Fusion Concepts for the Future

Chen, Francis F.

doi:10.1007/978-1-4419-7820-2_10

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…The ultimate goal of scientific and engineering research in the field of nuclear fusion is to build a power plant producing sustainable and clean electricity through fusion reactions from a confined plasma heated up to ~ 100 million degrees Celsius 1 , 2 . A tokamak is a torus-shape vacuum vessel within which the plasma is confined by magnetic fields directed along the long (toroidal) and short (poloidal) ways around the torus.…”

Section: Introductionmentioning

confidence: 99%

GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation

Joung,

Ghim,

Kim

et al. 2023

Sci Rep

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The force-balanced state of magnetically confined plasmas heated up to 100 million degrees Celsius must be sustained long enough to achieve a burning-plasma state, such as in the case of ITER, a fusion reactor that promises a net energy gain. This force balance between the Lorentz force and the pressure gradient force, known as a plasma equilibrium, can be theoretically portrayed together with Maxwell’s equations as plasmas are collections of charged particles. Nevertheless, identifying the plasma equilibrium in real time is challenging owing to its free-boundary and ill-posed conditions, which conventionally involves iterative numerical approach with a certain degree of subjective human decisions such as including or excluding certain magnetic measurements to achieve numerical convergence on the solution as well as to avoid unphysical solutions. Here, we introduce GS-DeepNet, which learns plasma equilibria through solely unsupervised learning, without using traditional numerical algorithms. GS-DeepNet includes two neural networks and teaches itself. One neural network generates a possible candidate of an equilibrium following Maxwell’s equations and is taught by the other network satisfying the force balance under the equilibrium. Measurements constrain both networks. Our GS-DeepNet achieves reliable equilibria with uncertainties in contrast with existing methods, leading to possible better control of fusion-grade plasmas.

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Section: Introductionmentioning

confidence: 99%