| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2025-12-01 |
| タイトル |
|
|
タイトル |
Zero-dimensional modeling of drift wave turbulence by using Bayesian regression |
|
言語 |
en |
| 著者 |
Sasaki, M
Mototake, Y
Kobayashi, T
Kin, F
Yatomi, G
Kawachi, Y
|
| アクセス権 |
|
|
アクセス権 |
embargoed access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_f1cf |
| 権利 |
|
|
権利情報 |
This is the Accepted Manuscript version of an article accepted for publication in [Plasma Physics and Controlled Fusion]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at [https://doi.org/10.1088/1361-6587/adff31] |
|
言語 |
en |
| 権利 |
|
|
権利情報 |
“This Accepted Manuscript is available for reuse under a CC BY-NC-ND licence after the 12 month embargo period provided that all the terms of the licence are adhered to” |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
We present a Bayesian model selection framework to identify reduced dynamical equations describing the evolution of volume-integrated turbulence energy in magnetically confined plasmas. Using data from two-dimensional Hasegawa–Wakatani simulations with varying adiabatic parameters, we consider a candidate model with polynomial nonlinearities up to ninth order and a coupling term to zonal flow energy. All combinations of these terms are evaluated based on Bayesian model evidence. The optimal model includes only linear growth, quadratic nonlinear saturation, and zonal flow suppression. While subcritical turbulence was not observed in the present dataset, the selection framework successfully excluded redundant higher-order terms, confirming its robustness. Crucially, this approach enables data-driven identification of nonlinear saturation and turbulence-zonal flow coupling, which are often introduced heuristically in conventional models. These results suggest that the proposed framework can serve as a useful tool for constructing interpretable reduced models of plasma turbulence and for examining key mechanisms involved in nonlinear energy regulation. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
IOP publishing |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1088/1361-6587/adff31 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0741-3335 |
| 書誌情報 |
en : Plasma Physics and Controlled Fusion
巻 67,
号 9,
p. 095011,
発行日 2025-09-08
|
| ファイル公開日 |
|
|
日付 |
2026-09-08 |
|
日付タイプ |
Available |
Bayesian_modelling_ver3.pdf (419 KB)
