MLfusion

Machine Learning for Disruption Prediction in Tokamaks

Started
May 1, 2023
Status
In Progress
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Abstract

Tokamak machines are promising technology for the ever-growing demand for clean energy. They confine plasma in a torus-shaped chamber in order to maintain prolonged fusion reactions. For Tokamaks' successful operation a critical phenomenon, known as disruptions (a catastrophic loss of plasma control), has to be avoided for the continuation of the fusion reaction and machine-damage mitigation. This project aims at leveraging Machine Learning models combined with efficient Bayesian Inference algorithms to detect the onset of plasma disruptions early on. For this purpose models shall project the high-dimensional sensor data, acquired during Tokamak operation, into a reduced latent space representation unveiling operation boundaries separating non-disruptive from disruptive regimes.  Non-stationary models with the objective of tracking the transitions between different plasma regimes will be employed to track the versatile dynamics. This is important to inform the control system to avoid a disruption.

The capability of the model to generalize to unseen (or partially seen) domains is a critical issue that future large-scale reactors such as ITER must address before high-performance operation phases. At the second stage of the project, we wish to improve the generalization capabilities of the proposed models incorporating domain invariant prior knowledge extracted by the analysis of the physics mechanisms leading to disruption in different tokamaks and quantify the extrapolation uncertainty of the proposed model using a multi-machine database from three European tokamaks (JET, TCV, and AUG).

People

Collaborators

SDSC Team:
Christian Donner
Giulio Romanelli
Guillaume Obozinski

PI | Partners:

EPFL, Swiss Plasma Center:

  • Prof. Olivier Sauter
  • Dr. Alessandro Pau
  • Yoeri Poels
  • Cristina Venturini

More info

description

Motivation

Tokamaks are devices that confine plasma allowing for fusion reaction. However, frequently fusion reaction is hampered by so-called disruptions - a sudden and uncontrolled loss of the plasma current and confinement, inducing severe thermal loads on plasma-facing components and large electromagnetic forces on the conductive structures surrounding the plasma. This can give rise to unacceptable damages, making the avoidance and mitigation (reduction of the detrimental effects) of disruptions, a critical need for the success of fusion energy in next-step fusion devices. Hence, it is crucial to design early warning strategies, that allow controlled shutdown, before a disruption occurs.

Proposed Approach / Solution

We attempt to learn data-driven approaches, that provide a real-time estimate of the disruption probability at any time. For this we use time-series models that learn dynamics in a latent space, which is also predictive for the danger of disruptions.

Impact

The resulting algorithm helps to predict disruption early and thereby prevent them, which is crucial for the new generation of fusion devices such as ITER, where disruption can cause severe damage to the machine.

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Annexe

Additional resources

Bibliography

  1. Pau, A., Fanni, A., Carcangiu, S., Cannas, B., Sias, G., Murari, A., & Rimini, F. (2019). A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET. Nuclear Fusion, 59(10), 106017. DOI: 10.1088/1741-4326/ab2ea9
  2. Degrave, J., Felici, F., Buchli, J., Neunert, M., Tracey, B., Carpanese, F., ... & Riedmiller, M. (2022). Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 602(7897), 414-419. DOI: 10.1038/s41586-021-04301-9

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