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  1. 原著論文

Quasilinear turbulent particle and heat transport modelling with a neural-network- based approach founded on gyrokinetic calculations and experimental data

https://repo.qst.go.jp/records/84925
https://repo.qst.go.jp/records/84925
69cb758a-b1dc-454b-ae71-dd612ff805f4
Item type 学術雑誌論文 / Journal Article(1)
公開日 2021-12-28
タイトル
タイトル Quasilinear turbulent particle and heat transport modelling with a neural-network- based approach founded on gyrokinetic calculations and experimental data
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
アクセス権
アクセス権 metadata only access
アクセス権URI http://purl.org/coar/access_right/c_14cb
著者 Emi, Narita

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Emi, Narita

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Mitsuru, Honda

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Nakata, M.

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Maiko, Yoshida

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Nobuhiko, Hayashi

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Emi, Narita

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Mitsuru, Honda

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Maiko, Yoshida

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Nobuhiko, Hayashi

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抄録
内容記述タイプ Abstract
内容記述 A novel quasilinear turbulent transport model DeKANIS has been constructed founded on the gyrokinetic analysis of JT-60U plasmas. DeKANIS predicts particle and heat fluxes fast with a neural network (NN) based approach and distinguishes diffusive and non-diffusive transport processes. The original model only considered particle transport, but its capability has been extended to cover multi-channel turbulent transport. To solve a set of particle and heat transport equations stably in integrated codes with DeKANIS, the NN model embedded in DeKANIS has been modified. DeKANIS originally determined turbulent saturation levels semi-empirically based on JT-60U experimental data, but now it can also estimate them using a theory-based saturation rule. The new saturation model is still partly connected to experimental data, but it offers the potential for applying DeKANIS independently of the device.
書誌情報 Nuclear Fusion

巻 61, 号 11, p. 116041, 発行日 2021-10
出版者
出版者 IOP Publishing
ISSN
収録物識別子タイプ ISSN
収録物識別子 0029-5515
DOI
識別子タイプ DOI
関連識別子 10.1088/1741-4326/ac25be
関連サイト
識別子タイプ URI
関連識別子 https://iopscience.iop.org/article/10.1088/1741-4326/ac25be/meta
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