Ryan McClarren (ND, USA), Hideo Nagatomo (Osaka U., Japan), Marcin Jakubowski (IPP, Germany)
On December 5, 2022, scientists at the Lawrence Livermore National Laboratory carried out the first-ever Inertial Confinement Fusion experiment that met all criteria for ignition. This 2.05-megajoule laser shot at the National Ignition Facility compressed a millimeter-size capsule containing hydrogen fuel, leading to fusion reactions, and generating 3.15 megajoules of energy, a gain of 1.5....
Magnetically confining a high temperature plasma in a toroidal device (e.g., tokamak, stellarator, etc) is arguably the most promising approach for mankind to archive controlled thermonuclear fusion energy. One critical concern of this approach is to find and maintain the proper heat and particle exhaust at the divertor region – a region at where the magnetic topology changes from “closed” to...
Accurate simulations of the scrape-off layer plasma in a tokamak employing state-of-the-art numerical models (e.g. SOLPS-ITER) require long convergence times. Such physically sophisticated models are required for a detailed design e.g. of the divertor in DEMO or in a fusion power plant (FPP). For design scoping studies or integration of models with the core-plasma, currently used reduced...
For decades, plasma transport simulations in tokamaks have employed the finite difference method (FDM) to solve the transport equations, a coupled set of time-dependent partial differential equations. In this conventional approach, a significant number of time steps, typically over $O(10^5)$, are needed for a single discharge to prevent numerical instabilities induced by stiff transport...
For stable and efficient fusion energy production using a tokamak reactor, maintaining high-pressure hydrogenic plasma without plasma disruption is essential. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to maneuver high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle avoidance...
Machine learning and artificial intelligence (ML/AI) methods have been applied to fusion energy research for over 2 decades, including the areas of disruption prediction, particle distribution and loss prediction, plasma equilibrium reconstruction and so on. The success in achieving magnetic control of the TCV tokamak with deep learning methods has demonstrated great opportunities for...
Edge plasma turbulence is critical to the performance and operation of magnetic confinement fusion devices. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by partial...
Michael Churchill (PPPL, USA), Cristina Rea (MIT-PSFC, USA), Zongyu Yang (SWIP, China)
Inertial confinement fusion (ICF) relies on the implosion of precision engineered capsules containing DT fuel. The implosion is initiated by a driver, usually a laser, and the target may feature one or more outer shells to enable driver coupling. First Light Fusion’s (FLF) novel approach separates the design of the target into a fuel capsule and a shock amplifier, which is uni-axially driven...
Magnetic confinement fusion research is characterized by computationally demanding physics models with a selection of uncertain, phenomenological input parameters. Rigorous usage of such models for predictive or interpretative applications requires a thorough inverse uncertainty quantification (UQ) for these input parameters [1]. Bayesian inference (BI) algorithms provide a principled approach...
Laser-driven inertial confinement fusion is an important approach to achieve controllable nuclear fusion. It applies high-power laser pulses or X-rays to ablate the outer surface of a spherical target, leading to a centripetal implosion and an increase in pressure and temperature of the fuel. In order to reach the Lawson criterion and thus realize a self-sustaining burning plasma, we have to...
Understanding the properties of materials when exposed to various plasma
temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure experiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto-MPEX, the...
Eni strongly relies on the use of Artificial Intelligence (AI) based solutions, with the purpose of progressively making operations more efficient and sustainable, from enhancing safety for personnel to ensuring the integrity of Eni's assets through predictive maintenance solutions and aiding the Research & Development department in crafting innovative technologies to achieve net-zero...
We present three case studies demonstrating the minimisation or elimination of human intervention from the process of generating data sets with relevance to different problems in tokamak fusion experiment design and control.
The design of new tokamaks, optimisation of plasma scenarios and construction of real-time control systems in tokamaks require a comprehensive and often expensive...
Accurate simulation of fusion plasma turbulence is required for reactor operation and control, but is either too slow or lacks accuracy with present techniques.
The FASTER project aims to circumvent these conflicting constraints of accuracy and tractability and provide real-time capable turbulent transport models with increased physics fidelity for tokamak temperature, density, and rotation...
During the WEST experimental fusion plasma discharges, several diagonistics are employed to collect diverse data. Among these diagnostics, two high-definition cameras, operating within the visible spectrum, broadcast in real time the plasma inside the vacuum vessel.
The intent of our study is to investigate the possibility of determining the plasma state from this live video for each...
In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. Also this controller is implemented on digital signal processor (DSP) control system.
In the first stage, a nonlinear model is identified for plasma vertical position, based on the...
Intelligent control of the thermal loads is required to guarantee the safety of fusion devices such as W7-X and ITER during quasi-steady-state operation with reactor-relevant performance. This feedback control system should implement preemptive strategies, enabling long-plasma operation by impeding thermal load escalation to dangerous levels and minimizing plasma terminations triggered by the...
It is a crucial challenge that disruption prediction should learn from limited data due to the considerable expense of obtaining an extensive experimental dataset in future tokamaks. To reduce the data requirements for future tokamaks, utilizing existing knowledge of disruption physics and tokamak discharge could be helpful. IDP-PGFE (Interpretable Disruption Predictor based on Physics-Guided...
A digital twin for plasma dynamics in a tokamak is useful for optimising and validating experimental scenario proposals, developing plasma control systems and more. Physics-based modelling of the entire tokamak discharge process is challenging due to nonlinear, multi-scale, multi-physics characteristics of the tokamak and demands time from a diverse team of experts as well as computational...
In KSTAR tokamak, frequency-modulated continuous wave (FMCW) reflectometry has been used to measure plasma density profiles with high spatial and temporal resolution. The data analysis process involves extracting time-varying phase differences from the incident swept signal and reflected wave’s signal and subsequently calculating profiles through the numerical inversion process. However,...
The quality of energetic particle confinement in a nuclear fusion reactor is a key factor in the reactor's efficiency. One way of studying the behavior of energetic particles in detail is to integrate "test particle" trajectories into a previously calculated turbulent electric potential field. The high cost of calculating the turbulent field, and the size of the data, make it very difficult to...
Developing reliable control systems for long pulse operation in fusion devices is crucial and challenging for the development of ITER and DEMO. In this context, two of the most critical issues are plasma-facing components (PFCs) protection from high heat loads and disruption prevention. This talk deals with Machine Learning (ML) tools developed for machine protection from these two issues,...
Plasmas and plasma-enabled technologies are pervasive in everyday life, but their nonlinear, multiscale behaviors bring challenges for understanding, modeling, and controlling these systems. Accurately revealing the physical mechanism of plasma may provide crucial information for the successful plasma control in real-time tokamak discharge. However, the computation demands a realistic...
Disruption prediction and mitigation is a crucial topic, especially for future large-scale tokamaks, due to disruption’s concomitant harmful effects on the devices. Recent progresses have proved that deep neural network can accurately predict the coming disruptions by learning from history experimental data, which becomes a potential solution for the disruption prediction in future devices [1,...
Alessandro Pau (EPFL-SPC, Switzerland), Michael Churchill (PPPL, USA), Fuyuan Wu (SJTU, China)
The European High Performance Computing Joint Undertaking (EuroHPC JU) is a joint initiative between the European Union, European countries and private partners to develop a world class supercomputing ecosystem in Europe. It pools resources from the stakeholders and coordinates their efforts to invest in research, innovation and the deployment of a world-leading HPC infrastructure in Europe....
Predictive and reliable simulations of fusion plasmas provide one important pathway towards accelerating fusion research. A popular approach for efficiently computing the dynamics of turbulent systems for a wide range of applications is the Large Eddy Simulation (LES) technique. Here, the system is simulated with only the largest scales resolved explicitly, while the unresolved scales are...
Quantum computing promises to deliver large gains in computational power that can potentially have a beneficial impact on a number of Fusion Energy Science (FES) application areas that rely on either intrinsically classical or intrinsically quantum calculations. This work presents an overview of our recent efforts [1] to develop and extend quantum algorithms to perform FES-relevant...
Plasma diagnostics is an essential tool to understand and improve plasma stability in fusion devices. It provides useful information for the analysis and understanding of physical phenomena.
Infrared (IR) thermography is also important diagnostics applied for machine protection and plasma control, especially for the future fusion devices working with long plasma pulses, such as ITER or DEMO....
Single-chord interferometry is widely used in plasma physics to obtain the line-integrated density of a plasma. In this work, we propose the use of a (deep) neural network (NN) to assist in the development of a novel diagnostic technique which allows the estimation of the plasma density profile from a single interferometry measurement. The purpose of the NN is to solve the inverse-scattering...
Artificial Intelligence (AI) and Machine Learning (ML) have imposed themselves as the standard toolkit for image processing. This happens also in experimental science, where the tools and methods of AI/ML (use case definition, annotated database creation, learning and inference) prove fruitful at solving science and technology challenges ill-addressed by conventional processes. At WEST, an...
In large tokamak reactors, one unmitigated disruption will bring intolerable damage to them. Accurate plasma disruption prediction system is needed to trigger the disruption mitigation system. Currently machine learning disruption predictor is the most promising way of solving this problem. But it does need data from the target machine to be trained. However, the future machine will not be...
Tokamaks require magnetic control across a wide range of plasma scenarios. The coupled behavior of plasma dynamics makes deep learning a suitable candidate for efficient control in order to fulfil these high-dimensional and non-linear situations. For example, on TCV, deep reinforcement learning has already been used for tracking of the plasma’s magnetic equilibrium [1]. In this work, we apply...
The aim of this work is the development and analysis of Al tools for welding success rate prediction and the posterior output processing of PAUT applied to welding defects detection in the ITER Vacuum Vessel manufacturing.
Due to its complexity, the manufacturing of this large equipment - based on the French nuclear design and manufacturing code (RCC-MR) - has generated a large amount of...
Artificial neural network (NN) surrogate models have been developed and trained on magnetic, magnetic + motional stark effect (MSE), and kinetic DIIII-D equilibria to accelerate tokamak equilibrium reconstructions for offline, between-shot, and real-time applications. Adaptation of the ML/AI algorithms has been facilitated through the recently developed device-independent portable equilibrium...
Tokamak is a promising device for producing nuclear fusion energy. Due to the high speed of physical processes, tokamak requires automation of control system to provide the most effective use. Researchers already proposed some essential automation such as gas puffing control system [[1]] and disruption mitigation system [[2]]. In turn, performance of those proposed automations highly relies on...
To reconstruct the electron density profile of tokamak plasma, the phase of reflected microwave is to be captured by tracing the signal along the frequency as an imaginary pathway on the spectrogram. From the blurry and broken lines among the unwanted clutters, the actual path should be estimated as a continuation of the tone heights at each vertical slice of the wavelet transform. Thus, we...
Fusion plasma devices have generated in the past years large amounts of shots exhibiting Alfvén activity, which is usually detected using external magnetic sensors (Mirnov coils), but also by other diagnostics. The behaviour of Magnetohydrodynamic (MHD) modes is commonly analysed through spectrograms from Mirnov signals. However, extracting physical information of individual mode activity from...
The transition from present day tokamaks to DEMO reactor will pose great scientific and technological challenges. As a way to overcome those challenges, we have launched the development of the Virtual-KSTAR (V-KSTAR), which is based on digital twin technology. It is aiming to establish a unified machine/fusion data framework and simulation workflows. By elevating the maturity of the digital...
The study of nuclear fusion requires a massive amount of computing resource with highly divergent computing paradigm, including simulation, diagnostic, plasma control, AI computing, etc. To achieve the best performance, various tasks should be implemented on heterogeneous computing devices, typically CPUs and GPUs. The heterogeneity on the computing resources and computing paradigms brings...
Reinforcement learning (RL) is a promising technology for the future of fusion power. A key challenge is to stabilize and regulate the plasma position and shape via magnetic fields generated by a set of control coils. This talk discusses our efforts to generate magnetic controllers using deep reinforcement learning. We train controllers on a Grad-Schafranov based simulator and then deploy the...
While the quantitative data generated by tokamaks is invaluable, tokamak operations also generate another, often underutilized data stream: text logs written by experimental operators. In this work, we leverage these extensive text logs by employing Retrieval-Augmented Generation (RAG) with state-of-the-art large language models (LLMs) to create chat-bot instances that can answer questions...
Predicting the evolution of plasma instabilities and turbulence within a tokamak power plant is essential for achieving sustainable fusion. Efficiently forecasting the spatio-temporal evolution of plasma enables rapid iteration over design and control strategies for both current tokamak devices and future power plants. However, traditional numerical solvers for modelling plasma evolution are...
Machine learning has vast potential in medical image analysis, improving possibilities for early diagnosis and prognosis of disease. Algorithms typically need large amounts of representative, annotated examples for good performance, which may be difficult to achieve, for example due to differences between image acquisition procedures, or the time and effort involved in annotation. To address...
