Literature DB >> 26325583

Monte Carlo simulation of moderator and reflector in coal analyzer based on a D-T neutron generator.

Qing Shan1, Shengnan Chu2, Wenbao Jia3.   

Abstract

Coal is one of the most popular fuels in the world. The use of coal not only produces carbon dioxide, but also contributes to the environmental pollution by heavy metals. In prompt gamma-ray neutron activation analysis (PGNAA)-based coal analyzer, the characteristic gamma rays of C and O are mainly induced by fast neutrons, whereas thermal neutrons can be used to induce the characteristic gamma rays of H, Si, and heavy metals. Therefore, appropriate thermal and fast neutrons are beneficial in improving the measurement accuracy of heavy metals, and ensure that the measurement accuracy of main elements meets the requirements of the industry. Once the required yield of the deuterium-tritium (d-T) neutron generator is determined, appropriate thermal and fast neutrons can be obtained by optimizing the neutron source term. In this article, the Monte Carlo N-Particle (MCNP) Transport Code and Evaluated Nuclear Data File (ENDF) database are used to optimize the neutron source term in PGNAA-based coal analyzer, including the material and shape of the moderator and neutron reflector. The optimized targets include two points: (1) the ratio of the thermal to fast neutron is 1:1 and (2) the total neutron flux from the optimized neutron source in the sample increases at least 100% when compared with the initial one. The simulation results show that, the total neutron flux in the sample increases 102%, 102%, 85%, 72%, and 62% with Pb, Bi, Nb, W, and Be reflectors, respectively. Maximum optimization of the targets is achieved when the moderator is a 3-cm-thick lead layer coupled with a 3-cm-thick high-density polyethylene (HDPE) layer, and the neutron reflector is a 27-cm-thick hemispherical lead layer.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Coal; Moderator; Neutron reflector; PGNAA; Ratio of the thermal neutron to fast neutron

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Year:  2015        PMID: 26325583     DOI: 10.1016/j.apradiso.2015.08.029

Source DB:  PubMed          Journal:  Appl Radiat Isot        ISSN: 0969-8043            Impact factor:   1.513


  1 in total

1.  Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network.

Authors:  Mona Zolfaghari; S Farhad Masoudi; Faezeh Rahmani; Atefeh Fathi
Journal:  Sci Rep       Date:  2022-05-23       Impact factor: 4.996

  1 in total

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