be suitable for predicting MDA-obtaining reaction kinetics. The proposed CA model can be used with the CFD model, suggested in Part 1, allowing the implementation of complex multiscale modeling of a flow catalytic reactor from the molecule level to the level of the entire apparatus.

Accelerating fuzzy cellular automata for modeling crowd dynamics

Pedestrian and crowd dynamics are physical phenomena that are fundamentally characterized by non-linear complexity. In the same time, the need of modern way of living seeks for such dynamics real time modeling also enabling computationally efficient and affordable solutions for sake of safety and easiness of people located in gathering places all over the world. Towards this direction, Cellular Automata (CAs), a parallel computational model combining macro- and microscopic inherent attributes that could severely help with adequate modeling of the aforementioned dynamics, are one of the best compromises among different competing computational techniques. In order to overcome CAs deterministic nature, in this paper the incorporation of fuzzy logic principles in a CA model that simulates crowd dynamics and crowd evacuation processes, with the usage of a Mamdani type fuzzy inference system, is proposed. More specifically, basic concepts of fuzzy logic such as linguistic variables and if-then rules are attributed to the proposed CA model to preserve fuzzy consequents and fuzzy antecedents thus resulting in a realistic and rather efficient modeling approach. Furthermore, in the paper the implementation of fuzziness in CA dynamics is tackled with the acceleration of the proposed model through fully parallel execution on Graphics Processing Units (GPU). The GPU implementation of the fuzzy CA model is analyzed in full detail and stressed against CPU corresponding implementation resulting to an important speed up of fuzzy CA execution. This is further explored through the GPU applications of the fuzzy CA model in a real building, namely the museum `CONSTANTIN XENAKIS', in Serres, Greece.

Discrete modelling techniques in application to generation of 3D synthetic microstructures of porous metallic materials

Abstract Development of two approaches for creation of digital microstructure representations of sintered porous metals for further numerical simulations is the goal of the paper. Initially, an approach based on series of exact replicas of real 2D microstructures processed by image analysis and reconstruction algorithms was proposed. Thus, developed in-house code for 3D reconstruction of 2D images obtained from optical microscopy is briefly described within the paper. Advantages and limitations of the approach are discussed in that part. Then, to facilitate the process of generation of digital models, another solution, based on synthetic microstructures, was developed with discrete numerical method and is presented within the work. Two algorithms based on the Cellular Automata (CA) technique were developed and implemented. Description of major models assumption including generation of computational domains, cells states, boundary conditions and finally transition rules is presented for the proposed CA model. Obtained digital microstructure models are eventually subjected to numerical simulation by means of finite element tool to highlight capabilities offered by synthetic microstructures in evaluation of local material behavior.

Parallel implementation of aircraft disembarking and emergency evacuation based on cellular automata

In this paper we present a model based on the parallel computational tool of cellular automata (CA) capable of simulating the process of disembarking in a small airplane seat layout, corresponding to Airbus A320/ Boeing 737 layout, in search of ways to make it faster and safer under normal evacuation conditions, as well as emergency scenarios. The proposed model is highly customizable, with the number of exits, the walking speed of passengers, depending on their sex, age and height, and the effects of retrieving and carrying luggage. Additionally, the presence of obstacles in the aisles as well as the emergence of panic being parameters whose values can be varied in order to enlighten the disembarking and emergency evacuation processes are considered in detail. The simulation results were compared to existing aircraft disembarking and evacuation times and indicate the efficacy of the proposed model in investigating and revealing passenger attributes during these processes in all the examined cases. Moreover, we parallelized our code in order to run on a graphics processing unit (GPU) using the CUDA programming language, speeding up the simulation process. Finally, in order to present a fully dynamical anticipative real-time system helpful for decision-making we implemented the proposed CA model in a field programmable gate array (FPGA) device, and recreated the results given by the software simulations in a fraction of the time. We then compared and exported the performance results among a sequential software implementation, the implementation running on a GPU, and a hardware implementation, proving the consequent acceleration that results from the parallel CA implementation in specific hardware.

Simulation Model for the Decision-Making Behavior in Pedestrian Evacuation with Floor Field Cellular Automata Approach

In order to simulate pedestrian evacuation from a room with multiple exits, an extended floor field cellular automata (CA) model is proposed to describe the decision-making behavior of pedestrians in a realistic way. The problem of the potential distortion and reciprocating route of pedestrians is solved. Meanwhile, the visual factor and the visual field are introduced to reveal the effect of visual sense on intelligent decision-making behavior of evacuees. To make the simulation more reasonable, human psychological behaviors are considered in the model, such as panic psychology, self-protection awareness, competition awareness, etc. Moreover, the width and the layout of exits are also taken into account and the critical value is obtained by simulation. The results show that the proposed CA model is efficient and realistic in the assessment of both human evacuation and building design.

Friction vibration modeling with detachment and adhesion by Cellular Automata

Friction vibration in a dynamical system composed of a slider supported by a spring on a belt was modeled and simulated by Cellular Automata (CA). Friction vibration in mechanical systems has been studied for a long time and various models have been proposed to understand its physical phenomena, but dynamical behavior of an elastic body on friction surface has not been revealed yet. This may be caused by the complexity of friction between two surfaces, and CA can be used as a strong tool of modeling physical phenomena introducing local neighbor and transition rules based on observation of phenomena. It may not be easy to derive the governing equation of motion including the precise mechanism of friction. In this study, a modeling procedure of friction dynamics by CA was discussed. The new model was based on a two-dimensional spring-block model proposed by Olami, Feder and Christensen, but an additional layer of internal surface was introduced to consider the contact area of sliding surfaces. The self-excited vibration, including the stick-slip vibration and the damped free vibration could be simulated in the proposed CA model.

The Study of Abrasive Water Jet Cutting Front Development using a Two-Dimensional Cellular Automata Model

In this paper, the cutting front development during abrasive water jet (AWJ) cutting is studied using a two-dimensional cellular automata (CA) model, hence the striation formation phenomenon is studied indirectly. To calculate the shape of the cutting front, the CA model uses the following inputs: cutting velocity, AWJ intensity and material type. The cutting process is described by simplified material removal and AWJ propagation models in the form of CA rules. The rules encompass AWJ ability to erode the workpiece material and, inversely, the workpiece material’s resistance to the erosion process. The proposed CA model is validated by checking the trend of the cutting front development at various input parameters. The simulation results are in good agreement with experimentally obtained trends and thus confirm the proper setup of CA rules. This gives a better insight into the AWJ cutting mechanism.

Three-dimensional cellular automata modelling of cleavage propagation across crystal boundaries in polycrystalline microstructures

A three-dimensional cellular automata (CA) with rectilinear layout is used in this work to create and cleave polycrystalline microstructures. Each crystal is defined by a unique randomly generated orientation tensor. Separate states for grains, grain boundaries, crack flanks and crack fronts are created. Algorithms for progressive cleavage propagation through crystals and across grain boundaries are detailed. The mesh independent cleavage criterion includes the critical cleavage stress and the length scale. Resolution of an arbitrary crystallographic plane within a 26-cell Moore neighbourhood is considered. The model is implemented in Fortran 2008 coarrays. The model gives realistic predictions of grain size and mis-orientation distributions, grain boundary topology and crack geometry. Finally, we show how the proposed CA model can be linked to a finite-element model to produce a multi-scale fracture framework.

Cellular automaton model of crowd evacuation inspired by slime mould

In all the living organisms, the self-preservation behaviour is almost universal. Even the most simple of living organisms, like slime mould, is typically under intense selective pressure to evolve a response to ensure their evolution and safety in the best possible way. On the other hand, evacuation of a place can be easily characterized as one of the most stressful situations for the individuals taking part on it. Taking inspiration from the slime mould behaviour, we are introducing a computational bio-inspired model crowd evacuation model. Cellular Automata (CA) were selected as a fully parallel advanced computation tool able to mimic the Physarum’s behaviour. In particular, the proposed CA model takes into account while mimicking the Physarum foraging process, the food diffusion, the organism’s growth, the creation of tubes for each organism, the selection of optimum tube for each human in correspondence to the crowd evacuation under study and finally, the movement of all humans at each time step towards near exit. To test the model’s efficiency and robustness, several simulation scenarios were proposed both in virtual and real-life indoor environments (namely, the first floor of office building B of the Department of Electrical and Computer Engineering of Democritus University of Thrace). The proposed model is further evaluated in a purely quantitative way by comparing the simulation results with the corresponding ones from the bibliography taken by real data. The examined fundamental diagrams of velocity–density and flow–density are found in full agreement with many of the already published corresponding results proving the adequacy, the fitness and the resulting dynamics of the model. Finally, several real Physarum experiments were conducted in an archetype of the aforementioned real-life environment proving at last that the proposed model succeeded in reproducing sufficiently the Physarum’s recorded behaviour derived from observation of the aforementioned biological laboratory experiments.

Real-time flood simulations using CA model driven by dynamic observation data

It is difficult to obtain accurate simulation results without observation data. So using real-time dynamic observation data in the simulation process has become an academic frontier of international research. This paper is a probing research on the data-driven adaptive modeling and automatic refactoring methods of flood routing simulation. A cellular automata (CA) data-driven flooding model was developed using the Hunhe River in Shenyang City as a case study. The proposed model can increase the accuracy of simulations by calculating differences in the water stages using high temporal resolution observational data. Meanwhile, corresponding parameter analysis was carried out based on the proposed CA model and the best lagging time between simulation and observation was discussed.

Comparison of Two Simulation Approaches to Safety Assessment: Cellular Automata and SSAM

AbstractA study is conducted to compare two simulation methods for estimating conflicts between road users. An improved cellular automata (CA) model is proposed to estimate the occurrences and severity of traffic conflicts (both vehicle–vehicle and vehicle–pedestrian) at signalized intersections. The proposed CA model is compared with a calibrated method of a surrogate safety assessment model (SSAM) based on Vissim. Simulated conflicts from both methods are compared with observed vehicle conflicts from automated vehicle tracking for both occurrences and severity. Simulation results show that the CA approach is able to replicate realistic conflicts. However, SSAM tends to overestimate occurrences and underestimate the severity of rear-end and lane-change conflicts. SSAM has also been found to overestimate the severity of crossing conflicts. Furthermore, the proposed CA model is able to estimate conflicts between vehicles and pedestrians.

Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata

The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.