New Neural Network-Assisted Code Enhances Tokamak Microturbulence Simulations

Researchers have developed a new neural network-assisted Global Gyrokinetic Code using Cylindrical Coordinates (G2C3) to study electrostatic microturbulence in tokamak geometries. The team, including Jaya Kumar Alageshan, Joydeep Das, Tajinder Singh, Sarveshwar Sharma, and Animesh Kuley, aimed to address computational complexities in existing codes that use flux coordinates. These complexities arise due to the anisotropy from confinement magnetic fields and the mathematical singularity on the magnetic separatrix surface.

G2C3 employs a cylindrical coordinate system for particle dynamics, allowing particle motion in arbitrarily shaped flux surfaces, including the magnetic separatrix of the tokamak. The code uses a hybrid scheme combining a neural network and an iterative local search algorithm for charge deposition and field interpolation. This approach enhances the efficiency of particle locating and speeds up subroutines related to gathering and scattering operations in gyrokinetic simulations.

The researchers verified the new code's capability through self-consistent simulations of linear ion temperature gradient modes in the core region of the DIII-D tokamak. The results indicate that G2C3 can effectively handle the challenges posed by realistic tokamak geometries, potentially leading to more accurate and efficient simulations of plasma behavior in fusion reactors.

This development is significant for the field of plasma physics as it offers a more robust tool for understanding and predicting microturbulence in fusion devices. Improved simulations can contribute to the optimization of tokamak designs and the advancement of fusion energy research.

For more details, refer to the paper titled 'Neural network assisted electrostatic global gyrokinetic toroidal code using cylindrical coordinates' by Jaya Kumar Alageshan, Joydeep Das, Tajinder Singh, Sarveshwar Sharma, and Animesh Kuley, available on arXiv https://arxiv.org/abs/2408.12851.