Neural network implementation for ITER neutron emissivity profile recognition

被引：3

作者：

Cecconello, M. ^{[1
]}

Conroy, S. ^{[1
]}

Marocco, D. ^{[2
]}

Moro, F. ^{[2
]}

Esposito, B. ^{[2
]}

机构：

[1] Uppsala Univ, EURATOM VR Assoc, Dept Phys & Astron, Uppsala, Sweden

[2] ENEA, CR Frascati, Via E Fermi 45, I-00044 Rome, Italy

来源：

FUSION ENGINEERING AND DESIGN | 2017年 / 123卷

关键词：

ITER; RNC; Neural network; Real time; Fusion power;

D O I：

10.1016/j.fusengdes.2017.02.058

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

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. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：637 / 640

页数：4

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