Performance Analysis of AutomataScales to Support Early Design Decisions

Abstract In this paper, we present a comprehensive study and performance analysis on the AutomataScales simulations method focusing on electric propulsion systems for deep space missions. These applications require precise and time efficient simulations. However, traditional simulation methods such as Particle-In-Cell (PIC) method facing challenges from computationally intensive (2.5–21 days), memory demands (random-access memory or RAM and CPU), and steep learning curve for researchers. These limitations reduce their effectiveness in resource-constrained environments. For instance, each GB of RAM consumes approximately 0.1875 watts which resulting in more power consumption ranging from 87.1 to 145.2 MW per simulation run. The AutomataScales method combines discretization techniques with cellular automata and a multi-layer, multi-resolution approaches. This method offers a powerful tool to model complex multiphysics interactions and utilizing hybrid numerical scheme (discrete and continuous) with lower computational time and memory usage. The method depicts intricate and accurate behaviors in various types of particle trajectory (ionized particles, primary and secondary electrons) and plasma physics (particle collision and ionization). It provides a scalable and adaptable framework for multiphysics simulations with almost real-time simulation (0.1 second per time step). A key aspect of our research is the computational efficiency of AutomataScales. Our results show that the method can achieve up to 36.9 times faster, and 2.1 times less physical memory (RAM) compared to commercial simulation tools such as COMSOL Multiphysics® software. This substantial reduction in computational resources make AutomataScales more efficient and accessible for researchers to explore broader design variables in their early design process with or without computational constraints.