Neural network assisted electrostatic global gyrokinetic toroidal code using cylindrical coordinates
Jaya Kumar Alageshan, Joydeep Das, Tajinder Singh, Sarveshwar Sharma,, Animesh Kuley

TL;DR
This paper introduces G2C3, a neural network-assisted gyrokinetic simulation code using cylindrical coordinates to effectively model microturbulence in tokamaks, overcoming flux coordinate singularities.
Contribution
G2C3 is the first gyrokinetic code employing cylindrical coordinates combined with neural networks for efficient and accurate turbulence simulation in realistic tokamak geometries.
Findings
Successfully simulated ion temperature gradient modes in DIII-D tokamak
Demonstrated improved computational efficiency with neural network acceleration
Validated the ability to handle complex flux surface geometries
Abstract
Gyrokinetic simulation codes are used to understand the microturbulence in the linear and nonlinear regimes of the tokamak and stellarator core. The codes that use flux coordinates to reduce computational complexities introduced by the anisotropy due to the presence of confinement magnetic fields encounter a mathematical singularity of the metric on the magnetic separatrix surface. To overcome this constraint, we develop a neural network-assisted Global Gyrokinetic Code using Cylindrical Coordinates (G2C3) to study the electrostatic microturbulence in realistic tokamak geometries. In particular, G2C3 uses a cylindrical coordinate system for particle dynamics, which allows particle motion in arbitrarily shaped flux surfaces, including the magnetic separatrix of the tokamak. We use an efficient particle locating hybrid scheme, which uses a neural network and iterative local search…
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Taxonomy
TopicsInertial Sensor and Navigation · Advanced Computational Techniques and Applications · Fuzzy Logic and Control Systems
