Search

Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively.

Land use and land cover change (LUCC) process exhibits spatial correlation and temporal dependency. Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC. Cellular automaton (CA) models, recognized as powerful tools for simulating dynamic LUCC processes, are traditionally applied in LUCC, focusing on time-slice driving factor data, often neglecting the temporal dimension. However, the transformer architecture, a highly acclaimed model in machine learning, has been rarely integrated into CA models for the simulation of dynamic LUCC processes. To fill this gap, we proposed a novel spatiotemporal urban LUCC simulation model, namely, transformer-convolutional neural network (TC)-CA. Based on CA models that involve the utilization of a convolutional neural network (CNN) for extracting latent spatial features, TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features. The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA, conventional CNN-CA, artificial neural network-CA, and transformer-CA. Compared with the three non-transformer-based CAs, the TC-CA improved the figure of merit by up to 2.85%–8.14%. This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling. [ABSTRACT FROM AUTHOR]

The behavior and evolution trajectory of hydrofracture, which show a close relationship with the hydrothermal mineralization process, is greatly influenced by fluid flow and fluid pressure. However, further investigation is needed to achieve an in-depth understanding of the formation and evolution mechanisms behind the link between the rate of fluid pressure development and the occurrence of induced hydrofracture and mineralization process. We considered different fluid pressure development rates as the initial data for a cellular automaton model. With the increase in the fluid pressure increase rates, the corresponding hydrofracture became more focused, changing in scale from a large number of small-scale hydrofractures to a small number of large-scale hydrofractures. Episodes of fluid pressure fluctuation induced by either low or high fluid pressure increase rates were shown to trigger mineral precipitation and further contribute to the generation of strong spatially structured and enriched geochemical patterns. Moreover, the correlation length at the percolation threshold, which is of great significance to the degree and scale of mineralization, increased with the increasing fluid pressure increase rates. It was concluded that computational grids with high fluid pressure increase rates are much more prone to produce enriched geochemical patterns with strong spatial structures than grids with low fluid pressure increase rates owing to a larger correlation length at the percolation threshold. These results suggest that the way of fluid pressure development is a key factor for quantifying the behavior of hydrofracture and mineralization process. [ABSTRACT FROM AUTHOR]

In the field of physical logistics, efficient and precise control of material flow during the emptying of containers such as silos and wagons is crucial. This study introduces a not conventional cellular automaton model to simulate spillage from containers. The model is implemented using Excel macros, providing a user-friendly and accessible platform for logistics professionals. By modifying the traditional cellular automaton framework, the simulation can accurately represents the dynamics of material flow during container emptying processes. The proposed method allows for fine-tuned control over the spillage by integrating specific constraints and parameters, enhancing operational efficiency and reducing material loss. The versatility of the model makes it applicable to a wide range of logistical scenarios, demonstrating its potential as a valuable tool in physical logistics management. [ABSTRACT FROM AUTHOR]

This paper presents discontinuous Riemann integrable functions on the unit interval $ [0, 1] $ derived from the dynamics of two-dimensional elementary cellular automata. Based on the self-similarities of their orbits, we write down the numbers of nonzero states in the spatial and spatio-temporal patterns and obtain discontinuous Riemann integrable functions by normalizing the values. We calculate the integrals of the two obtained functions over $ [0, 1] $ and demonstrate the relationship between them. [ABSTRACT FROM AUTHOR]

Cornerstones of the Cellular Automaton Interpretation of Quantum Mechanics are its ontological states that evolve by permutations, in this way never creating would-be quantum mechanical superposition states. We review and illustrate this with a classical Ising spin chain. It is shown that it can be related to the Weyl equation in the continuum limit. Yet, the model of discrete spins or bits unavoidably becomes a model of qubits by generating superpositions, if only slightly deformed. We study modifications of its signal velocity which, however, do not relate to mass terms. To incorporate the latter, we consider the Dirac equation in 1+1 dimensions and sketch an underlying discrete deterministic "necklace of necklaces" automaton that qualifies as ontological. [ABSTRACT FROM AUTHOR]

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice. [ABSTRACT FROM AUTHOR]

Hot flow forming (HFF) is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs (CPLIRs) made of magnesium (Mg) alloys, which has wide applications in the aerospace field. However, due to the thermo-mechanical coupling effect and the existence of stiffened structure, complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall (CW) and inner rib (IR) of Mg alloy thin-walled CPLIRs during the HFF. In this paper, a modified cellular automaton (CA) model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network (ANN) method. It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model. Furthermore, the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis (FEA). The results show that compared with the microstructure at the same layer of the IR, more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW; various differences of strain and strain rate in the wall-thickness exist between the CW and IR, which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer; the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results.

The cellular automaton-lattice Boltzmann method-immersed moving boundary coupled model is established to study the microstructure evolution during the solidification process of 2009Al/SiCp. After several model benchmarks, the natural convection of liquid phase and its effect on particle settlement under different pulling velocities were studied firstly. The results showed that at higher pulling velocity, the particle decelerates downward before contacting with dendrite front due to intensified natural convection. At lower pulling velocity, the particle decelerates gradually until a change in direction and upward movement. As the particle approach the tip of the dendrite, the particle experiences a brief upward movement before contacting the dendritic front. Then the convection induced by particles has an impact on the distribution of solutes in the liquid phase. It has been observed that at lower pulling velocity, there is a significant difference in solute distribution at the solidification front. The primary reason for this phenomenon is the higher solute concentration near the solidification front at lower pulling velocity, rather than the higher liquid phase flowing rate. Finally, our model investigated the microstructure evolution process during the directional solidification process of 2009Al/SiCp at different pulling velocities. The results indicate a good agreement between the simulation results and experiment observations.

Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions, leading to potential conflicts among natural and human-related issues as well as among stakeholders. This study introduced economic standards for farmers. A hybrid approach (CA-ABM) of cellular automaton (CA) and an agent-based model (ABM) was developed to effectively deal with social and land-use synergic issues to examine human–environment interactions and projections of land-use conversions for a humid basin in south China. Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change. The major modules of the CA-ABM are initialization, migration, assets, land suitability, and land-use change decisions. Empirical estimates of the factors influencing the urban land-use conversion probability were captured using parameters based on a spatial logistic regression (SLR) model. Simultaneously, multicriteria evaluation (MCE) and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion. An agent-based CA-SLR-MCE-Markov (ABCSMM) land-use conversion model was proposed to explore the impacts of policies on land-use conversion. This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services. The results demonstrated land-use simulations under multi-policy scenarios, revealing the usefulness of the model for normative research on land-use management.

• A modified cellular automaton (CA) model considering the effects of deformation conditions on material parameters was developed using artificial neural network (ANN). • Microstructure evolution of Mg alloy cylindrical part with longitudinal inner ribs (CPLIRs) during hot flow forming was well described by coupling ANN-modified CA and finite element. • Microstructure inhomogeneity along the wall-thickness of Mg alloy CPLIRs is dominated by differences in strain and strain rate between the cylindrical wall and inner rib. Hot flow forming (HFF) is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs (CPLIRs) made of magnesium (Mg) alloys, which has wide applications in the aerospace field. However, due to the thermo-mechanical coupling effect and the existence of stiffened structure, complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall (CW) and inner rib (IR) of Mg alloy thin-walled CPLIRs during the HFF. In this paper, a modified cellular automaton (CA) model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network (ANN) method. It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model. Furthermore, the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis (FEA). The results show that compared with the microstructure at the same layer of the IR, more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW; various differences of strain and strain rate in the wall-thickness exist between the CW and IR, which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer; the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results. [ABSTRACT FROM AUTHOR]

In this study, a visualized microstructure evolution model for the primary shear zone during NiTi smart alloy machining was established by integrating an analytical cutting model with a cellular automaton method. Experimental verification was conducted using an invented electromagnet rotation-type quick-stop device. The flow stress curve during the dynamic recrystallization of the NiTi smart alloy, the influence of relevant parameters on the dynamic recrystallization process, and the distribution of dynamic recrystallization in the primary shear zone were studied via the model. The simulation results showed that strain rate and deformation temperature significantly affect the relevant parameters during the dynamic recrystallization process. Three typical shear planes were selected for a comparison between simulation results and experimental results, with a minimum error of 3.76% and a maximum error of 11.26%, demonstrating that the model accurately simulates the microstructure evolution of the NiTi smart alloy during the cutting process. These results contribute theoretical and experimental insights into understanding the cutting mechanism of the NiTi smart alloy. [ABSTRACT FROM AUTHOR]

The impacts of merging on mixed traffic flow in the highway on-ramp zone are numerically investigated in this paper. An on-ramp mixed traffic flow merging model is proposed. Our model focuses on the on-ramp merging zone and considers a mixed traffic flow comprising of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Firstly, we establish the Velocity-Modified Comfortable Driving (V-MCD) model for HDVs, which incorporates a velocity estimation module into the base Modified Comfortable Driving (MCD) model. The Cooperative Adaptive Cruise Control (CACC) model developed by PATH Labs is used for CAVs. A cooperative merging model has been developed to capture the interaction behavior of vehicles in various driving scenarios. Merging rules are established based on vehicle type and merging order. The merging rules for HDV-HDV are based on safety gap. Meanwhile, those for CAV-HDV are based on velocity difference, and for CAV-CAV are based on first-come-first-served. We conduct numerical simulation experiments to derive the fundamental diagram of the mixed flow and analyze the interaction between vehicles on the on-ramp and mainline. The simulation results indicate that the V-MCD model can effectively reflect on the hysteresis effect caused by on-ramp merging vehicles in real traffic scenarios. The increase in CAV penetration improves road traffic efficiency in terms of both traffic velocity and capacity. However, as the penetration of CAVs continues to rise, their positive impact will diminish. The sensitivity analysis of the hysteresis effect at both micro and macro levels can enhance our understanding of the impacts on traffic flow. As the hysteresis effect increases, merging has a greater impact on mainline upstream traffic, leading to reduce headway and more erratic velocity changes. [ABSTRACT FROM AUTHOR]

The selective orientation of grains during laser powder bed fusion (L-PBF) leads to anisotropy in mechanical properties. The current understanding of the evolution between the different morphologies of the grains is still limited. The present work is aimed at revealing the grain evolution process through the development of a coupled discrete elemental method-computational fluid dynamics-metacellular automata model. The results show that most of the grains observed through characterization experiments are irregularly shaped columnar grains and exhibit anisotropy in mechanical property tests. The grains observed in the simulations can be generalized into four types: V-shaped grain, slanted grain, vertical grain, and equiaxial grain. During the overlapping of interlayer and intertrack, some of the slanted grains are retained and others grow into V-shaped grains. In addition, some of the V-shaped grains coarsen during remelting and resolidification and subsume the slanted grains in the direction in which they grow, eventually developing into coarse vertical grain. Therefore, an appropriate increase in hatch distance is beneficial to reduce anisotropy. This study contributes to an in-depth understanding of grain transformation during remelting and resolidification and guides the design of specific microstructures. [ABSTRACT FROM AUTHOR]

This study explored whether a policy with maximum velocity limitation prior to each jam point in a traffic flow system can mitigate jam situations in a highway. To quantify this question, a new cellular automata traffic model based on the Revised S-NFS model was established. We perused two specific scenarios: a jam naturally brought by a high traffic density, and one brought by a lane-closed section placed as an explicit bottleneck. The result of multi-agent simulation (MAS) reveals that such maximum velocity limitation policy can certainly mitigate jam situations when each jam is naturally bought up not by a bottleneck, as proved by several statistics, and cannot improve the traffic flux. [ABSTRACT FROM AUTHOR]

The modeling of blind people's behavior is an important topic in pedestrian evacuation dynamics. In order to study movement characteristics of blind people in evacuation process and the effect of indoor obstacles on the evacuation efficiency of blind people, an extended cellular automata model is proposed to simulate blind people evacuation by considering the influence of obstacles. The control variable method is adopted to study the effect of indoor obstacles on the blind evacuation efficiency rule. The numerical simulation is applied to analyze the evacuation process of blind people under different setting (e.g. the location, size and other factors) of obstacles and different evacuation scenarios. The shunt effect of obstacles is discussed and the layout strategy of obstacles is optimized. The results show that the longitudinal obstacles in front of the exit can effectively play the diversion role of obstacles when the obstacle is at a certain distance from the exit, which will improve the evacuation efficiency of the blind. Furthermore, a horizontal obstacle of appropriate length can constrain the trajectory of the blind people and improve the efficiency of evacuation, but when the horizontal obstacle is too long, it will induce extra jam on both sides of the obstacle and hinder the evacuation of the blind. The finding can provide the theoretical basis for the blind people's organization, management and the optimization of evacuation strategies during an emergency. [ABSTRACT FROM AUTHOR]

In this study, an integrated crystal plasticity and cellular automaton (CA) model has been developed and employed to investigate the processes of cold rolling and annealing in ferritic stainless steel. Crystal plasticity simulates the deformation microstructure, deformation texture and corresponding dislocation density distribution in the cold rolled ferritic stainless steel. Meanwhile, CA predicts the microstructural evolution originating from nucleation and growth. The resolution of the crystal plasticity model has been improved using a newly developed remeshing technique, which allows for the capture of the grain substructure in the deformation microstructure. The developed model has been validated by experimental measurements with electron backscatter diffraction (EBSD) in terms of comparing the orientation distribution functions (ODFs), misorientation, local substructures, and grain size. The good agreement between simulations and experiments indicates that the developed crystal plasticity with CA model is reliable and efficient for predicting the microstructure and texture evolutions during cold rolling and subsequent annealing.

Science is currently becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines, ranging from cosmology to physics, chemistry, biochemistry, and biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively parallel, self-organized, emergent, error-resilient computational environments?” The thesis is solely studied on cellular automata. Initially, an overview of the basic building blocks enabling us to reach this end goal is provided. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python, cellular automata software GoL-N24. A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. Together, these pose the crucial question of whether there is something deeper beyond the Turing machine theoretical description of massively parallel computing. The perspective, future directions, including applications in robotics and biology of this research, are discussed in the light of known information.

ABSTRACTCoarse – and fine-grained bimodal-structures in a Al-Mg base alloy with rare earth elements of Sc/Zr is produced due to the ultrafast nonequilibrium solidification occurs in laser-induced molten pools during laser powder bed fusion (LPBF) additive manufacturing. A novel high-fidelity cellular automaton (CA) algorithm incorporating numerical calculations of molt-pool temperature fields elucidates the formation and evolution of the bimodal-structure. Subsequent heat treatment induces precipitation of Al3(Sc/Zr) particles within the grains, synergistically enhancing strength and plasticity of the LPBF-processed alloy. The crystal plastic finite element method (CPFEM) is used to reveal the synergistic effect between the strength and plasticity during the material tensile procedure. The bimodal-structure exhibits good fatigue resistance but intriguing anisotropy under low stress cyclic loading. It is proved that differentiated distribution patterns relative to the principal stress direction of the bimodal-structure have a significant influence on its fatigue performance. Numerical evolutionary of the bimodal grain deformation reflects this phenomenon.

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exploration of the relationship between process parameters and grain evolution during the thermal deformation of rare-earth magnesium alloys using simulation software has significant implications for enhancing research and development efficiency and advancing the large-scale engineering application of high-performance rare-earth magnesium alloys. Through single-pass hot compression experiments, this study obtained high-temperature flow stress curves for rare-earth magnesium alloys, analyzing the variation patterns of these curves and the softening mechanism of the materials. Drawing on physical metallurgical theories, such as the evolution of dislocation density during dynamic recrystallization, recrystallization nucleation, and grain growth, the authors of this paper establish a cellular automaton model to simulate the dynamic recrystallization process by tracking the sole internal variable—the evolution of dislocation density within cells. This model was developed through the secondary development of the DEFORM-3D finite element software. The results indicate that the model established in this study accurately simulates the evolution process of grain growth during heat treatment and the dynamic recrystallization microstructure during the thermal deformation of rare-earth magnesium alloys. The simulated results align well with relevant theories and metallographic experimental results, enabling the simulation of the dynamic recrystallization microstructure and grain size prediction during the deformation process of rare-earth magnesium alloys. [ABSTRACT FROM AUTHOR]

In this study, we employed our previously developed data mining method to show that a thermodynamic state shift occurred preceding the 2011 Mw 9 East Japan Earthquake (GEJE), coinciding with the onset of crustal stress manifestations. Our discussion starts with the insights obtained from our prior research, which revealed that small ground vibration fluctuations (GVF) detected near the epicenter of the GEJE are thermodynamically equivalent to a cellular automaton (CA). This equivalence allowed us to consider the "Process to GEJE," defined as the transition from GVF to catastrophic rupture in the GEJE, as analogous to the nonequilibrium-to-equilibrium thermodynamic transition in the CA. Therefore, if we find a thermodynamic state shift in the CA, it is reasonable to assume that a similar thermodynamic state shift may exist in the "Process to GEJE." We successfully derived a thermodynamic state function that exhibits a significant change before the nonequilibrium-to-equilibrium thermodynamic transition in the CA. By evaluating this thermodynamic state function using the GVF data of the "Process to GEJE," we discovered its maximum value occurring on 2007-10-28 at 10:30:00 JST, 1231 days before the GEJE occurred on 2011-03-11 at 14:46 JST. This maximum value can be considered as an indication of a thermodynamic state shift prior to the GEJE. Furthermore, near the timing of the thermodynamic state shift, it has been observed that the GVF state approaches thermodynamic equilibrium and a sudden increase in crustal stress was first noted on 2007-11-22 at 18:02:44 JST. [ABSTRACT FROM AUTHOR]

In this paper, we prove that there is a strongly universal cellular automaton on the heptagrid with five states under the relaxation of the assumption of rotation invariance for the rules. The result is different from that of a previous paper of the author with six states but with rotationally invariant rules. Here, the structures is more constrained than in the quoted paper with six states and rotation invariance of the rules. [ABSTRACT FROM AUTHOR]

The evolution of microstructure during hot deformation is key to achieving good mechanical properties in aluminum alloys. We have developed a cellular automaton (CA) based model to simulate the microstructural evolution in 7075 aluminum alloy during hot deformation. Isothermal compression tests were conducted to obtain material parameters for 7075 aluminum alloy, leading to the establishment of models for dislocation density, nucleation of recrystallized grains, and grain growth. Integrating these aspects with grain topological deformation, our CA model effectively predicts flow stress, dynamic recrystallization (DRX) volume fraction, and average grain size under diverse deformation conditions. A systematic comparison was made between electron back scattered diffraction (EBSD) maps and CA model simulated under different deformation temperatures (573 to 723 K), strain rates (0.001 to 1 s−1), and strain amounts (30% to 70%). These analyses indicate that large strain, high temperature, and low strain rate facilitate dynamic recrystallization and grain refinement. The results from the CA model show good accuracy and predictive capability, with experimental error within 10%. [ABSTRACT FROM AUTHOR]

The object of research is the processes of planning the minimum operating costs of a vessel with minimal risk to it and its cargo, considering the forecasted hydrometeorological conditions. The aim is to increase the fuel efficiency of a vessel’s passage, considering the forecast of weather conditions when forming an optimal safe route in the e-Navigation system. To achieve the research goal, conventional cellular automata and the mathematical apparatus of fuzzy sets and fuzzy logic were used in the process of decision-making and assessment of the impact of weather conditions on traffic efficiency. The devised approach makes it possible to synthesize an optimal route for the vessel, which ensures minimum fuel consumption and has minimal risk for the vessel and cargo while considering variable hydrometeorological conditions along the route. Minimization of operating costs is achieved through the ability of cellular automata to describe the complex behavior of objects, considering local rules. Automata are a computing system in discrete spaces. Data uncertainty has led to the need to use a fuzzy system, the effectiveness of which depends on the quality and accuracy of rules. Fuzzy automata, by combining fuzzy logic and automata theory, made it possible to process continuous steps and model the inherent uncertainty. To determine the state of cells of a fuzzy cellular automaton and the transition function between them, a system of productive rules and membership functions was used. It is the consistency of the system of productive rules when using fuzzy logic to build a cellular automaton that enables the construction of a quasiglobal optimal routing method in comparison with conventional methods for calculating the ship’s route. [ABSTRACT FROM AUTHOR]

Mean dimension may decrease after taking the natural extension. In this paper we show that mean dimension is preserved by natural extension for an endomorphism on a compact metrizable abelian group. As an application, we obtain that the mean dimension of an algebraic cellular automaton coincides with the mean dimension of its natural extension, which strengthens a result of Burguet and Shi [Israel J. Math. (to appear).] with a different proof. [ABSTRACT FROM AUTHOR]

Science is currently becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines, ranging from cosmology to physics, chemistry, biochemistry, and biology. This leads us to the main motivation and simultaneously to the central thesis of this review: "Can we design artificial, massively parallel, self-organized, emergent, error-resilient computational environments?" The thesis is solely studied on cellular automata. Initially, an overview of the basic building blocks enabling us to reach this end goal is provided. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python, cellular automata software GoL-N24. A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. Together, these pose the crucial question of whether there is something deeper beyond the Turing machine theoretical description of massively parallel computing. The perspective, future directions, including applications in robotics and biology of this research, are discussed in the light of known information. [ABSTRACT FROM AUTHOR]

Engine components, particularly exhaust valves, are manufactured by a specific forming process known as electric upsetting, which contributes to microstructures with finer grains which enhance the performance of these components. To this end, it is necessarily required to describe the grain evolution behaviors of heat-resistant alloys during the electrical-thermal-mechanical coupling forming process. The grain evolution behaviors of Ni80A superalloy including dynamic recrystallization (DRX) kinetics models and mesoscale cellular automaton (CA) model are solved from the acquired stress–strain curves in resistance heating isothermal compression tests. Following which, a multi-field and multi-scale coupling FEM model is established by embedding the multi-scale grain evolution models into the electrical-thermal-mechanical multi-field coupling compression model. Then the grain evolution behaviors in isothermal compressions with processing conditions are reproduced by simulations. The results reveal that during the isothermal compressions, recrystallized grains intend to bulge and generate around the original grain boundaries, representing as 'necklace' microstructures inhomogeneously distributed in the matrix, and then such grain morphology is gradually replaced by refined and equiaxed DRX grains. For a fixed strain, grain size decreases with decreasing temperature and increasing strain rate. Finally, the EBSD characterization results show that the developed FEM model can well describe the grain evolution behaviors of Ni80A superalloy in electrical-thermal-mechanical coupling compression process. [ABSTRACT FROM AUTHOR]

Cancer is a disease characterized by the uncontrolled growth of abnormal cells in the body. It is a complex and multifaceted disease that has challenged researchers and doctors for decades. The ability to visualize and understand tumor growth can provide valuable insights into how cancer develops and spreads, leading to significant improvements in cancer diagnosis, treatment, and prevention. The three-dimensional tumor growth simulation tool that is being developed is an important step in this direction. It allows for detailed visualization of tumor growth in different parts of the human body, which can provide valuable insights into how cancer develops and spreads. Additionally, the ability of this tool to simulate tumor growth in different parts of the body means that it can be used to study a wide range of cancer types. This tool utilizes a cellular automaton and a small-world network to create connections between cells, allowing for a more accurate representation of organ and tumor structures. Furthermore, it allows for the loading of configurations and parameters from external files, providing great flexibility to the tool and allowing for customization of the simulation to the specific needs of each case. For 3D rendering, the Marching Cubes technique is used, which enables detailed and accurate three-dimensional representation of tumors.

The use of a laser with a Gaussian-beam profile is frequently adopted in attempts of crystallizing nonsingle-crystal thin films; however, it merely results in the formation of polycrystal thin films. In this paper, selective area crystallization of nonsingle-crystal copper(II) oxide (CuO) is described. The crystallization is induced by laser, laser-induced crystallization, with a beam profile in the shape of a chevron. The crystallization is verified by the exhibition of a transition from a nonsingle-crystal phase consisting of small (∼100 nm × 100 nm) grains of CuO to a single-crystal phase of copper(I) oxide (Cu2O). The transition is identified by electron back scattering diffraction and Raman spectroscopy, which clearly suggests that a single-crystal domain of Cu2O with a size as large as 5 μm × 1 mm develops. The transition may embrace several distinctive scenarios such as a large number of crystallites that densely form grow independently and merge, and simultaneously, solid-state growth that takes place as the merging proceeds reduce the number of grain boundaries and/or a small number of selected crystallites that sparsely form grow laterally, naturally limiting the number of grain boundaries. The volume fraction of the single-crystal domain prepared under the optimized conditions─the ratio of the volume of the single-crystal domain to that of the total volume within which energy carried by the laser is deposited─is estimated to be 32%. Provided these experimental findings, a theoretical assessment based on a cellular automaton model, with the behaviors of localized recrystallization and stochastic nucleation, is developed. The theoretical assessment can qualitatively describe the laser beam geometry-dependence of vital observable features (e.g., size and gross geometry of grains) in the laser-induced crystallization. The theoretical assessment predicts that differences in resulting crystallinity, either single-crystal or polycrystal, primarily depend on a geometrical profile with which melting of nonsingle-crystal regions takes place along the laser scan direction; concave-trailing profiles yield larger grains, which lead to a single crystal, while convex-trailing profiles result in smaller grains, which lead to a polycrystal, casting light on the fundamental question Why does a chevron-beam profile succeed in producing a single crystal while a Gaussian-beam profile fails?

With the growing size of cities and the need to renew residential areas, architects are calling for more efficient use of already settled areas. This paper presents a performance-driven architectural design (PDAD) workflow for shape generation and genetic optimization based on environmental data, using public housing in the Singapore region as a case study. It integrates three-dimensional cellular automata, parametric performance simulation, genetic optimization algorithms, and hierarchical clustering algorithms. The results show that the average value of Useful Daylight Illuminance (UDI) is 85.19%, the average value of Energy Use Intensity (EUI) is 159.41 kWh/m2, and the average value of Predicted Mean Vote (PMV) is 0.64 for the optimal set of solutions produced after genetic optimization. The optimal solution set was further classified into 4 categories by a hierarchical clustering algorithm and was visualized for further evaluation and selection by the architect. The study helps architects to integrate data analysis results with human decision-making as a design research method in the early stages of design and leads to further discussion on bottom-up design approaches in the urban renewal process.

The latest advances in additive manufacturing have given rise to an increasing interest in additively built lattice structures due to their superior properties compared to foams and honeycombs. The foundation of these superior properties is a tailored microstructure, which is difficult to achieve in additive manufacturing because of the variety of process parameters influencing the quality of the final part. This work presents the numerical prediction of the resulting grain structure of a lattice structure additively built by electron beam powder bed fusion. A thermal finite-difference model is coupled to a sophisticated cellular automaton-based crystal growth model, including nucleation. Numerically predicted grain structures, considering different nucleation conditions, are compared with experimentally derived EBSD measurements. The comparison reveals that nucleation is important, especially in fine lattice structures. The developed software, utilizing the nucleation model, is finally able to predict the as-built grain structure in lattice structures. [ABSTRACT FROM AUTHOR]

The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth. [ABSTRACT FROM AUTHOR]

Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in this paper to predict landslide hazards at a local scale. The proposed model not only extracts spatial scene features for computations but also achieves dynamic prediction, which means that only one input is needed to obtain continuous predictions. Experiments were performed in Lushan, Sichuan, China. After using a convolutional neural network (CNN) to obtain the initial static landslide hazard zoning results, the landslide hazard zoning results for 2016–2025 were predicted with the SCBR-CA model. For comparison, a CA combined with a CNN (CNN-CA), was introduced. The area under the curve (AUC) of the receiver operating characteristic curve and Moran's I index were used to assess the performance of the model. The experimental results showed that SCBR-CA yields slightly better AUC and Moran's I index values than CNN-CA, and the dynamically predicted landslide hazard zoning results are equivalent or superior to those of static zoning, which indicates that the SCBR-CA model effectively predict local landslide hazards. [ABSTRACT FROM AUTHOR]

In this paper, we construct a weakly universal cellular automaton in the heptagrid, the tessellation $ \{7,3\} $ { 7 , 3 } which takes place in the hyperbolic plane. The cellular automation is not rotation invariant but which is truly planar. This result, under these conditions, cannot be improved for the tessellations $ \{p,3\} $ { p , 3 } of the hyperbolic plane. [ABSTRACT FROM AUTHOR]

Simple Summary: Approximately 50% of patients diagnosed with cancer receive radiotherapy at least once during their disease. Experiments with sophisticated in-cellulo assays to improve radiotherapeutic outcomes are still challenging, and some critical details of tumor cell dynamics still need to be explored. To enhance the informative value of such approaches and support future therapeutic study designs, we developed an efficient mathematical model for three-dimensional multicellular tumor spheroids, which reflect microregions within a large tumor or avascular micrometastases and which are an auspicious experimental framework to pre-assess the curative effect of radio(chemo)therapy. We validate our mathematical model using experimental tumor spheroid growth data of several cell lines with and without radiotherapy and observe equal or better performance than previous models. Moreover, our model allows for efficient parameter calibration within previously reported and/or physiologically reasonable ranges. Based on this data-driven approach, we can explain the mechanism of the characteristic dynamics at small tumor volumes. Understanding the complex dynamics of tumor growth to develop more efficient therapeutic strategies is one of the most challenging problems in biomedicine. Three-dimensional (3D) tumor spheroids, reflecting avascular microregions within a tumor, are an advanced in vitro model system to assess the curative effect of combinatorial radio(chemo)therapy. Tumor spheroids exhibit particular crucial pathophysiological characteristics such as a radial oxygen gradient that critically affect the sensitivity of the malignant cell population to treatment. However, spheroid experiments remain laborious, and determining long-term radio(chemo)therapy outcomes is challenging. Mathematical models of spheroid dynamics have the potential to enhance the informative value of experimental data, and can support study design; however, they typically face one of two limitations: while non-spatial models are computationally cheap, they lack the spatial resolution to predict oxygen-dependent radioresponse, whereas models that describe spatial cell dynamics are computationally expensive and often heavily parameterized, impeding the required calibration to experimental data. Here, we present an effectively one-dimensional mathematical model based on the cell dynamics within and across radial spheres which fully incorporates the 3D dynamics of tumor spheroids by exploiting their approximate rotational symmetry. We demonstrate that this radial-shell (RS) model reproduces experimental spheroid growth curves of several cell lines with and without radiotherapy, showing equal or better performance than published models such as 3D agent-based models. Notably, the RS model is sufficiently efficient to enable multi-parametric optimization within previously reported and/or physiologically reasonable ranges based on experimental data. Analysis of the model reveals that the characteristic change of dynamics observed in experiments at small spheroid volume originates from the spatial scale of cell interactions. Based on the calibrated parameters, we predict the spheroid volumes at which this behavior should be observable. Finally, we demonstrate how the generic parameterization of the model allows direct parameter transfer to 3D agent-based models. [ABSTRACT FROM AUTHOR]

To explore companion movement laws of different age groups and transfer rules of panic emotion during evacuation at subway stations, evacuation groups of different ages were distinguished in this study through differences in movement rate. Moreover, a double-exit subway cellular automaton evacuation model considering companion behaviours was constructed. A case study of evacuation of dense crowds at the Hukou Subway in Wuhan City of China was simulated, and effects of personnel structure, companion behaviours, and panic emotion on evacuation efficiency were analysed. Results show that, (1) the overall crowd evacuation time present a linear growth trend with the increase of pedestrian density. (2) The evacuation time initially decreases and then increases with the increase of companion ratio. The shortest evacuation time is achieved when the companion cells account for 10 %. (3) People are more reasonable and the general evacuation efficiency is higher if they can control emotions better during evacuation. The obtained conclusions provide great references to analyse companion behaviours of different age groups during evacuation. [ABSTRACT FROM AUTHOR]

On the basis of developed individual-based model and empirical data the importance of anemochory for the dispersal of giant hogweed mericarpia over long distances (up to 55 m from the parent plant) was proved. The use of cellular automate for modelling of the plant population area increase allowed to reveal the influence of weather condition, number and spatial distribution of hogweed plants in the period of the beginning of introduction on the rate of invasion. Verification of the results of the cellular automaton work based on satellite images and field observations showed a significant level of compliance of theoretical calculations and observed results. The logistic functions parameters describing the change in the area of giant invasive hogweed stands were determined. A retrospective analysis of satellite images of model plots, starting from the 1990s of the 20th century, showed an annual increase in the area of hogweed populations by 20% in the exponential population growth phase. A significant variability in the rate of invasion (from 5 to 70% per year) depended on the initial conditions and stage of invasion, usage modes and ecological capacity of the available sites. [ABSTRACT FROM AUTHOR]

This work aims to utilize Internet of Things (IoT) technology and the Artificial Neural Network - Cellular Automaton (ANN-CA) model to analyze the construction of indicators for territorial spatial planning and urban development suitability assessment. Firstly, the IoT technology is introduced, and its application potential in land planning is explored. Using the IoT technology, various data related to land use are collected, and these data are transmitted and summarized through IoT equipment to form a data base. Based on the collected data, the ANN-CA model and the “dual assessment” concept are employed to establish an indicator system for urban development suitability assessment, encompassing permanent basic farmland, ecological redlines, and current built-up areas. Through the combination of these two models, the future land use situation can be predicted more accurately. The trained model is evaluated, including simulation accuracy, error analysis, Kappa coefficient and other indicators. Compared with the actual data, the accuracy and credibility of the model are verified. Finally, according to the prediction results of the model, the land use situation is analyzed and interpreted to provide decision support for urban planning and development. The research results show that the combination of IoT technology and ANN-CA model can effectively analyze the evolution law of land use and the suitability of urban development. Through the reasonable setting and processing of model parameters and data, people can get high accuracy land use prediction results, which provides important reference and support for urban planning and sustainable development. The suitability for urban development within County M exhibits noticeable spatial disparities, with the central region being more suitable for development while the peripheral regions are relatively less favorable. This work provides valuable guidance for decision-makers and researchers in the field of territorial planning, and promotes orderly urban development and sustainable prosperity.

A two-dimensional (2-D) multi-phase cellular automaton (CA)-finite difference (FD)-lattice Boltzmann (LB) coupled model is developed to simulate the hydrogen pore formation during dendritic and eutectic solidification of hypoeutectic Al-Si alloys. In this model, the dendrite and eutectic structures are simulated using a CA technique. The solute diffusion is solved by the FD method. The nucleation, growth, and motion behavior of gas pore, and hydrogen transport are simulated by a LB Shen-Chen scheme. It is found that the pores can grow fully but compete in the primary dendrite growth stage. Conversely, pores newly nucleated in residual inter-dendrite liquid during the eutectic solidification stage grow restrictedly and independently. Pore shape severely deformed by the compression of the surrounding solid structures. Moreover, the effects of the initial Si composition, initial hydrogen concentration and cooling rate on the pore formation are qualitatively and quantitatively studied. It is found that the initial Si composition significantly impacts on the pore size and distribution, but little effect on the porosity percentage which decreases markedly with the decreasing initial hydrogen concentration and increasing cooling rate. Comparison reveals that the simulated microstructure morphological characteristics and the predicted porosity percentage agree well with the experimental observation reported in the literature.

The 1500 mm-wide AZ31 magnesium alloy plate is an important material for the molding of large shell components.In the industrial rolling process, the wider the plate, the more uneven the temperature and stress distribution are in the width direction during the industrial rolling process, which results in poor mechanical properties and an uneven microstructure. In this paper, the mechanical properties and microstructure evolution along the width direction during a single-pass rolling process (6–3 mm) of this plate were qualitatively and quantitatively investigated. Additionally, a cellular automaton (CA) model was used to reproduce the grain distribution along the width direction. The result shows that there is a significant difference in the elongation of different parts of the plates along the wide direction, and the elongation of the center part is significantly higher than the edge, which is related to the uneven microstructure distribution. The average grain size from the edge to the center shows a tendency of increase and then decrease, and the degree of grain refinement has increased. The number of twins increased in the edge part, showing a typical mixed grain state with poor unevenness. Further CA shows that the average grain size and percentage of dynamic recrystallization increase and then decrease from the center to the edge. The parts 0–250 mm and 625–700 mm from the edge are fine-grained (average grain size less than 1 μm) and account for about 46 % of the plate. These parts are preferred for the subsequent further processing of rolled plates.

Selective Laser Melting (SLM) is an efficient and advanced manufacturing technology that utilizes lasers to melt and solidify metal or alloy powders, layer by layer, to form the desired object shape. This study aims to investigate the evolution behavior of the solidification microstructure during the SLM process and presents a novel numerical simulation method based on the Smoothed Particle Hydrodynamics-Cellular Automaton (SPH-CA) coupling model. The SPH-CA coupling model combines the particle information, such as position and temperature, from Smoothed Particle Hydrodynamics (SPH) with the Cellular Automaton (CA) method in a one-way coupling manner, enabling simultaneous computation of multiple physical fields, including heat transfer, melting, solidification, and morphology. This model addresses challenges that are difficult to handle in grid-based computations, such as free surface variations, boundary deformations, interfacial motion, and large deformations. Additionally, it effectively simulates the morphology of the solidification microstructure. Furthermore, the coupling process performs kernel function weighted calculations at the same scale, which results in higher accuracy compared to the grid-based multiscale mapping. The simulation results demonstrate that different processing parameters influence the morphology of the molten pool and the thermal undercooling, thereby affecting the growth characteristics of the grains. A higher laser scanning speed leads to smaller molten pool size, finer grain sizes in the solidification microstructure, larger laser grain angles, and a higher content of columnar grains. These findings are consistent with experimental observations and existing conclusions, providing evidence for the feasibility and effectiveness of the proposed model in the context of SLM.

In this paper, the flow behavior and evolution of microstructure during the hot forging process are predicted using numerical simulation, providing a reference for optimizing the process. The flow stress-strain curve of 30Cr2Ni3MoV steel was obtained by conducting hot compression experiments using Gleeble-3500 thermal simulator. The constitutive and dynamic recrystallization (DRX) cellular automaton (CA) models were established by analyzing the thermal deformation behavior, considering work hardening and DRX softening. This model was optimized by combining the dislocation density difference drive mechanism, increasing the total number of grain orientations, and then implementing Hash mapping to obtain the actual grain orientation. The simulation results of strain and strain rate in the process of thermal deformation were obtained through the constitutive model. Compared with the results of the hot compression experiment and the metallographic experiment, the accuracy of the simulation results of the DRX CA model was verified. Finally, the real-time updating module of deformation parameters is established, and the real-time process parameters of deformation calculation are updated to the CA model. The simulation results are consistent with the experimental results through metallographic experiments.

The addition of Li to Al alloys produces such benefits as a 6% increase of modulus and a 3% weight reduction upon adding 1 wt% Li, and yet it has led to difficulties for manufacturing due to the highly active Li and 25 times equilibrium [H] concentration in the liquid at elevated temperatures. In this study, the 3D porosity morphology was quantified using the X-ray computed tomography (X-CT) from both sand gravity and vacuum castings. A cellular automaton model has been adopted to predict porosity distribution as a function of equilibrium hydrogen concentration and thermal boundary conditions. Combining experimental and simulation results, it was found that the mechanical properties of vacuum casting Al–Li alloy have been improved significantly due to the reduction of hydrogen porosity. The prediction of porosity as a function of hydrogen levels and cooling conditions agrees well with experiments, and porosity has been found to decrease Young's modulus and initiating cracks in Al–Li alloys.