日本地球惑星科学連合2021年大会

講演情報

[E] 口頭発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM10] Frontiers in solar physics

2021年6月6日(日) 10:45 〜 12:14 Ch.06 (Zoom会場06)

コンビーナ:横山 央明(東京大学大学院理学系研究科)、今田 晋亮(名古屋大学宇宙地球環境研究所)、鳥海 森(宇宙航空研究開発機構 宇宙科学研究所)、Sterling Alphonse(NASA/MSFC)、座長:横山 央明(東京大学大学院理学系研究科)

11:59 〜 12:14

[PEM10-11] Cross-disciplinary study on heating, transport, and turbulence dynamics in solar/astrophysical and fusion/laboratory plasmas: SoLaBo-X

*仲田 資季1、石川 遼太郎4、勝川 行雄2、政田 洋平5、小林 達哉1、庄田 宗人2、今田 晋亮3、鳥海 森6、永岡 賢一1、SoLaBo-X project1,2 (1.自然科学研究機構 核融合科学研究所、2.自然科学研究機構 国立天文台、3.名古屋大学 宇宙地球環境研究所、4.総合研究大学院大学、5.愛知教育大学、6.国立研究開発法人宇宙航空研究開発機構 宇宙科学研究所)

キーワード:乱流、太陽プラズマ、核融合プラズマ、分野融合研究

A cross-disciplinary research activity for solar/astrophysical and fusion/laboratory plasmas “SoLaBo-X (Solar + Laboratory + Cross-disciplinary)” is presented. Multi-scale interactions and the energy transfer processes among the microscopic fluctuations and the macroscopic structures are key ingredients to elucidate long-standing issues, e.g., solar coronal heating, solar wind accelerations, and turbulence suppressions by spontaneously generated zonal flows in fusion plasmas. Beyond large difference of the mean plasma parameters, our joint-study project focuses on the turbulence dynamics and associated heating and transport processes in solar and magnetically confined plasmas. Two major directions have been specified, i.e., (1)Energy transfer analyses based on the higher order turbulence correlations in multi-component turbulent fields, and (2)Deep-learning-assisted fast and accurate evaluations/predictions of the spatio-temporal structures in the turbulent fields. In these directions, several research topics have been addressed: (i)Visualization experiments and the image analysis of electrically-driven turbulent convections in relevance to the dynamics in the solar tachocline, (ii)Statistical analysis and modeling of entropy-gradient-driven(local) and cooling-driven(nonlocal) turbulence in the solar convection zone, (iii)Multi-scale convolutional neural network modeling for the fast predictions of hardly-observable turbulence fields(e.g., horizontal velocity and/or magnetic fields, etc. ) from the observables(e.g., continuum intensity and/or vertical velocity, etc. ), and (iv)Extended modeling of the electron parallel energy transport and the experimental verification in a linear plasma device with the direct measurement of the electron energy distribution function.