Research on the Artificial Neural Network Unfolding Method for the Water-Pumping-Injection Multi-Homocentric Sphere Neutron Spectrometer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Kang Chang, Yi Cheng, Jianbo Yang, Yujie Qiao, Rui Li, Can Zhang

ABSTRACT

This work aimed to study an algorithm for the unfolding spectrum acquired by using a water-pumping-injection multi-homocentric sphere neutron spectrometer. The readings of the water-pumping-injection multi-homocentric sphere neutron spectrometer under 32 different neutron sources, including 241Am-Be spontaneous, 238Pu Watt, and 232Th Maxwell fission, were obtained by using the FLUKA Monte Carlo code and were input as the neural network training values for constructing the method of back-propagation artificial neural network neutron spectrum unfolding (MBANSU). Two neutron readings that were Monte Carlo experimentally measured from 252Cf spontaneous and 245Cm Maxwell fission sources were used as the testing values for MBANSU. The calculated neutron energy spectrum was in good agreement with the ENDF energy spectrum, and the information entropy of the result spectrum approximated that of the ENDF spectrum. Results indicate that the established MBANSU algorithm has potential applications for unfolding spectra collected by using the water-pumping-injection multi-homocentric sphere neutron spectrometer. More... »

PAGES

542-546

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.3938/jkps.74.542

DOI

http://dx.doi.org/10.3938/jkps.74.542

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112965323


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