Computer-Aided Engineering (CAE) systems face significant challenges in modeling complex, nonlinear, and discrete engineering phenomena. Traditional numerical methods based on partial differential equations struggle to accurately simulate systems with intricate spatial dynamics and stochastic behaviors.
This research introduces a novel methodology for integrating Cellular Automata (CA) models with existing CAE platforms, specifically focusing on converting data representations between COMSOL Multiphysics and the CATLIB cellular automata library. The study addresses critical interoperability limitations by developing a specialized converter tool that enables seamless data translation between different computational paradigms. Utilizing COMSOL’s Java API and the Python MPh library, the research demonstrates a robust approach to extracting and transforming complex geometrical and physical simulation data. The proposed solution aims to enhance the accessibility of cellular automata modeling techniques for engineers and researchers with varying levels of technical expertise.
Key contributions include a framework for comparative analysis between traditional CAE solutions and cellular automata models, a methodology for bridging rule-based and equation-based solvers, and a proof-of-concept implementation that showcases the potential of integrating alternative modeling approaches. The research successfully extracts critical simulation parameters, including geometric boundaries, mesh configurations, and initial conditions, from COMSOL models for subsequent CA processing. Experimental validation using a turbulence formation simulation demonstrated the converter’s capability to handle complex engineering scenarios. The methodology not only addresses current limitations in computational modeling but also provides a foundation for more flexible and comprehensive approaches to engineering-physics simulations. This work represents a significant advancement in computational modeling techniques, offering a standardized approach to integrating cellular automata models into mainstream engineering simulation platforms. The research has broader implications for interdisciplinary innovation, potentially transforming how engineers and researchers approach the modeling of complex, dynamic systems.