2D Grain Growth Process Simulation

In our simulation of two-dimensional grain growth, we implemented a cellular automata approach with a 500x500 lattice grid. The core algorithm employs the Monte Carlo Potts model, where each cell represents a discrete grain orientation. Key implementation features include neighbor interaction rules (typically 8-neighborhood) and probabilistic state transition functions based on energy minimization principles. We incorporated variable simulation parameters such as temperature (affecting transition probabilities) and pressure conditions through modified boundary handling algorithms. The visualization module tracks grain boundary evolution using phase field methods, enabling observation of complex growth behaviors including grain coalescence and boundary migration. These material science phenomena were analyzed through statistical measurements of grain size distribution and boundary energy calculations. Our simulation framework provides valuable insights into grain growth mechanisms, with applications in materials optimization and manufacturing process control through parametric studies and sensitivity analysis.