Multi-track, Multi-layer Dendrite Growth and Solid Phase Transformation Analysis during Additive Manufacturing of H13 Tool Steel Using a Combined Hybrid Cellular Automata/Phase Field and Solid-state Phase Prediction Models

Using an efficient hybrid Cellular Automata/Phase Field (CA-PF) dendrite growth modeling in combination with a solid phase transformation model, microstructure evolution and solid-phase transformation were predicted during laser direct deposition (LDD) of H13 tool steel powder across multiple tracks and layers. Temperature and surface geometry data were provided by a comprehensive physics-based laser deposition model. The computational efficiency of the CA-PF model allows for simulating domains large enough to capture dendrite growth across an entire molten pool and into neighboring LDD tracks. The microstructure of the target track is strongly affected by heat from neighboring tracks including re-melting and re-solidification, and martensite tempering. Dendrite size and growth direction across the entire fusion zone, as well as predicted hardness values, are found to be in good agreement with experimental results.

Modeling and Analysis of Cardiac Hybrid Cellular Automata via GPU-Accelerated Monte Carlo Simulation

The heart consists of a complex network of billions of cells. Under physiological conditions, cardiac cells propagate electrical signals in space, generating the heartbeat in a synchronous and coordinated manner. When such a synchronization fails, life-threatening events can arise. The inherent complexity of the underlying nonlinear dynamics and the large number of biological components involved make the modeling and the analysis of electrophysiological properties in cardiac tissue still an open challenge. We consider here a Hybrid Cellular Automata (HCA) approach modeling the cardiac cell-cell membrane resistance with a free variable. We show that the modeling approach can reproduce important and complex spatiotemporal properties paving the ground for promising future applications. We show how GPU-based technology can considerably accelerate the simulation and the analysis. Furthermore, we study the cardiac behavior within a unidimensional domain considering inhomogeneous resistance and we perform a Monte Carlo analysis to evaluate our approach.

Methods and Models for the Formation of Pseudo-Random Number Sequences Based on Cellular Automata

The chapter describes well-known models and implementation options for pseudorandom number generators based on cellular automata. Pseudorandom number generators based on synchronous and asynchronous cellular automata are briefly reviewed. Pseudorandom number generators based on one-dimensional and two-dimensional cellular automata, as well as using hybrid cellular automata, are described. New structures of pseudorandom number generators based on asynchronous cellular automata with a variable number of active cells are proposed. Testing of the proposed generators was carried out, which showed the high quality of the generators. Testing was conducted using graphical and statistical tests.

COVID-19 Hotspot Trend Prediction Using Hybrid Cellular Automata in India

The coronavirus disease 2019 (COVID-19) is an infectious disease identified at Wuhan, China, in December 2019 caused by new Coronavirus. The Indian government has taken many initiatives to mitigate the effect of COVID by encouraging the standard mechanisms of social distancing, the use of masks, and various safety parameters. COVID-19 hotspot identifies regions in India where COVID-19 severity is very high. We propose a novel hybrid cellular automata classifier for predicting the trend of various Hotspots in India, processing different parameters including infection control, virus reproduction rate, critical correlation, safety parameters, and social distancing. The proposed classifier was named Hybrid Cellular Automata-Hotspot (HCA-HS), predicts the number of hotspots in various districts of states, and also gives the status of each city marked either as Totally Safe or Marginally Safe or Unsafe. This will alert the state authorities to take necessary action to mitigate the COVID effect and help the people for possibly refraining from going to the infected areas, i.e., hotspots. The data sets were collected from Kaggle and the local Indian database for more adaptability. The accuracy of the predictions of Hotspots is reported as 91.58%, which is considerable at this moment. The developed classifier is compared with Support Vector Mechanism (SVM), K-Means, Decision Tree, and HCA-HS has reported an accuracy of 10.69% higher than the existing literature.

Study of phase transformations in complex phase steel using a mesoscale cellular automaton model Part I: Modeling Fundamentals

A two-dimensional mesoscale model based on the concept of hybrid cellular automata is developed to study phase transformations in a complex phase steel during continuous cooling. The model is capable of simulating microstructure evolution with carbon diffusion in the volume and along grain boundaries, γ/α interfaces migration into austenite, as well as formation of bainite and martensite islands during intensive cooling in lower temperatures. In contrast to the classic statistical approaches which are based on the assumption of modeling one point in the material with homogeneous microstructure, the proposed phase transformations’ model in the mesoscale accounts for material heterogeneity. The simulation results in the form of a digital material representation with microstructures and maps showing the carbon concentration field as well as microhardness distribution are presented. One of the main advantages of the model is that has only seven adjustment coefficients that are used in the fitting process.

Optimal hydropower operation of multi-reservoir systems: hybrid cellular automata-simulated annealing approach

Hydropower operation of multi-reservoir systems is very difficult to solve mostly due to their nonlinear, nonconvex and large-scale nature. While conventional methods are long known to be incapable of solving these types of problems, evolutionary algorithms are shown to successfully handle the complexity of these problems at the expense of very large computational cost, particularly when population-based methods are used. A novel hybrid cellular automata-simulated annealing (CA-SA) method is proposed in this study which avoids the shortcomings of the existing conventional and evolutionary methods for the optimal hydropower operation of multi-reservoir systems. The start and the end instances of time at each operation period is considered as the CA cells with the reservoir storages at these instances are taken as the cell state which leads to a cell neighborhood defined by the two adjacent periods. The local updating rule of the proposed CA is derived by projecting the objective function and the constraints of the original problem on the cell neighborhoods represented by an optimization sub-problem with the number of decision variables equal to the number of reservoirs in the system. These sub-problems are subsequently solved by a modified simulated annealing approach to finding the updated values of the cell states. Once all the cells are covered, the cell states are updated and the process is iterated until the convergence is achieved. The proposed method is first used for hydropower operation of two well-known benchmark problems, namely the well-known four- and ten-reservoir problems. The results are compared with the existing results obtained from cellular automata. Genetic algorithm and particle swarm optimization indicating that the proposed method is much more efficient than existing algorithms. The proposed method is then applied for long-term hydropower operation of a real-world three-reservoir system in the USA, and the results are presented and compared with the existing results.

A Novel Cellular Automata Classifier for COVID-19 Prediction

Introduction: China has witnessed a new virus Corona,which is named COVID-19. It has become the world’s most concern as this virus has spread over the worldat a higher speed;the world has witnessed more than one lakh cases and one thousand deaths in a span of few days. Methods: We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the datasets from Kaggle and other standard websites. Results: The proposed classifier, hybrid non-linear cellular automata (HNLCA), was trained with 23,078 datasets and tested with 6785 datasets. HNLCA is compared with conventional methods of long short-term memory, AdaBoost, support vector machine, regression, and SVR and has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc.