Automata Simulation Research Articles

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.

Semantic Modeling Approach Supporting Process Modeling and Analysis in Aircraft Development

With the increasing complexity of aircraft development programs, the development processes of aircraft and their subsystems are continuously becoming complicated, leading to the growing risks of development cost across the entire life cycle. In this study, we proposed a model-based systems engineering approach to support process modeling of aircraft development using a multi-architecture modeling language KARMA. Simultaneously, property verification and hybrid automata simulation were used to implement the static cost analysis of each work task and dynamic cost analysis of the entire development process. Finally, a development process model of aircraft avionics system was created using a case study, in which cost analysis is implemented by the KARMA language. From the result, we found that the KARMA language enables the integration of the process modeling with static and dynamic analyses of the development process in a multi-architecture modeling tool MetaGraph 2.0.

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.

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.

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

A Two-Stage Fuzzy Optimization Model for Urban Land Use: A Case Study of Chongzhou City

Under the background of New-type Urbanization, with the continuous advancement of urbanization and the all-round development of cities, all kinds of demands are also rising. In the case of demand, it is difficult to quickly adjust from the land supply side and to guide the optimization of the structure and layout of land use is one of the methods to achieve this based on the current situation and shortage of urban land use structure and spatial arrangement. Because of the complexity, uncertainty and dynamics of the land use system, it is necessary to use an uncertain model to accurately describe and propose the approximate optimal solution, so this study analyzes the influencing mechanism of land use and optimize the land use structure under uncertainties by using a Bayesian network and fuzzy mathematical programming. Based on the results of the two stages of analysis, the cellular automata simulation is completed. The framework is applied to Chongzhou city in western China. The results indicated that the optimal land space for cultivated land is in the middle and the south based on the joint influence probability of arable land and urban construction land. The conversion probability of the area near the east is low, and the joint impact probability of construction land in all areas is generally similar except for the western protection area. After the optimization of the fuzzy planning, the optimal construction land scale is 69.42 km2. Under the condition that the cultivated land’s red line is guaranteed, there is still 98.87 km2 of space for the increase in cultivated land. It is found through simulation that the increase in construction land would occur in the central and western parts of Chongzhou, which may be caused by the urban siphon effect. According to Monte Carlo verification, when the conversion probability exceeds 50%, the cultivated land could be turned into urban construction land, with an accuracy of 91.99%. Therefore, this proposed framework is helpful to understand the process of land use and provides a reference for making scientific and reasonable territorial spatial planning and guiding land use practice under uncertainties.

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.

Cellular automaton simulation of the quantum Hotelling game with reservation cost

Cellular automaton simulation of the quantum Hotelling game with reservation cost

The effects of sample size and sample prevalence on cellular automata simulation of urban growth

ABSTRACT This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.

Modified Cellular Automaton Simulation of Metal Additive Manufacturing

Modified Cellular Automaton Simulation of Metal Additive Manufacturing

Modified Cellular Automaton Simulation of Metal Additive Manufacturing

Modified Cellular Automaton Simulation of Metal Additive Manufacturing

Predictive integrated numerical approach for modeling spatio-temporal microstructure evolutions and grain size dependent phase transformations in steels

A computational modeling for predicting microstructure evolutions and mechanical properties of steels under thermo-mechanical-metallurgical process is established, for the first time, by integrating the finite element (FE) simulation, cellular automaton simulation (CA), and phase transformation kinetics. In this microstructural-integrated modeling, various recrystallization processes, such as dynamic recrystallization (DRX), meta-DRX, and static recrystallization (SRX), are formulated based on dislocation density based constitutive laws. With microstructure information provided by the CA modeling, the austenite grain size (AGS)-dependent phase kinetics in the form of continuous cooling transformation (CCT) diagram is applied for addressing the effect of AGS on transformations under various cooling conditions. The integrated numerical approach implemented in the FE software via user defined subroutines can simulate the morphology and size distribution of constituent grains, transformed fractions of various phases, hardness profiles and flow stresses after thermo-mechanical process with large plastic deformation. As a validation of the integrated modeling, the multiple oval-round pass hot rolling and subsequent cooling process are simulated for the seismic reinforcing steel bar and the predicted microstructure and mechanical properties are compared to those of experimental data.

GPU-Accelerated Cellular Automaton Model for Grain Growth during Directional Solidification of Nickel-Based Superalloy

To accelerate the large-scale cellular automaton (CA) simulation for grain growth, a parallel CA model for grain growth was developed. The model was implemented based on the compute unified device architecture (CUDA) parallel computing platform. The model was verified by the grain growth of a single crystal and the columnar-to-equiaxed transition (CET) of an Al-7wt% Si specimen of uniform undercooling with a constant cooling rate. The grid independence of the model was verified. The grain growth of a plate-like casting of nickel-based superalloy during directional solidification process was simulated and the obtained results of grain density at each section with different heights were compared with the experimental data. The CET transition of directional solidified Al-7wt% Si cylindrical ingot was simulated. The grain texture and cooling curves were in good agreement with experimental results from the literature. Finally, high parallel performance of the CA model was obtained and evaluated.

Investigation of West Nile Virus Off-Pathway Membrane Fusion Mechanisms using Kinetic Modeling

West Nile virus (WNV) is a positive-sense, single-stranded RNA virus that causes febrile illness (West Nile fever) in humans. It is a member of the flavivirus genus, which includes dengue virus, tick-borne encephalitis, yellow fever virus, and Zika virus. To infect a host cell, West Nile virions first bind to the host cell plasma membrane. Upon binding, the virions are taken up via endocytosis, during which a decrease in pH causes the virions to fuse with the endosomal membrane. Fusion is mediated by the viral glycoprotein E, which is triggered by endosomal pH and undergoes a dimer-to-trimer rearrangement during the fusion process. Previously, we and others used simple chemical kinetics modeling and a cellular automaton simulation to show that an off-pathway state is necessary to fit WNV and Zika virus fusion data. Here, we investigate hypotheses to further explore what the off-pathway state entails, including whether the off-pathway state occurs before or after fusion loop insertion, and whether it occurs as a monomer or a multimer state. We also discuss comparisons between the ability of the off-pathway model to fit both West Nile virus and dengue virus fusion data.