Modeling of transport phenomena in tokamak plasmas with neural networks

Meneghini, Orso; Luna, Christopher J.; Smith, Sterling P.; Lao, Lang L.

doi:10.1063/1.4885343

Title: Modeling of transport phenomena in tokamak plasmas with neural networks

Journal Article · Mon Jun 23 00:00:00 EDT 2014 · Physics of Plasmas

DOI:https://doi.org/10.1063/1.4885343· OSTI ID:1354832

Meneghini, Orso ^[1]; Luna, Christopher J. ^[2]; Smith, Sterling P. ^[3]; Lao, Lang L. ^[3]

Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)
Arizona State Univ., Phoenix, AZ (United States)
General Atomics, San Diego, CA (United States)

A new transport model that uses neural networks (NNs) to yield electron and ion heat ux pro les has been developed. Given a set of local dimensionless plasma parameters similar to the ones that the highest delity models use, the NN model is able to efficiently and accurately predict the ion and electron heat transport pro les. As a benchmark, a NN was built, trained, and tested on data from the 2012 and 2013 DIII-D experimental campaigns. It is found that NN can capture the experimental behavior over the majority of the plasma radius and across a broad range of plasma regimes. Although each radial location is calculated independently from the others, the heat ux pro les are smooth, suggesting that the solution found by the NN is a smooth function of the local input parameters. This result supports the evidence of a well-de ned, non-stochastic relationship between the input parameters and the experimentally measured transport uxes. Finally, the numerical efficiency of this method, requiring only a few CPU-μs per data point, makes it ideal for scenario development simulations and real-time plasma control.

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Research Organization:: General Atomics, San Diego, CA (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: FG02-95ER54309

OSTI ID:: 1354832

Journal Information:: Physics of Plasmas, Vol. 21, Issue 6; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 14 works

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Real-time capable first principle based modelling of tokamak turbulent transport Citrin, J.; Breton, S.; Felici, F. Nuclear Fusion, Vol. 55, Issue 9 https://doi.org/10.1088/0029-5515/55/9/092001	journal	July 2015
Machine-learning assisted steady-state profile predictions using global optimization techniques Honda, M.; Narita, E. Physics of Plasmas, Vol. 26, Issue 10 https://doi.org/10.1063/1.5117846	journal	October 2019
Validation of nonlinear gyrokinetic transport models using turbulence measurements White, A. E. Journal of Plasma Physics, Vol. 85, Issue 1 https://doi.org/10.1017/s0022377818001253	journal	February 2019
Real-time capable first principle based modelling of tokamak turbulent transport Citrin, J.; Breton, S.; Felici, F. arXiv https://doi.org/10.48550/arxiv.1502.07402	text	January 2015
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