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Michela Gelfusa

Researcher at University of Rome Tor Vergata

Publications -  180
Citations -  1860

Michela Gelfusa is an academic researcher from University of Rome Tor Vergata. The author has contributed to research in topics: Jet (fluid) & Computer science. The author has an hindex of 18, co-authored 154 publications receiving 1464 citations. Previous affiliations of Michela Gelfusa include Sapienza University of Rome & University of Nice Sophia Antipolis.

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Overview of the JET results in support to ITER

X. Litaudon, +1228 more
- 15 Jun 2017 - 
TL;DR: In this paper, the authors reviewed the 2014-2016 JET results in the light of their significance for optimising the ITER research plan for the active and non-active operation, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric.
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Overview of the JET results

Francesco Romanelli, +1104 more
- 27 Mar 2015 - 
TL;DR: In this paper, a detailed analysis of the plasma-facing components of the day-one tungsten divertor in ITER-like wall has been carried out, showing that the pattern of deposition within the divertor has changed significantly with respect to the JET carbon wall campaigns due to the absence of thermally activated chemical erosion of beryllium in contrast to carbon.
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Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

E. Joffrin, +1245 more
- 01 Nov 2019 - 
TL;DR: In this article, a detailed review of the physics basis for the DTE2 operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of DTE plasmas (thermal and particle transport, high confinement mode, Be and W erosion, fuel recovery, etc).
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Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions

TL;DR: A new clustering method,based on the geodesic distance on a probabilistic manifold, has been applied to the JET disruption database and has proved to clearly outperform the more traditional classification methods based on the Euclidean distance.
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Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

TL;DR: A new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data, which give the probability of disruption and improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics.