Compressed Sensing Artificial Neural Network for Reactor Core Flux Mapping

被引:6
作者
Bahuguna, S. K. [1 ]
Mukhopadhyay, S. [2 ,3 ]
Tiwari, A. P. [2 ,3 ]
机构
[1] Homi Bhabha Natl Inst, Bombay 400094, Maharashtra, India
[2] Bhabha Atom Res Ctr, Elect & Instrumentat Grp, Bombay 400085, Maharashtra, India
[3] Homi Bhabha Natl Inst, Dept Engn & Sci, Bombay 400094, Maharashtra, India
关键词
Advanced heavy water reactor (AHWR); artificial neural network (ANN); basis pursuit denoising (BPDN); compressed sensing (CS); flux synthesis method (FSM); incoherence; self-powered neutron detectors (SPNDs); sparsity; LEAST-SQUARES METHOD; CANDU-PHWR; DESIGN; AHWR;
D O I
10.1109/TNS.2018.2854667
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a novel technique of signal recovery from sparse measurements based on compressed sensing (CS) and artificial neural network (ANN) and its application to core flux mapping in Advanced Heavy Water Reactor (AHWR). In large size nuclear reactors, neutron flux distribution undergoes continuous variation due to routine perturbations, nonuniform burn up at different locations, xenon oscillation, etc. An online flux mapping system (FMS) is needed to continuously monitor neutron flux distribution and display to the operator. FMS employs a suitable algorithm to estimate the core flux distribution from the measurements of only a few in-core detectors. The proposed method called CS-ANN methodology first uses CS technique for flux mapping along the vertical direction at the detector housing locations (1-D) and subsequently ANN technique in several horizontal planes (2-D) for estimating the 3-D neutron flux profile under different operating conditions. Error in the estimation using the proposed CS-ANN methodology has been compared with other existing methods and found to be significantly lower.
引用
收藏
页码:2240 / 2249
页数:10
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