Conveners
Machine Learning
- Didier Mazon (CEA Cadarache)
- Joshua Stillerman (MIT Plasma Science and Fusion Center)
Machine Learning
- Joshua Stillerman (MIT Plasma Science and Fusion Center)
- Didier Mazon (CEA Cadarache)
Machine Learning
- Didier Mazon (CEA Cadarache)
- Joshua Stillerman (MIT Plasma Science and Fusion Center)
Robust control of tokamak plasma is still one of the most challenges for a fusion reactor due to the complicated plasma dynamics together with its response with complicated structures and actuators and the extreme control requirements. In recent years, artificial intelligence showed its great potential in predication of plasma states and in control of plasma equilibrium. On EAST, all disrupted...
Plasma with elongated configuration has the advantage of higher discharge parameters while at the cost of vertical displacement instability. Once the vertical displacement is out of control, it will inevitably lead to a major disruption, causing great damage to the device, which will have unacceptable consequences if it occurs on ITER. Therefore, active control of vertical displacement is...
- William Tang
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- Heading
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##The US goal (March, 2022) to deliver a Fusion Pilot Plant [1] has underscored urgency for accelerating the fusion energy development timeline. This will rely heavily on validated scientific and engineering advances driven by HPC together with advanced statistical methods featuring artificial intelligence/deep learning/machine...
The reliability of the plasma density measurement is crucial for plasma density
control in tokamak. Currently, the density feedback system in EAST uses lineaveraged density from either the Hydrogen Cyanide (HCN) laser interferometer
or the polarimeter-interferometer (POINT) diagnostic system. However, insufficient laser energy or noise interference can lead to erroneous density...
The traditional algorithm currently used for plasma equilibrium reconstruction in tokamaks assumes a plasma current profile in a certain polynomial form (usually 2nd or 3rd order) or a tension spline function and performs a least square fitting to the diagnostic data under the model given by the Grad-Shafranov equation 1. The physics-informed neural network (PINN) integrates measurement data...
In Tokamak plasma, The instability of magnetohydrodynamic(MHD) severely limits the improvement of plasma parameters and may even lead to plasma disruption events, thereby threatening the safety of device components. The identification of MHD modes is crucial for the study and control of MHD instabilities.
Traditional MHD mode recognition methods mostly use the raw information of...
High-performance disruption prediction and instability event identification are crucial for tokamak plasma operation. Given the intrinsic correlation between plasma disruptions and their precursor instability events, this study introduces a multi-task learning-based integrated model that concurrently processes both tasks. The model identifies three key instability events—Edge Localized Modes...
The MDSplus[1][2] data management system is widely used in the magnetic fusion energy research community for data storage, management, and remote access. The system provides data access through a vector based, interpreter API. It was developed and optimized for rapid single shot analyses. Machine Learning applications require data from large numbers of shots and potentially from different...
Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, future tokamaks require disruption prediction from the first shot, posing challenges of data scarcity...
During long pulse steady-state discharge, the position and shape of plasma reconstructed by the EFIT code may produce significant errors due to factors such as integrator drift and local magnetic field changes, which in turn affect discharge stability. However, optical based boundary reconstruction signals are not affected by the complex electromagnetic environment within the Tokamak. The use...
