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Neural network implementation for ITER neutron emissivity profile recognition
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
ENEA, CR Frascati, Via E Fermi 45, I-00044 Rome, Italy..
ENEA, CR Frascati, Via E Fermi 45, I-00044 Rome, Italy..
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2017 (English)In: Fusion engineering and design, ISSN 0920-3796, E-ISSN 1873-7196, Vol. 123, p. 637-640Article in journal (Refereed) Published
Abstract [en]

The ITER Radial Neutron Camera (RNC) is a neutron diagnostic intended for the measurement of the neutron emissivity radial profile and the estimate of the total fusion power. This paper presents a proof of-principle method based on neural networks to estimate the neutron emissivity profile in different ITER scenarios and for different RNC architectures. The design, optimization and training of the implemented neural network is presented together with a decision algorithm to select, among the multiple trained neural networks, which one provides the inverted neutron emissivity profile closest to the input one. Examples are given for a selection of ITER scenarios and RNC architectures. The results from this study indicate that neural networks for the neutron emissivity recognition in ITER can achieve an accuracy and precision within the spatial and temporal requirements set by ITER for such a diagnostic.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE SA , 2017. Vol. 123, p. 637-640
Keywords [en]
ITER, RNC, Neural network, Real time, Fusion power
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-341820DOI: 10.1016/j.fusengdes.2017.02.058ISI: 000418992000132OAI: oai:DiVA.org:uu-341820DiVA, id: diva2:1183103
Conference
29th Symposium on Fusion Technology (SOFT), SEP 05-09, 2016, Prague, CZECH REPUBLIC
Funder
Swedish Research Council, VR 826-2012-5116Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-02-15Bibliographically approved

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Cecconello, MarcoConroy, Sean

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