Cellular Automata Simulation Research Articles

Transverse microstructural evolution and its cellular automata simulation during hot rolling of AZ31 alloy wide-width plate

The microstructure characteristics and transverse distribution laws during the hot flat rolling of 1500 mm wide AZ31 plate were studied in this paper. The microstructure morphology was analyzed experimentally, the thermal-mechanical coupling behavior was simulated, and the cellular automaton (CA) microscopic simulation model was established. A series of CA simulation functions and related material parameters were presented, which can accurately predict the microstructure evolution during rolling process and be verified through experimental results. The results showed that, the horizontal microstructure distribution of wide AZ31 plate was uneven significantly, which is characterized by a typical chain dynamic recrystallization mechanism and obvious shear bands structure at the transverse edge area. In particular, the average grain size (Dm) and dynamic recrystallization volume fraction (VDRX) showed significant differences along the transverse. The recrystallization activation energy (Qact) and hardening index parameters (θ0) suitable for the evolution of wide plate rolling to different areas are determined by using the reverse search method based on Dm index and VDRX index.The accuracy of CA model is verified by comparing the grain size dispersion and analyzing the relative error. Moreover, based on the CA model, the continuous and uninterrupted distribution characteristics of transverse microstructure of wide AZ31 plate after rolling were obtained. CA simulation results shows that from transverse center to edge area, the heterogeneity of microstructure is remarkable. The Dm and VDRX increase first and then decrease, and will turn in 1/4 width.

Experimental study and cellular automata simulation of corrosion behavior of ferritic stainless steel in molten aluminum

Experimental study and cellular automata simulation of corrosion behavior of ferritic stainless steel in molten aluminum

Cellular automaton simulation and experimental validation of eutectic transformation during solidification of Al-Si alloys

The morphology of eutectic silicon in solidification microstructure is critical to the performance of Al-Si-based alloys. Simulating eutectic Si phase formation has been a challenge in ICME (integrated computational materials engineering) based design and manufacturing of solidification products of Al-Si-based alloys. In this study, our previous three-dimensional (3-D) cellular automaton (CA) model for α-Al dendritic growth was extended to include eutectic (α-Al + Si) transformation in multi-dendrite domains, providing a complete solidification simulation of critically important Al-Si based alloys. The quantitative results of the Si phase in the eutectic microstructure were experimentally validated using scanning electron microscopy and deep etching techniques. The simulation results show a good agreement with the experimental observations and calculations by the Scheil model and lever rule. This 3-D CA model is useful for predicting and optimizing the solidification microstructure including eutectic transformation during solidification processing such as casting, potentially welding, and additive manufacturing.

The Evolution of Ecological Space in an Urban Agglomeration Based on a Suitability Evaluation and Cellular Automata Simulation

Changing and reconstructing the ecological space of urban agglomerations is inevitable for ecological conservation and a scientific problem that needs urgent attention from geography, ecology, and urban and rural planning. Using ArcGIS and other software for data processing, this study established a spatial attribute database, constructed a land use conversion matrix of the Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration’s ecological space, and quantitatively analyzed the main changes in ecological land. Using a trained cellular automata model with predicted land use in 2035 as the threshold value, the simulation research was presented by creating two simulation scenarios for the spatial distribution of land use by 2035 in the “Green Heart” area of the CZX urban agglomeration. The simulation results were compared, and the constraining role of land use suitability evaluation on ecological space evolution was analyzed. This study found that the total area of ecological space in the Green Heart area saw a rapid reduction, and it predicted that, by 2035, the total area of the CZX Green Heart area will have decreased. Comparing the two simulation scenarios proved the hypothesis that zoning ecological space reconstruction based on a land suitability evaluation can effectively protect ecological space and ensure ecological network functions are harnessed.

2D and 3D cellular automata simulation on the corrosion behaviour of Ni-based alloy in ternary molten salt of NaCl–KCl–ZnCl2

When molten salt is used as a high-temperature heat carrier or electrolyte solution, the corrosion of molten salt is necessary to be studied regarding the device's safety. In this work, a cellular automata method is used to simulate the corrosion behaviour of alloys in ternary molten salt of NaCl–KCl–ZnCl 2 . The CA method described in this paper can be used to reproduce the distribution of corrosion products and components and the change in morphology and thickness of the corrosion layer in the phase of high-temperature corrosion, which provides a unique insight into the corrosion behaviour of chloride molten salt over time. In addition, we also consider the pitting corrosion that may occur during the corrosion process. Furthermore, the applicability of the model can predict the corrosion behaviour of different metals in molten chloride salt. • Cellular automaton (CA) method is proposed to study molten chloride salt corrosion. • The simulation of cross-sectional corrosion morphology of alloy is proposed. • The growth process of a single corrosion pit is simulated innovatively. • A novel 3D CA model realizes the calculation of the corroded volume.

Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting

The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.

Finding the Optimal Bus-Overtaking Rules for Bus Stops with Two Tandem Berths

Overtaking rule is a key factor for the estimation of bus discharge flow and bus delay at stops. In general, there are four kinds of overtaking rules, namely no-overtaking, enter-overtaking, exit-overtaking and free-overtaking. This paper studies a two-berth tandem bus stop in a saturated state and proposes calculation models for the maximum bus discharge flow and average berth blocking time under different overtaking rules. Cellular automata simulation is applied to verify the model’s reliability. Then the influence of bus dwell time characteristics and overtaking rules are analyzed. Results show that overtaking has a positive impact on the maximum bus discharge flow and average berth blocking time to a certain extent. If only one overtaking behavior is allowed, the exit-overtaking rule is recommended. The study reveals that overtaking behavior plays an important role in bus service level and operational efficiency. Bus-overtaking rules are suggested to be changed with different bus flow states to obtain the optimal berth effectiveness.

How to alter path dependency and promote the use of EPC model in public projects of China?

The key to promoting the EPC (Engineering, Procurement, Construction) model in China’s public construction projects is to alter the path dependence of a project owner’s choice of project delivery model (PDM). This study uses evolutionary game theory to discuss the mechanism of government incentives as an external motivation to alter path dependence in the PDM. In addition, a cellular automata simulation to examine the influence of various government incentives on the project owner’s choice. The results show that the combination of subsidies and penalties can produce the best incentive. Subsidies are more effective at promoting PDM institutional change, whereas penalties are more effective at preventing PDM institutional change from anti-recession effects. Based on our results, we propose that the Chinese government should take active subsidy measures at the initial stage of EPC promotion, and adopt a dynamic incentive strategy of continuously reducing subsidies and increasing penalties according to the improvement of the development degree of EPC model.

Cellular Automaton Simulation of Dendrite Growth in Solidification Process of Cr17 Stainless Steel under Mechanical Vibration

Herein, a coupled model of temperature field and dendrite growth is built to examine the solidification microstructure of Cr17 stainless steel under vibration. The temperature field is calculated with ANSYS FLUENT software. In addition, considering solute diffusion and local curvature, a low grid anisotropic cellular automaton (CA) model is used to simulate the dendrite growth. To verify the major source of crystal nucleus in the melts, a mechanical model of dendrite fracture is built, and an investigation is conducted on the critical condition of grain dissociation. As indicated from the results, the dendrites are stripped and dissociated into the melt when the vibration frequency and amplitude values exceed the critical condition of dendrite fracture, the grains are dissociated more easily, and the more equiaxed grains are indicated in the melt with the increase in the vibration frequency. It is preliminarily demonstrated that vibration can facilitate the dendrites to break and be dissociated from the chilling generator surface. The simulation results comply with the experiment.

Prediction of the Non-Isothermal Austenitization Kinetics of Fe-C-Cr Low Alloy Steels with Lamellar Pearlite Microstructure.

The austenitization of low alloy steels during rapid heating processes was involved in many kinds of advanced heat treatment technologies. Most of the previous research on the austenitization kinetics was focused on the spherical pearlite microstructures, which were different from the lamellar pearlite microstructures. In the present research, to predict the non-isothermal austenitization process of an Fe-C-Cr steel with lamellar pearlite, a novel 3-dimensional (3D) cellular automata model, which considered the influences of the coupling diffusion of Cr and C, and the interfacial diffusion between pearlite lamellae and the pearlite lamellar orientation, was established based on the thermodynamic equilibrium data obtained from the Thermo-Calc software and the simulation results of the DICTRA module. To clarify the influences of the heating rate on the austenitization kinetics and validate the simulation results, the austenitization processes of a Fe-1C-1.41Cr steel for different heating rates were studied with a series of dilatometric experiments. The good agreements between the cellular automata simulation results and the experimental results showed that the newly proposed cellular automata model is reasonable. The experimental results show an obvious change of the transition activity energies from the low to high heating rates. The transition from partitioning local equilibrium (PLE) to non-partitioning local equilibrium (NPLE) mechanisms was proved with DICTRA simulations. Basing on the simulation results, the influences of the pearlite lamellae orientation on the austenitization kinetics and the topological aspects of austenite grains were evaluated. In addition, the topological aspects of the rapidly austenitized grains were also compared to the normal grains.

Avalanches of magnetic flux rope in the state of self-organized criticality

ABSTRACT The self-organized criticality (SOC) is a universal theory to explain the ubiquitous power-law size distributions of astrophysical instabilities such as solar eruptions. One way to understand the dynamical processes of an SOC system is through cellular automaton (CA) simulations. Here, we develop a three-dimensional solar CA model that assumes a twisted magnetic flux rope (MFR), in which the avalanche takes place when a local magnetic vector potential exceeds a Gaussian distributed instability criterion, triggered by a global and space-dependent energy driving mechanism. To avoid non-physical released energies, an energy screening mechanism is applied to calculate the avalanche energies of each time-step. Our results show that the statistics of the CA simulated flaring events are comparable to the frequency distributions of observed solar flares originating from an individual active region. Due to the fact of the universality of MFRs, the CA model can be applied to many other astrophysical SOC systems.

Unsignalized Intersection Capacity Estimation Through Traffic Rule Re-adjustments Using Agent-Based Cellular Automata Simulations

Unsignalized Intersection Capacity Estimation Through Traffic Rule Re-adjustments Using Agent-Based Cellular Automata Simulations

A Mesoscopic Simulation Approach Based on Metal Fibre Characterization Data to Evaluate Brake Friction Performance

The coefficient of friction (COF) is an important parameter when evaluating brake system performance. It is complex to predict friction due to its dependence on parameters, such as sliding velocity, contact pressure, temperature, and friction material mixtures. The aim of this work is to evaluate the macroscopic COF of a disc brake system under specific braking conditions by a meso-scale approach, using a cellular automaton simulation where the friction material mixture is modelled starting from its basic components. The influence of the local components in contact is taken into account. Simulated COF values are in line with the experimental values.

Application of Cellular Automata with Improved Dynamic Analysis in Evacuation Management of Sports Events

In this paper, the cellular automaton simulation technology is applied to the evacuation management of sports events, combined with the dynamic analysis method to analyze the collision of people in the evacuation process. At the same time, the cellular automaton model is used to refine the evacuation space to simplify environmental modeling, and the network model is used to determine the individual evacuation path from a macroperspective, simplify modeling, and improve simulation efficiency. In addition, this article simulates the evacuation process of the cellular automaton according to the actual evacuation situation of sports events and constructs the evacuation management system of sports events. Finally, this article evaluates the effect of the model in conjunction with experiments. The experimental results show that the role of the evacuation management system for sports events based on cellular automata proposed in this paper is obvious.

A Cellular Automata Model for Heterogeneous Traffic Flow Incorporating Micro Autonomous Vehicles

Despite the fact that significant research efforts have been made to the traffic flow theory of autonomous vehicles and manual vehicles, few existing studies have incorporated different modes of both vehicles in their analysis. In this study, we develop a cellular automata simulation model to investigate the impact of different modes of autonomous vehicles (autonomous car, autonomous bus, and autonomous micro car) and conventional vehicles (manual car, manual bus, and manual micro car) on the characteristics of traffic flow. A new type of autonomous mode, i.e., autonomous micro car, is investigated in the model to study the effects of this vehicle mode on the overall capacity of the network. Furthermore, two types of lane-changing behavior, i.e., aggressive lane changing and polite lane changing, are incorporated into the model. The results reveal that micro cars (manual and autonomous) have the potential to reduce traffic congestions and increase the capacity or flow rate (vehicles/hour) of the road. Where the average vehicle occupancy is less than 2, if autonomous micro cars are deployed alongside autonomous cars, the flow rate (vehicles/hour) can be increased significantly. The results highlight the significance of the autonomous micro cars to traffic flow, passenger occupancy, and road capacity.

Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

Joint apron-runway assignment for airport surface operations

Airport surface operations, such as off-block control, taxi routing, and runway sequencing, are typically confined to a fixed network topology with given origin–destination (O-D) pairs for departures and arrivals. Reconfiguring the O-D distribution of flights by actively assigning their aprons and runways is a potentially effective measure to maximize the utilization of network capacity. To date this has not been investigated in the literature due to its complexity involving operational constraints, assignment rules, and different stakeholders. This paper demonstrates the significant potential of O-D reconfiguration in improving surface network efficiency by proposing a joint apron-runway assignment framework for pre-tactical operations. This is underpinned by a comprehensive review of apron and runway assignment rules, including constraints and preferences, and an elaborated optimization scheme that encompasses lexicographic and iterative approaches along with temporal buffers to absorb uncertainties in pre-tactical operations. The proposed apron-runway assignment is implemented and assessed in a case study at Beijing Capital International Airport. An airport cellular automata simulator is employed for quantitative evaluation, and qualitative assessment is based on interviews with subject matter experts. Compared to the current operations, the proposed apron-runway assignment is very promising, with reductions in total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time, and fuel consumption by 15.5% (Table 3), 6.2%, 19.8%, 17.6% (Table 4) and 6.6% (Table 5) respectively; gated assignment is increased by 11.8% (Table 4). Importantly, these benefits remain robust to unforeseen flight delays, as demonstrated in a sensitivity analysis.

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.

Dynamic Recrystallization Evolution Mechanism and Cellular Automaton Simulation for Ea4t High-Speed Railway Axle Steel During Hot Deformation

Dynamic Recrystallization Evolution Mechanism and Cellular Automaton Simulation for Ea4t High-Speed Railway Axle Steel During Hot Deformation

Study of improved signal‐based merge strategy in work zone areas based on Cellular Automata simulation

Work zones have critical effects on traffic safety and efficiency. Previous researchers have developed lots of merge strategies to regulate drivers’ behaviours, which can be divided into early merge and late merge. As a special kind of late merge, signal-based merge strategy was proposed in recent years to reduce traffic conflicts and increase throughput at bottlenecks. Although traffic signal has been confirmed to improve the traffic in work zone areas effectively, the existing strategy still has limitations that require further improvements. In this research, an improved signal-based merge (IM) strategy was proposed to fulfil the research gaps. The strategy included a calculation method of green time considering the relationship between arrival traffic and the bottleneck's capacity and a dynamic signal-phase conversion method. Based on the Cellular Automata simulation, the effectiveness of the IM strategy was verified through parameter sensitive analysis and comparative study with the existing merge strategies. The results showed that the IM strategy could lead to greater throughputs, lower traffic delays, less aggressive merge and more balanced queue length. Furthermore, the IM strategy also showed great stability in response to the changes of parameters and the imbalance of arrival traffic. Overall, the proposed IM strategy is considered more effective in improving the traffic management in work zone areas and is feasible to be applied in practice.