Machine learning of turbulent transport in fusion plasmas with neural network

Hui LI (李慧); Yanlin FU (付艳林); Jiquan LI (李继全); Zhengxiong WANG (王正汹)

doi:10.1088/2058-6272/ac15ec

Plasma Science and Technology > 2021 > 23(11): 115102. > DOI: 10.1088/2058-6272/ac15ec

Hui LI (李慧), Yanlin FU (付艳林), Jiquan LI (李继全), Zhengxiong WANG (王正汹). Machine learning of turbulent transport in fusion plasmas with neural network[J]. Plasma Science and Technology, 2021, 23(11): 115102. DOI: 10.1088/2058-6272/ac15ec

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Machine learning of turbulent transport in fusion plasmas with neural network

¹ Key Laboratory of Materials Modification by Laser, Ion, and Electron Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, People's Republic of China
² Southwestern Institute of Physics, Chengdu 610041, People's Republic of China
³ School of Physics, Dalian University of Technology, Dalian 116024, People's Republic of China

Funds: This work was supported by the National Key R&D Program of China (Nos. 2017YFE0301200 and 2017YFE0301201) and partially by National Natural Science Foundation of China (Nos. 11775069 and 11925501), the Fundamental Research Funds for the Central Universities (No. DUT21GJ205) as well as the Liao Ning Revitalization Talents Program (No. XLYC1802009).

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Received Date: April 07, 2021
Revised Date: July 09, 2021
Accepted Date: July 18, 2021

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Abstract

Abstract

Turbulent transport resulting from drift waves, typically, the ion temperature gradient (ITG) mode and trapped electron mode (TEM), is of great significance in magnetic confinement fusion. It is also well known that turbulence simulation is a challenging issue in both the complex physical model and huge CPU cost as well as long computation time. In this work, a credible turbulence transport prediction model, extended fluid code (ExFC-NN), based on a neural network (NN) approach is established using simulation data by performing an ExFC, in which multi-scale multi-mode fluctuations, such as ITG and TEM turbulence are involved. Results show that the characteristics of turbulent transport can be successfully predicted including the type of dominant turbulence and the radial averaged fluxes under any set of local gradient parameters. Furthermore, a global NN model can well reproduce the radial profiles of turbulence perturbation intensities and fluxes much faster than existing codes. A large number of comparative predictions show that the newly constructed NN model can realize rapid experimental analysis and provide reference data for experimental parameter design in the future.
- neural network,
- plasma turbulence,
- transport properties,
- plasma simulation

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Machine learning of turbulent transport in fusion plasmas with neural network