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ORIGINAL RESEARCH article

Front. Phys.
Sec. Fusion Plasma Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1476618

Key Feature Identification of Internal Kink Mode Using Machine Learning

Provisionally accepted
Hongwei Ning Hongwei Ning 1Shuyong Lou Shuyong Lou 2Jianguo Wu Jianguo Wu 1Teng Zhou Teng Zhou 3*
  • 1 Anhui University, Hefei, Anhui Province, China
  • 2 Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu Province, China
  • 3 Hainan University, Haikou, Hainan Province, China

The final, formatted version of the article will be published soon.

    The internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP). We conduct an in-depth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices.

    Keywords: feature importance1, internal kink mode2, Random Forest3, XGBoost4, permutation5, SHAP6

    Received: 06 Aug 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Ning, Lou, Wu and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Teng Zhou, Hainan University, Haikou, 570228, Hainan Province, China

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