A Novel Scientific Model for Enhancing Magnetic Confinement Efficiency in Fusion Reactors

This study introduces a novel scientific framework aimed at improving magnetic confinement efficiency in nuclear fusion reactors. By integrating magnetohydrodynamic (MHD) stability theory with adaptive neural network control algorithms, the proposed model offers enhanced real-time plasma confinement under dynamic conditions. Numerical simulations based on modified Grad-Shafranov equations and Lyapunov stability criteria demonstrate a substantial improvement in energy confinement time (τ_E) by approximately 𝜏45.7%, and a reduction in plasma instabilities by nearly 47%, compared to conventional PID-based control models. The model leverages Python-based simulation using open-source fusion modeling tools and presents new possibilities for hybrid AI-assisted fusion systems. While the results are promising, experimental validation in tokamak-scale devices such as ITER or Wendelstein 7-X is essential. This research contributes to the evolving landscape of fusion energy and proposes a path toward robust plasma control through advanced computational techniques.