In response to the increasing intricacy of plasma behaviors within magnetically confined fusion systems, we propose a comprehensive approach leveraging interpretable artificial intelligence (AI) to enhance modeling, system reduction, and diagnostic analysis. The presented framework integrates a range of machine learning methods designed to uphold both physical insight and computational...
Extracting net energy from fusion reactors will require a profound understanding of the underlying physics and the development of efficient control strategies. Plasma diagnostics are essential to these efforts, but obtaining accurate information from their measurements often involves solving quite delicate data analysis problems. Regrettably, many of the current approaches rely on simplifying...
Recently the huge amounts of data collected in modern large experiments have motivated the deployment of machine learning (ML) in physics. Unfortunately, ML models are typically black boxes almost impossible to interpret. Consequently, in the last years eXplainable Artificial Intelligence, whose objective consists of improving the transparency of ML tools, has received a lot of attention....
Disruptions are catastrophic forms of collapse that have affected all tokamak devices and are therefore one of the main potential showstoppers on the route to a commercial reactor. A new approach to proximity detection has been developed. It allows determining both the probability of and the time interval remaining before an incoming disruption. The methodology has been implemented with...
The approaching initial operation of major new tokomaks is rendering more pressing the need for effective disruption prediction techniques. The required tools should be not only accurate but also capable of operating with a minimum number of signals, because in the first campaigns of new devices typically only a very limited number of diagnostics is available. In addition, a very limited...
With a view to reliability, availability, maintainability, and inspectability (RAMI) for DEMO, achieving an availability of 30-60\% while minimizing unscheduled shutdowns is a critical prerequisite [1]. Plasma-facing components (PFCs), such as the divertor and first wall, are exposed to extreme thermal loads, shocks, and particle bombardment, conditions that can lead to component failure. In...
Abstract
Reversed Shear Alfvén Wave (RSAE) is an ubiquitous mode driven by energetic particles (EPs). Through RSAE, one can infer the existence of reversed $q$-profile and the EP activities. Therefore it is an important branch of Alfvén wave. In this work, we propose a data-driven approach to identify RSAEs in HL-2A tokamak experiments.
First, by analyzing the diagnostic data...
Equilibrium reconstruction is a fundamental task in tokamaks, as it provides the distribution of the fields and currents inside the plasma. In recent years, the magnetic configurations and plasma scenarios have become increasingly complex. Their accurate identification is therefore particulalrly important to achieve the required performances. Accurate knowledge of the magnetic fields is also...
To achieve high-performance plasmas, it is necessary to measure and understand perturbation components of plasma velocity distribution functions and avoid disruptive instabilities or continuous losses of plasmas by turbulent transport. One of the most reliable and prevailing methods for measuring velocity distribution functions is the charge exchange recombination spectroscopy (CXS). CXS...
In plasma experiments heat loads to material surfaces are of interested, which involves solving an inverse problem given temperature information. Infrared cameras can provide surface temperature information at high spatial and temporal resolution. To determine the heat flux density to the material the heat diffusion equation in the solid needs to be solved, respecting relevant boundary...
Chenguang Wan 1,3, *, Feda Almuhisen 2 , Philippe Moreau 2 , Remy Nouailletas 2 , Zhisong Qu 1 , Youngwoo Cho 1 , Robin Varennes 1 , Kyungtak Lim 1 , Kunpeng Li 1 , Zhengping Luo 3 , Qiping Yuan 3 , Xavier Garbet 1,2
1 School...
Understanding the scaling of the L-H transition power threshold $P_\mathrm{LH}$ is crucial for the operation of future tokamaks such as ITER and SPARC[1~3]. However, multicollinearity of the predictor variables in the scaling makes it difficult to disentangle the effect on the power threshold of the individual plasma variables. In this study, we analyze the scaling of $P_\mathrm{LH}$ in a...
Quantification of the total emitted radiation is essential for the understanding and control of magnetic confinement plasmas. Its relevance is going to increase in the next generation of metallic devices that will have to operate at very high radiated fractions. The local emission from the bolometric integrated measurements is obtained with sophisticated tomographic algorithms. The layout of...
A new soft X-ray (SXR) array system based on ceramic circuit boards has been installed on HL-3 tokamak to provide line-integrated measurements of the plasma emissivity in the energy range of 0.1–20 keV, which contains important information for the study of magnetohydrodynamic (MHD) activity. The lines-of-sight of this diagnostic cover a substantial portion of the poloidal cross-section,...
Fusion devices, such as the Korea Superconducting Tokamak Advanced Research (KSTAR) facility, operate under conditions characterized by substantial noise and complexity, necessitating advanced non-invasive diagnostic methods. In recent studies at KSTAR, Bayesian inference techniques are applied to significantly improve the accuracy and reliability of plasma diagnostics. Specifically, Bayesian...
Beam emission spectroscopy [1] (BES) is an active plasma diagnostic employed for plasma density measurements. In multiple BES applications such as synthetic diagnostics, density inference models, and plasma control frameworks computationally expensive emission inference calculations are utilised to determine the expected emission for a given density profile. The resource intensiveness of such...
In Tokamak, plasma density is a key parameter influencing confinement and transport. The rapid acquisition of reliable plasma density profiles is crucial for the real-time feedback control of density. However, measurement uncertainty leads to inconsistence between different density diagnostic systems, making it difficult to use and understand data. This report introduces data fusion and...
In magnetic confinement fusion, precise control of plasma dynamics and shape is essential for stable operation. We present two complementary developments toward real‑time, intelligent control on the HL‑3 tokamak. First, we build a high‑fidelity, fully data‑driven dynamics model to accelerate reinforcement learning (RL)–based trajectory control. By addressing compounding errors inherent to...
The stability control of neoclassical tearing modes (NTMs) is critical for achieving high-performance steady-state operation in future magnetic confinement fusion devices. Active suppression of seed magnetic island formation represents a key early intervention strategy to minimize the cost of NTM control. This study addresses the critical threshold problem of NTM seed magnetic island...
Integrated modeling and experimental validation of H-mode divertor detachment and core confinement compatibility on HL-2A tokamak
SHU Yukun1) WANG Zhanhui1) XU Xinliang1) WU Xueke1) WANG Zhuo1)
WU Ting1) ZHOU Yulin1) FU Cailong1) ZHONG Yijun2) YU Xin1) LIYonggao1) HE Xiaoxue1) YANG Zengchen1) Kunlun Integrated Simulation and Design Group1)
1) (Center for Fusion Science,...
This report focuses on technological innovations in visualization within magnetic confinement fusion research, systematically elaborating on the groundbreaking applications of cinematic dynamic simulation, extended reality (XR) interaction, and intelligent 3D reconstruction in device modeling, theoretical demonstration, and scientific communication.
Based on high spatiotemporal resolution...
Understanding plasma behavior throughout the entire duration of a pulse is critical for achieving the objectives of ITER and future fusion devices. Plasma parameters such as temperature, density, current profiles, and impurity content evolve dynamically during a discharge, influencing key aspects of performance including energy confinement, stability, and fusion power production. Analyzing...
The increasing integration of artificial intelligence (AI) into fusion research demands scalable, standardized, and traceable data infrastructures. To address this need, we introduce JDDB (J-TEXT Disruption Database), a flexible and extensible most of all, a light weight data processing framework designed to streamline the management and transformation of fusion data across tokamak...
Fast ions generated by fusion reactions or auxiliary heating system play a quite essential role in magnetic confinement fusion reactor. The experiments on ASDEX-U and DIII-D have shown that low-frequency MHD activities like TM or NTM will induce the fast ion losses[1][2]. Theoretical and simulation analyses indicate two distinct mechanisms for low-frequency MHD-induced fast-ion losses (FIL)....
Accurate measurement of charged fusion products in magnetically confined plasmas faces significant challenges due to plasma radiation exposure, electromagnetic interference, thermal loads, and background bombardment by electrons/ions, which generate substantial noise while yielding sparse signals. To address these issues, advanced solutions are required beyond conventional detector design and...
Edge-localized modes (ELMs) are quasi-periodic edge instabilities in tokamaks, accompanied by expulsion of heat and particles from the plasma. Large ELMs can heighten the risk of damage to the plasma-facing components (PFCs). However, under certain conditions, ELMs can exhibit strongly stochastic behavior, showing a mix of relatively small and larger bursts. This complicates the predictability...
Traditional first-principle-based tokamak plasma magnetic response models, while pos-
sessing clear physical significance, often exhibit significant deviations when compared
with actual experimental data, limiting our deep understanding of plasma dynamics pro-
cesses. This study develops an autoregressive neural network model based on improved
WaveNet architecture that directly learns...
Accurate real-time control of plasma equilibrium is critical for stable tokamak operation. This study proposes a novel imitation learning framework to predict optimal feedforward currents for poloidal field (PF) coils based on plasma state observations. The model ingests high-dimensional state vectors including plasma boundary coordinates, plasma current centroid positions, and total plasma...
This paper presents a novel uncertainty optimization algorithm for the design of line-of-sight (LOS) systems used in tomographic inversion. By extending Gaussian process tomography from discrete pixel space to continuous function space through Bayesian inference, we introduce an uncertainty function and analyse its typical distributions. We develop an algorithm to minimize the uncertainty,...
Next-generation fusion devices such as DEMO present significant challenges in diagnostic system design due to spatial and cost constraints. Previous work has demonstrated the successful application of Bayesian experimental design to DEMO, optimizing both the placement and orientation of magnetic coils to reduce sensor quantity while maintaining diagnostic accuracy.
In this study, we extend...
Disruption is a catastrophic event in tokamaks and represents a major challenge for future commercial fusion reactors. Although data-driven disruption prediction models trained on a single tokamak have successfully triggered disruption mitigation, obtaining large disruptive datasets for every new device is economically and operationally impractical. Cross-tokamak disruption prediction is...
A degree of detachment is critical for the sustainable operation of a fusion energy plant within stringent safety limits [1]. Consequently, it is essential to develop a thorough and practical understanding of divertor detachment and control to move the technology readiness level of the tokamak from development to deployment. This topical review will cover the data analysis techniques and...
Preparing to interpret the data arising from ITER plasma operation requires the development and validation of models for each of the diagnostic systems installed.
Prioritising the development of synthetic diagnostics for the Start of Research Operations (SRO) has focused attention initially on models for the interferometers (TIP and DIP), polarimeter (PoPola, including full Stokes vector...
Tungsten will be a strong candidate of the material for ITER divertor. All Spectroscopies related to tungsten were then become highlight fields. Meanwhile Molybdenum is an impurity that are not be neglected in many Tokamak plasma, and thus be used for diagnostic widely.
The x-ray transitions from W45+,46+ ions and Mo32+ in the 5.19–5.26Å wavelength range that are relevant as a...
Tungsten (W), widely used in tokamak plasma-facing components due to its high melting point, poses a critical challenge for fusion performance due to potential core accumulation and associated radiative losses [1]. Recent experiments on EAST have observed a remarkable 70% reduction in core W radiation following the application of resonant magnetic perturbation (RMP) fields [2], yet the...
Wendelstein 7-X (W7-X) is a superconducting optimized stellarator built in Greifswald/Germany which started its first operation with limiter plasmas in 2015. Since 2022 it is being operated with fully water-cooled first wall components including high heat flux graphite divertors, allowing quasi steady-state plasma operation. Approx. 50 diagnostic systems are applied to get insights into the...
Reliable magnetic equilibrium reconstruction is key for the interpretation of experimental data and for physics modelling. Uncertainties of equilibrium quantities are frequently not provided although essential for the validation and quantification of derived physical quantities. A Monte-Carlo approach applicable to a free-boundary equilibrium reconstruction is suitable to provide uncertainties...
Effective impurity control is essential for sustaining high-performance plasma operation in magnetic confinement fusion devices such as ITER. The visible spectroscopy reference system (VSRS) is designed to measure visible radiation, including bremsstrahlung, spectral lines, and synchrotron radiation, for the inference of key plasma parameters, such as the effective ion charge $Z_\mathrm{eff}$,...
International Thermal Nuclear Reactor (ITER) will be equipped with a large array of diagnostics and produce a huge amount of data characterized by redundant, complementarity and complex errors. A prominent challenge for ITER as well as other fusion devices, is how to make the best use of such a large amount of data to obtain as much useful information as possible while meeting the requirements...
Tungsten (W) will be used in the ITER tokamak for its divertor and first wall. The strong plasma radiation from heavy impurities like tungsten may cause significant power loss and can pose a major risk to the energy confinement. Reliably estimating the tungsten concentration in a fusion device is therefore critical for transport studies and active impurity control. This is often achieved by...
Stimulated emission by neutral beam (NB) injection is of key importance for diagnosing the state of the core plasma in tokamaks. This paper reports on the development of a new Bayesian model of a spectral diagnostic system with NB injection. Implemented within the Minerva framework and with data entry via the ITER Integrated Modelling & Analysis Suite (IMAS), the model provides a generic...
The precision of plasma equilibrium is a pivotal issue in the field of fusion plasma physics and the operation of tokamak plasmas. During the plasma equilibrium uncertainty quantification (UQ) campaign, input parameters for the equilibrium solver are assigned default value ranges derived from EAST experimental data. These input parameters are subsequently processed by an encoder, decoder, and...
The cornerstone of the Integrated Modelling & Analysis Suite (IMAS) developed by ITER is its machine-agnostic data model, called the Data Dictionary (DD). It was recently made open access [1] to permit a broader adoption outside of the ITER Members and by private fusion ventures. Discussions and contributions are welcome and held publicly in GitHub, either for new extensions, clarifications or...
The Neutral Beam Injection (NBI) is the practical and promising auxiliary method for heating and current drive in magnetic fusion facilities. The work demonstrates a general workflow for decision of beam geometries and parameters, with detailed numerical simulation about power deposition, current drive, beam ion confinement and fusion performance prediction with HL-3 parameters. Optimization...
Artificial Intelligence based fusion data processing using textual semantic models to interpret fusion data.Based on the training dataset, the textual topic semantic extraction model combined with the physical model is used to realize the prediction and evaluation of the data.Use the dataset for topic model concept mapping, convert the concept of text semantic topic extraction to data feature...
Fusion energy research is generating increasingly complex, heterogeneous, and high-volume datasets that span from engineering to plasma physics domains, multiple institutions and instruments. While there have been attempts to enable data integration (such as ITER Integrated Modelling and Analysis Suite, IMAS), the infrastructure required to make this data FAIR — Findable, Accessible,...
Innovative materials development is essential for advancing nuclear fusion energy technologies, which require high-performance materials capable of withstanding extreme conditions such as intense heat and radiation exposure. Tungsten (W) has historically been a preferred material due to its high melting point, availability, and cost-effectiveness [1]; however, it remains highly susceptible to...
