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Future fusion power plants may experience fewer energy losses in their burning plasma than anticipated. The authors of the study - researchers from the EUROfusion consortium, including Dr. Michał Poradziński from the Institute of Plasma Physics and Laser Microfusion (IPPLM) - published this surprising result in the prestigious journal Nature Communications. Their findings, based on experiments conducted in 2021 in the Joint European Torus machine (JET), reveal that a fuel mix containing tritium stabilizes the plasma, significantly enhancing reactor performance. This stabilization reduces turbulence and energy losses, paving the way for smaller, more efficient fusion power plants.

Experiments with deuterium fuel are the most common ones performed nowadays in tokamak (toroidal magnetic trap) experiments all over the world. Many aspects of controlling such plasmas have been investigated so far and are known. Much less common however are experiments with tritium which is a heavier (contains one proton and two neutrons) and unstable isotope of hydrogen. Deuterium is a lighter isotope which contains one proton and one neutron. By adding tritium, we create a D-T mixture which is the actual fuel mix to be used in a power plant.

The fusion of deuterium and tritium will produce a highly energetic neutron and a heavier but still very fast helium ion. That is why D-T plasmas are called "burning plasmas". The JET (Joint European Torus) experiment carried out in Culham (UK) in 2021 was the third in the history after TFTR (1993) and JET (1997) to include tritium in a fuel mixture. It was a huge unknown how tritium, being heavier than deuterium, would affect the confinement of the plasma which is the ability to trap ions by the magnetic field. Another unknown was to what extent the fast helium ions would affect the plasma stability. A recent article published in Nature Communications shows that in conditions close to the ones expected in a future fusion reactor, fast helium ions have a positive effect on plasma stability and tritium has a beneficial effect on plasma confinement. This discovery puts us one step closer to the working fusion power plant.

Dr. Michał Poradziński from the IPPLM, a co-author of the article, does not hide his enthusiasm: "Thanks to the deuterium-tritium experiments we have been able to explore areas of fusion research that have been available to us only using extrapolation from deuterium and hydrogen experiments and by applying known theoretical models. However, due to the complexity of the processes occurring in tokamak plasmas, the theoretical uncertainty of the physical models was large and required verification in the experiment. This research shows that we are never completely sure what is behind the corner. In this case, it turned out that fast helium ions work in our favour by reducing the instabilities. Moreover, the turbulence usually taking place in the outer regions of the plasma core has been reduced which is an unexpected result. This is very good news. It's a very important step towards fusion reactors."

Source: Nature Communications, EUROfusion

By launching 15 new research projects, EUROfusion is engaging data science experts across Europe to apply Artificial Intelligence and Machine Learning techniques to fusion energy. These projects will leverage the world's largest and most diverse dataset of fusion experiments to identify optimal methods for understanding and controlling the fusion process, in order to develop optimal methods for understanding and controlling the hydrogen isotope fusion process, thus accelerating the path to its practical application in the energy industry.

New technologies in Polish research on nuclear Fusion

One of the projects was awarded to a research team from the Institute of Plasma Physics and Laser Microfusion (IPPLM), which is involved in the development of LIBS (Laser Induced Breakdown Spectroscopy) diagnostics in the context of nuclear fusion research. Researchers, who previously participated in key LIBS experiments as an application of a remotely operated diagnostic head on an FTU tokamak in Italy, will now focus on implementing machine learning methods for mass processing of spectroscopic data. Their goal is to use models based on convolutional networks, which are great for image analysis, for new challenges in spectral diagnostics. Although spectroscopic data require specific pre-processing methods and network architecture, the main author of the project, Dr. Paweł Gąsior from the IPPLM, emphasizes that the potential of artificial intelligence can bring similar breakthrough results to those observed in other fields, such as image recognition. In addition to the IPPLM team, researchers from CU (Slovakia) and FZJ (Germany) are also involved in the research. The first test of the novel approach will be conducted using data from the ongoing LIBS for JET experiment on the JET tokamak in Culham, UK, opening new perspectives for the application of LIBS technology in nuclear fusion research.

Fusion energy promises to deliver safe, sustainable, and low-carbon baseload power, complementing other clean energy sources like solar and wind. To achieve this, we need to address complex physics and engineering challenges, including understanding the collective movements of charged particles in magnetic fields, mitigating disruption events, analysing material erosion effects, and processing data rapidly enough for use in control loops. Artificial Intelligence and Machine Learning offer tools which allow for a substantial progress in all these seemingly diverse research areas.

Dr. Paweł Gąsior emphasizes that the use of artificial intelligence can bring breakthrough results: "Machine learning, particularly convolutional neural networks, has demonstrated remarkable proficiency in recognizing patterns within large datasets. Consequently, they can be significantly beneficial when handling spectral data, which, despite being sensitive to experimental conditions, still retains information too deeply embedded for traditional data processing methods."

Artificial intelligence will accelerate progress in research

"With new research projects on Artificial Intelligence and Machine Learning, EUROfusion aims to accelerate progress towards fusion energy and support the ongoing efforts in its work packages", explains Sara Moradi of the EUROfusion Programme Management Unit. "Machine learning and Artificial Intelligence are powerful tools for extracting insight from data, uncovering patterns and suggest control schemes that are too computationally intensive to identify with traditional computer models."

EUROfusion’s extensive dataset of fusion experiments spans decades of research, from the earliest fusion machines to the most advanced systems currently in operation. This unparalleled resource positions EUROfusion uniquely to drive forward Artificial Intelligence applications in fusion research.

Fusion is a great sandbox for Artificial Intelligence and Machine Learning, agrees José Vicente (University of Lisbon), the principal investigator of one of the fifteen projects. "As a very complex system, it has many open questions. We can already address those with today's large amounts of experimental data and realistic numerical simulations of the key physics, but not all of them — that is the gap that Artificial Intelligence may help close."

The 15 projects will receive a total amount of €2.659 million, of which half is provided by collaborative co-funding from the researchers' home institutes and half from EUROfusion. The research projects will run for a period of two years.

Projects supported by EUROfusion, including the one implemented by the team from the IPPLM, underscore the potential of Artificial Intelligence and Machine Learning to address key challenges in fusion research, paving the way for more efficient and effective control strategies as we move closer to realizing fusion energy.

Artist’s impression of Artificial Intelligence research for fusion. Credit: Pexels / GoogleDeepMind

Supported projects:

David Zarzoso (CEA / CNRS, France)
Artificial Intelligence augmented Scrape Off Layer modelling for capturing impact of filaments on transport and PWI in mean field codes simulations.

Feda Almuhisen (CEA / Aix-Marseille Université, France)
Towards Tokamak operations Conversational Artificial Intelligence Interface Using Multimodal Large Language Models

Augusto Pereira (CIEMAT, Spain)
Testing cutting-edge Artificial Intelligence research to increase pattern recognition and image classification in nuclear fusion databases

Sven Wiesen (DIFFER, the Netherlands)
Machine learning accelerated Scrape Off Layer L simulations: SOLPS-NN

Gergő Pokol (EK-CER, Hungary)
Fast inference methods of advanced diagnostics for real-time control

Riccardo Rossi (ENEA / Università di Roma Tor Vergata, Italy)
Artificial Intelligence-assisted Causality Detection and Modelling of Plasma Instabilities for Tokamak Disruption Prediction and Control

Michela Gelfusa (ENEA / Università di Roma Tor Vergata, Italy)
Development of Physics Informed Neural Networks (PINNs) for Modelling and Prediction of Data in the Form of Time Series

Alessandro Pau (EPFL, Switzerland)
Artificial Intelligence-assisted Plasma State Monitoring for Control and Disruption-free Operations in Tokamaks

Pawel Gasior (IPPLM, Poland)
Laser Induced Breakdown Spectrocopy data-processing with Deep Neural Networks and Convolutional Neural Networks for chemical composition quantification in the wall of the next step-fusion reactors

Jose Vicente (IST, Portugal)
Deep Learning for Spectrogram Analysis of Reflectometry Data

Geert Verdoolaege (LPP-ERM-KMS / Ghent University, Belgium)
Identification and confinement scaling of hybrid scenarios across multiple devices

Marcin Jakubowski (IPP, Germany)
Leveraging Generative Artificial Intelligence Models for Thermal Load Control in High-Performance Steady-State Operation of Fusion Devices

Daniel Böckenhoff (IPP, Germany)
Surrogate modelling of ray-tracing and radiation transport code for faster real-time plasma profile inference in a magnetic confinement device

Antti Snicker (VTT, Finland)
Applying Artificial Intelligence/Machine Learning for Neutral Beam Injection ionization and slowing-down simulations using ASCOT/BBNBI

Aaro Järvinen (VTT, Finland)
Machine learning accelerated pedestal Magneto Hydro Dynamics stability simulations

Source: EUROfusion

Recently, IPPLM Professor Agata Chomiczewska and Dr. Natalia Wendler have taken part in the international conference Plasma Surface Interaction in Controlled Fusion Devices PSI-26 in Marseille, during which they presented the results of their research from the unique deuterium-tritium campaign on the JET tokamak. This year's edition of the above-mentioned conference took place on the 50th anniversary of its establishment. As part of the event, speakers had the unique opportunity to visit the construction site of the largest scientific project in the world, the ITER tokamak. It was a unique opportunity to directly observe the progress of work on the installation of key components of the future experimental thermonuclear reactor.

Prof. Agata Chomiczewska shared her impressions: "A visit to ITER evokes extraordinary emotions. The opportunity to see with your own eyes the construction of a giant thermonuclear reactor is an amazing experience. By observing the complex infrastructure and advanced technologies up close, it is easy to feel part of a project that could revolutionize the global energy industry."

Similar feelings were expressed by Dr. Natalia Wendler: "The impressions from my first visit to ITER are unforgettable. This is a place where researchers from around the world join forces, working on technology that can change the future of energy. The sight of such a complicated installation arouses respect and admiration, and at the same time motivates us to continue working and overcoming the challenges posed by a project with such enormous potential."

ITER is an international research project aimed at demonstrating the possibility of controlled nuclear fusion as a practical energy source. A tokamak is a device that uses a magnetic field to confine hot plasma, necessary to carry out a thermonuclear reaction similar to those taking place inside the Sun. ITER's success could be a breakthrough in the pursuit of clean and almost unlimited energy, which is of great importance for the future of global energy.

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On June 9-10, 2024, the 2nd Congress "Science for Society" was held in the Great Hall of the Warsaw University of Technology under the slogan "This is how science in Poland affects the lives of every person." The Institute of Plasma Physics and Laser Microfusion (IPPLM) presented its achievements at a special stand, where guests could learn about nuclear fusion and the Institute's scientific activities.

The main research area of the IPPLM is the use of controlled nuclear fusion to produce electricity in a safe and environmentally friendly way. The Institute is involved in diagnostic work, modelling of plasma phenomena, research on the behaviour of plasma impurities and the impact of plasma on structural surfaces.

The IPPLM stand presented 3D models of the experimental ITER tokamak built in Cadarache, France, and a stellarator model. Both of these devices use a magnetic field to keep the hot plasma away from the inner walls of the vessel, allowing fusion reactions to be carried out safely at temperatures as high as 100-150 million degrees Celsius.

Specialists from the Department of Laser Plasma Physics and Applications presented the results of research on laser plasma in the context of energy production from fusion with inertial plasma maintenance. Guests could also watch a film presenting the IPPLM laboratories and purchase numerous educational materials.

During the Congress, the IPPLM Professor Agata Chomiczewska gave an interview to a TVP reporter. A report from the event and a statement by Prof. Agata Chomiczewska can be watched on the TVP3 website in the "Dziennik Regionów" program from June 9, 2024 (beginning of coverage: 9:23).

On the first day of the Congress, the guest of honour was the Minister of Science, Dariusz Wieczorek, who in his speech emphasized the importance of increasing expenditure on research and development and promoting the results of the work of scientists and research institutions. At the end of the meeting, Minister Wieczorek together with the chairman of the Main Council of Research Institutes, Prof. Henryk Skarżyński, presented the participants with occasional statuettes and diplomas.

Photo: IPPLM

The 17^th edition of the Kudowa Summer School of Plasma Physics "Towards Fusion Energy" has been completed. The event, organized by the Institute of Plasma Physics and Laser Microfusion (IPPLM) from June 3-7, 2024, in Kudowa-Zdrój, was attended by 61 people from 11 countries.

The opening ceremony was led by the School's director Dr. Jozef Ongena of the Royal Military Academy in Brussels. The scientific and organizational committees were headed by the IPPLM Professor Monika Kubkowska and Dr. Agnieszka Jardin from the IPPLM.

The program included lectures by 25 distinguished specialists from leading scientific centers such as the Max Planck Institute for Plasma Physics (Germany), Kyoto University (Japan), University of York (UK), UKAEA (UK), CEA IRFM (France), ENEA (Italy), CELIA (France), ELI ERIC (Czech Republic) and ITER Organization.

During the student sessions, 19 participants presented their research results. The best presentations were awarded. Sebastian Thiede of the Max Planck Institute for Plasma Physics won first place. Letizia Melaragni from the University of Tuscia (Italy) and Mateusz Majszyk from the Military University of Technology took ex aequo second place. Third place was awarded to Agnieszka Wojtusiak of the Science and Technology Facilities Council (UK) and Jan Novotny of the Faculty of Electrical Engineering CTU in Prague (Czech Republic). A special prize was awarded to Yurii Martseniuk from the National Science Center Kharkiv Institute of Physics and Technology (Ukraine).

Also honored were participants with doctoral degrees: Agnieszka Jardin, Grzegorz Pełka and Łukasz Syrocki from the IPPLM.

Topics covered various aspects of plasma physics, including fusion energy, plasma experiments, plasma technologies and diagnostics.

The event was co-organized by the Euratom-IPPLM National Contact Point. Among Sponsors of the event were EKSPLA, IRtech Sp. z o.o., LTB Lasertechnik Berlin GmbH, Precoptic, Quantum Design.

The next edition of the Summer School of Plasma Physics will be held in 2026. We invite you to participate in this unique event today!

Participants of the 17th edition of the Kudowa Summer School

Photo: IPPLM