A new milestone in controllable nuclear fusion, AI achieves fully automatic optimization of dual tokamak 3D field for the first time, published in Nature sub-issue

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Now, at the Princeton Plasma Physics Laboratory (PPPL), scientists are using artificial intelligence to solve a pressing challenge facing humanity: generating clean, reliable energy through fusion plasma.

Unlike traditional computer code, machine learning is more than just a list of instructions. It can analyze data, infer relationships between features, and learn and adapt from new knowledge.

PPPL+ researchers believe this ability to learn and adapt could improve their control of fusion reactions in a number of ways. This includes perfecting the design of the vessel surrounding the superheated plasma, optimizing heating methods, and maintaining stable control of the reaction over increasingly longer periods of time.

Recently, PPPL’s AI research has achieved significant results. PPPL researchers explain how they use machine learning to avoid magnetic disturbances and stabilize fusion plasmas. This achievement is of great significance for achieving sustainable fusion energy. By analyzing and training large amounts of data, the researchers successfully developed a machine learning model that can accurately

Illustration: The two tokamak shown above ( Machine learning code for detecting and eliminating plasma instabilities is deployed in DIII-D and KSTAR devices. (Source: General Atomics and Korea Fusion Energy Research Institute)

SangKyeun Kim, PPPL research physicist and lead author of the discussion paper, said: "The results are impressive because we were able to use the same The code achieves these results on two different tokamak devices."

The related research is titled "

Highest fusion performance without harmful edge energy bursts in tokamak

" and was published in "#. ##Nature Communications》On.

Paper link: https://www.nature.com/articles/s41467-024-48415-w

Suppressing "edge explosions" in fusion

In order to be economically competitive in the fusion energy market, it must achieve sufficient ion density (n), temperature (T) while maintaining fusion ) and the high fusion triple product (nτT) of the energy confinement time (τ).

Ions require a sufficient quality factor (G∝ατT) to achieve high fusion denaturation, and this increases with the ion confinement mass (H89: normalized energy confinement time).

In order for the tokamak design to become a viable option for fusion reactors, reliable methods must be developed to periodically suppress edge burst events without affecting G.

Scientists have used various methods to mitigate fringe outbreaks. One effective approach is to utilize resonant magnetic perturbation (RMP) of external 3D field coils, which has proven to be one of the most promising methods for edge burst suppression.

Illustration: 3D field coil structure in a tokamak. (Source: Paper)

However, this scenario is costly, resulting in significant deterioration of H89 and G compared to standard high-confinement plasma systems, thereby weakening the economic prospects. In addition, 3D fields also increase the risk of catastrophic core instability, known as disruption, which is even more severe than edge blowout. Therefore, the safe accessibility and compatibility of edge-burst-free operations and high-constraint operations urgently need to be explored.

First implementation on two tokamak

This research conducted an innovative and integrated 3D field on two tokamak, KSTAR and DIII-D for the first time Optimization, by combining machine learning (ML), adaptive and multi-machine capabilities, to automatically access and achieve almost completely edgeless burst states while improving plasma fusion performance from the initial burst suppression state, which is the edgeless future reactor A major milestone in the outbreak's run.

This is achieved by exploiting the lag between edgeless burst onset and loss in real time to enhance plasma confinement, while expanding ML's capabilities in capturing physics and optimizing nuclear fusion technology.

Illustration: Performance comparison of ELM-free discharge in DIII-D and KSTAR tokamak. (Source: paper)

This integration helps:

• Highly enhanced plasma confinement, reaching the highest fusion G in the Edge Localized Mode-free (ELM-free) scenario of the two machines, with G increased by up to 90%;

• First fully automated 3D field optimization using ML-based 3D field simulator;

• Simultaneous establishment of burst suppression from the beginning of plasma operation , achieving almost complete edge-free burst operation close to ITER-related levels. This achievement represents a crucial step for future devices such as the International Thermonuclear Experimental Reactor (ITER), where reliance on empirical RMP optimization is no longer a feasible or acceptable approach.

"There are instabilities in the plasma that can cause serious damage to the fusion device. We cannot use these substances in commercial fusion vessels. Our work advances the field and And shows that artificial intelligence can play an important role in managing fusion reactions to avoid instability while allowing the plasma to generate as much fusion energy as possible," said corresponding author Egemen Kolemen, associate professor in the Department of Mechanical and Aerospace Engineering at PPPL.

Fully Automated ML-Based 3D Field Optimization

In this experiment, a series of discharges are used to find optimized 3D waveforms for safe ELM suppression.

In this context, the study introduces ML techniques to develop novel paths for automated 3D coil optimization and demonstrates the concept for the first time.

Illustration: Real-time RMP optimization algorithm based on machine learning. (Source: Paper)

Researchers developed a surrogate model of the GPEC code (ML-3D) to leverage physics-based models in real time. The model uses ML algorithms to accelerate computation time to the ms level and is integrated into the adaptive RMP optimizer in KSTAR.

ML-3D consists of a fully connected multilayer perceptron (MLP) driven by nine inputs. To train the model, 8490 KSTAR balanced GPEC simulations were utilized.

Illustration: ML-3D model performance. (Source: paper)

The algorithm utilizes the ELM status monitor (Dα) signal to adjust the IRMP in real time, which can maintain sufficient edge 3D fields to access and maintain ELM suppression. At the same time, the 3D field optimizer uses the output of ML-3D to adjust the current distribution on the 3D coil, thus ensuring a safe 3D field to avoid interruptions.

Illustration: Plasma parameters for a fully automated ELM suppressed discharge (#31873) with integrated RMP optimization. (Source: paper)

In KSTAR experiments, the ML-integrated adaptive RMP optimizer triggered in 4.5 seconds and achieved safe ELM suppression in 6.2 seconds.

Research also demonstrates 3D-ML as a viable solution for automating ELM-free access. ML-3D is based on physical models and does not require experimental data, making it directly scalable to ITER and future fusion reactors. This strong applicability to future devices highlights the advantages of ML's integrated 3D field optimization approach. Furthermore, better field optimization and higher fusion performance are expected to be achieved in future devices with higher 3D coil current limitations.

Research successfully optimized controlled ELM-free states in KSTAR and DIII-D devices with highly enhanced fusion performance, covering low-n RMP related to future reactors to nRMP related to ITER = 3 RMP, and achieved the highest level in various ELM-free scenarios in two machines.

Illustration: Plasma parameters for optimized RMP amplitude (#190738) with highly enhanced performance. (Source: paper)

In addition, the innovative integration of ML algorithms with RMP control enables fully automatic 3D field optimization and ELM-free operation for the first time, and with the support of adaptive optimization processes, performance is significantly enhanced . This adaptive approach demonstrates compatibility between RMP ELM suppression and high limits.

Additionally, it provides a robust strategy to achieve stable ELM suppression in long pulse scenarios (lasting more than 45 seconds) by minimizing the loss of limiting and uninductive current fractions.

Notably, significant performance (G) improvement was observed in DIII-D with nRMP = 3 RMP, showing an improvement of more than 90% from the initial standard ELM suppression state. This enhancement is attributed not only to the adaptive RMP control but also to the self-consistent evolution of the plasma rotation. This response enables ELM suppression at very low RMP amplitudes, thereby enhancing the base. This feature is a good example of a system transitioning to an optimal state through a self-organized response to adaptive modulation.

Illustration: Improving discharge performance through adaptive RMP optimization. (Source: paper)

In addition, the adaptive scheme is combined with the early RMP-ramp method to achieve ITER-related ELM-free scenarios with almost completely ELM-free operation. These results confirm that integrated adaptive RMP control is a very promising approach to optimize ELM suppression states, with the potential to address one of the most difficult challenges in achieving practical and economically viable fusion energy.

Reference content: https://phys.org/news/2024-05-ai-intensive-aspects-plasma-physics.html

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