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Performance prediction applying different reduced turbulence models to the SMART tokamak

D.J. Cruz-Zabala, M. Podestà, F. Poli, S.M. Kaye, M. Garcia-Munoz, E. Viezzer, J.W. Berkery2024年12月Nuclear FusionIF 3出版社

The SMall Aspect Ratio Tokamak (SMART) is currently being commissioned at the University of Seville and will be able to compare the performance of positive and negative triangularity plasmas at low aspect ratio. Predictive simulations have been performed for different machine scenarios and heating schemes using the TRANSP code. The objectives of these simulations are to predict the parameters expected in positive triangularity plasmas, to guide diagnostic development, and to validate transport models. Several reduced turbulence models have been used to predict electron and ion temperatures for the operational phase 2. All models provide similar results from approximately mid-radius to the separatrix but important discrepancies are found in the core region. These positive triangularity results are compared with experiments from a similar size machine like GLOBUS-M2. The multi-mode model (MMM) shows the best agreement. Simulations with different boundary conditions have been performed and no strong differences have been observed between them. The impact of neutral beam injection (NBI) on the predicted profiles has also been addressed. Rotation reduces turbulence levels so higher temperatures are achieved when included in the simulations. Studying the different contributions to the thermal diffusivities, it is observed that electron temperature gradient (ETG) turbulence dominates at the plasma core while micro-tearing modes (MTM) dominate at the edge in the electron channel. In the ion channel, the neoclassical contribution is dominant at the core and at the very edge while the Weiland component, which includes ion temperature gradient mode (ITG), trapped electron mode (TEM), kinetic ballooning mode (KBM), peeling mode (PM) and collisionless and collision dominated magnetohydrodynamic (MHD) modes governs the mid-radius region. For phase 3, two plasmas with different electron densities have been studied. The case with lower density matches well a specific discharge of GLOBUS-M2. The higher density plasma shows high performance with .

日本語訳

SMall Aspect Ratio Tokamak (SMART)は現在セビリア大学でコミッショニングが行われており、低アスペクト比における正および負の三角形度プラズマの性能を比較することが可能になる。TRANSPコードを用いて、異なる装置シナリオおよび加熱方式に対する予測シミュレーションが実施された。これらのシミュレーションの目的は、正三角形度プラズマにおいて期待されるパラメータを予測し、診断開発を導き、輸送モデルを検証することである。運転フェーズ2の電子温度とイオン温度を予測するために、いくつかの簡約乱流モデルが使用された。すべてのモデルは、おおよそ中間半径からセパラトリクスまで類似した結果を与えるが、コア領域では重要な不一致が見られる。これらの正三角形度の結果は、GLOBUS-M2のような類似サイズの装置における実験と比較される。マルチモードモデル(MMM)が最も良い一致を示す。異なる境界条件でのシミュレーションが実施され、それらの間には強い差異は観測されなかった。予測されたプロファイルに対する中性粒子ビーム入射(NBI

AIによる論文要約

スマート核融合装置における異なる簡略化乱流モデルの性能予測
JAこの論文は、核融合プラズマの性能予測手法に興味のある研究者や学生に有益です。特に、簡略化乱流モデルの適用性や、プラズマ物理現象の理解を深めたい人に向けて書かれています。#PlasmaPerformancePrediction #ReducedTurbulenceModels #SmallAspectRatioTokamak
LLM向け: {'Title': 'Performance prediction applying different reduced turbulence models t…

この論文では、スペイン・セビリア大学で建設中のスマート核融合装置の性能予測シミュレーションについて報告しています。複数の簡略化乱流モデルを使用して、正三角形プラズマの電子・イオン温度プロファイルを予測しました。モデルによって中心部の予測に差があるものの、全体的な傾向は似ており、特に多モードモデルが実験結果と最も良く一致しています。また、中性粒子入射の影響や、コア部のETG乱流、エッジのMTM乱流の寄与についても分析しています。

Performance prediction applying different reduced turbulence models to the SMART tokamak
ENThis paper would be of interest to fusion researchers and engineers working on tokamak design and performance prediction, as well as students in the field of plasma physics and fusion energy.#SmallAspectRatioTokamak #TurbulenceModeling #PerformancePrediction #FusionResearch
LLM向け: {'Title': 'Performance prediction applying different reduced turbulence models t…

This paper presents simulations of the SMART tokamak, a new low-aspect ratio device, using various turbulence models to predict its performance. The results show good agreement with experiments from a similar machine, GLOBUS-M2, especially using the multi-mode model. The study examines the contributions of different turbulence modes and the impact of neutral beam injection on the predicted profiles.

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