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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Feature extraction for improved disruption prediction analysis at JET.

G A Rattá1, J Vega, A Murari

  • 1Asociación EURATOM/CIEMAT para Fusión, Avda. Complutense 22, 28040 Madrid, Spain.

The Review of Scientific Instruments
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

A new disruption predictor for tokamaks was developed using machine learning on Joint European Torus data. This tool aims to reliably forecast major plasma instabilities in advance.

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Area of Science:

  • Plasma Physics
  • Fusion Energy Engineering

Background:

  • Disruptions are significant instabilities in tokamak devices, posing a major challenge to sustained fusion reactions.
  • Accurate prediction of these events is crucial for developing control strategies and ensuring operational reliability.

Purpose of the Study:

  • To develop and validate computational disruption classifiers for tokamaks.
  • To assess the performance and reliability of these classifiers in predicting disruptions.
  • To determine the lead time achievable for reliable disruption prediction.

Main Methods:

  • Utilized the Joint European Torus (JET) database for disruption data.
  • Employed supervised learning techniques for developing automatic classifiers.
  • Evaluated classifier performance and prediction reliability.

Main Results:

  • Developed accurate automatic classifiers for tokamak disruptions.
  • Demonstrated the capability of these classifiers to operate with acceptable reliability.
  • Quantified the advance warning time achievable before disruption onset.

Conclusions:

  • Supervised learning provides an effective method for developing tokamak disruption predictors.
  • The developed predictor offers reliable advance warning, crucial for mitigating disruption consequences.
  • This work advances the operational safety and efficiency of tokamak fusion devices.