Machine learning might help us finally unlock nuclear fusion

What if we could replace a time-consuming analysis, an important prerequisite to judge the right mix of isotopes to use?
Amal Jos Chacko
Representational image of a thermal power plant.jpg
Representational image of a thermal power plant.


Why can’t we find power the same way stars do— clean, renewable, and free of radioactive waste? 

Humanity's quest for clean and sustainable energy sources has reached a pivotal moment as researchers explore nuclear fusion. Unlike current nuclear fission plants that produce energy at the cost of radioactive waste, nuclear fusion offers the promise of virtually limitless and environmentally friendly power generation.

Nuclear fusion, the phenomenon that powers the Sun and other stars, involves the fusion of hydrogen isotopes under extreme conditions. The result is the creation of helium, with a release of energy due to the difference in mass between the initial elements and the newly formed helium. 

However, realizing this dream of harnessing fusion energy on Earth necessitates precise control over the choice of hydrogen isotopes used in the process.

Machine learning meets spectroscopy

A recent press release revealed that researchers are now turning to the realm of artificial intelligence to enhance our understanding of nuclear fusion and its potential as a clean energy source. 

A prerequisite for nuclear fusion is to know what mix of hydrogen isotopes to use— a time-consuming analysis currently done with spectroscopy.

In a paper published in The European Physical Journal D, Mohammed Koubiti, an Associate Professor at Aix-Marseille Universite in France, presented a novel approach that combines machine learning with plasma spectroscopy to determine the optimal ratios of hydrogen isotopes for nuclear fusion plasma performance.

Koubiti focuses on the challenge of managing the mix of hydrogen isotopes, particularly deuterium and tritium, within fusion power plants. 

Deuterium and tritium are the preferred isotopes for fusion because of their efficiency, but strict regulatory limits govern the amount of tritium that can be used due to safety concerns. Koubiti aims to address this challenge by introducing machine learning into the equation.

"The ultimate aim is to avoid using spectroscopy, whose analysis is time-consuming, and replace it— or at least combine it— with deep learning to predict tritium contents in fusion plasmas," explained Koubiti.

Although this study represents just the first step towards this goal, Koubiti revealed that he continues to use spectroscopy to identify features that can be employed by deep learning algorithms to predict tritium content as a function of time in fusion plasmas.

A bright future for fusion

The integration of machine learning into nuclear fusion research opens up exciting possibilities. Beyond the immediate application in predicting tritium content, Koubiti envisions extending deep-learning techniques to various magnetic fusion devices, including tokamaks like JET, ASDEX-Upgrade, WEST, DIII-D, and even stellarators. 

Tokamaks, devices that leverage powerful magnetic fields to confine hot plasmas in the shape of a donut, are touted to generate clean fusion power.

Koubiti's vision reveals a determination to identify non-spectroscopic features crucial for deep-learning algorithms to achieve accurate predictions. This approach could hold the potential to transform our understanding of nuclear fusion and accelerate its transition from a theoretical concept to reality.

The world grapples with an urgent need to reduce carbon emissions and combat climate change, a situation in which nuclear science represents a beacon of hope. While challenges remain, Koubiti's pioneering work indicates that innovation and technology will play a pivotal role in shaping a cleaner and more sustainable future.

Study Abstract

Machine learning, a subfield of artificial intelligence, is being increasingly used in physics and other scientific domains for data analysis and predictions. This trend to use machine learning concerns now several plasma physics topics like those related to magnetic fusion. With the ongoing or planned buildings of larger tokamaks like ITER, magnetic fusion is a research field where artificial intelligence techniques can be of a great help. In this short communication, I will discuss in particular the use of machine learning in connection with plasma spectroscopy for the hydrogen isotopic ratio determination. In addition to some preliminary results, I will discuss some ideas and open questions related to predictions of isotopic ratio determination for HD and DT fusion plasmas.

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