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In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential…

等离子体物理 · 物理学 2024-03-05 Zefang Liu , Weston M. Stacey

The dynamics of burning plasmas in tokamaks are crucial for advancing controlled thermonuclear fusion. This study applies the NeuralPlasmaODE, a multi-region multi-timescale transport model, to simulate the complex energy transfer processes…

等离子体物理 · 物理学 2024-12-13 Zefang Liu , Weston M. Stacey

While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards…

等离子体物理 · 物理学 2023-11-01 Allen M. Wang , Darren T. Garnier , Cristina Rea

The Tokamak device is the most promising candidate for producing sustainable electric power by nuclear fusion. It is a torus-shaped device that confines plasma by a strong magnetic field. The development, design and control of the design…

等离子体物理 · 物理学 2022-11-17 M. Holst , V. Kungurtsev , S. Mukherjee

A challenging and fundamental research problem is the better understanding and control of the turbulent transport of heat in present-day tokamak fusion experiments. Recent developments in numerical methods along with enormous gains in…

等离子体物理 · 物理学 2008-02-03 Jeremy Kepner , Scott Parker , Viktor Decyk

In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage…

等离子体物理 · 物理学 2024-08-15 Ekin Öztürk , Rob Akers , Stanislas Pamela , The MAST Team , Pieter Peers , Abhijeet Ghosh

We present a generalized multinodal model for simulating particle and energy transport in toroidal plasma configurations, developed to support burning plasma analysis and reactor-scale modeling. Unlike fixed-node models, this formulation…

等离子体物理 · 物理学 2025-07-21 Zefang Liu , Weston M. Stacey

A self-consistent model is presented for the simulation of a multi-component plasma in the tokamak boundary. A deuterium plasma is considered, with the plasma species that include electrons, deuterium atomic ions and deuterium molecular…

等离子体物理 · 物理学 2022-04-13 A. Coroado , P. Ricci

Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential…

等离子体物理 · 物理学 2025-07-15 Zefang Liu , Weston M. Stacey

The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develops a reinforcement learning approach to this problem by…

等离子体物理 · 物理学 2024-02-15 Allen M. Wang , Oswin So , Charles Dawson , Darren T. Garnier , Cristina Rea , Chuchu Fan

This paper studies the decay of an objective functional using a new control technique within Pontryagin's framework. Convergence analysis is carried out on the infinite-dimensional space of Tokamak plasma dynamical state as described by…

最优化与控制 · 数学 2025-11-10 Slim Jmal , Matteo Tacchi-Bénard , Emmanuel Witrant

In this paper, we consider an open-loop, finite-time, optimal control problem of attaining a specific desired current profile during the ramp-up phase by finding the best open-loop actuator input trajectories. Average density, total power,…

等离子体物理 · 物理学 2017-03-08 Zhigang Ren , Chao Xu , Yongsheng Ou

Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and accurately allow us to quickly iterate over design…

Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state $\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time $T$. Recent advances…

机器学习 · 计算机科学 2022-09-20 Lucas Böttcher , Thomas Asikis

A lumped parameter model for tokamak plasma current and inductance time evolution as function of plasma resistance, non-inductive current drive sources and boundary voltage or poloidal field (PF) coil current drive is presented. The model…

等离子体物理 · 物理学 2011-03-28 J. A. Romero , JET-EFDA Contributors

A discrete Boltzmann model (DBM) for plasma kinetics is proposed. The constructing of DBM mainly considers two aspects. The first is to build a physical model with sufficient physical functions before simulation. The second is to present…

等离子体物理 · 物理学 2024-04-16 Jiahui Song , Aiguo Xu , Long Miao , Feng Chen , Zhipeng Liu , Lifeng Wang , Ningfei Wang , Xiao Hou

The most promising concepts for power and particle control in tokamaks and other fusion experiments rely upon atomic processes to transfer the power and momentum from the edge plasma to the plasma chamber walls. This places a new emphasis…

plasm-ph · 物理学 2009-10-28 D. E. Post

In this work, we provide an overview of various control strategies aimed at steering plasma toward desired configurations using an external magnetic field. From a modeling perspective, we focus on the Vlasov equation in a two-dimensional…

数值分析 · 数学 2026-04-06 Federica Ferrarese

The density limit is one of the major obstacles to achieving the desired fusion performance in tokamaks. However, the underlying physics mechanism for its recently observed power dependence in experiments has not been well understood or…

等离子体物理 · 物理学 2025-02-20 Jiaxing Liu , Ping Zhu , Dominique Franck Escande

Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability to forecast the spatio-temporal evolution of plasma field…

等离子体物理 · 物理学 2023-02-14 Vignesh Gopakumar , Stanislas Pamela , Lorenzo Zanisi , Zongyi Li , Anima Anandkumar , MAST Team
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