Literature DB >> 31154075

Analysis by Monte Carlo of thermal neutron flux from a 241Am/9Be source for a system of trace analysis in materials.

Lenin E Cevallos-Robalino1, Gonzalo F García-Fernández2, Alfredo Lorente2, Eduardo Gallego2, Hector Rene Vega-Carrillo3, Karen A Guzmán-Garcia2.   

Abstract

Neutron techniques to characterize materials have a wide range of applications, one of the major developments being the identification of terrorist threats with chemical, biological, radiological, nuclear and explosives (CBRNE) materials. In this work, a thermal neutron irradiation system, using a241Am/9Be source of 111 GBq inside polyethylene cylindrical moderators, has been designed, built and tested. The geometry of moderator and the neutron source position were fixed trying to maximize the thermal neutrons flux emitted from the system. Therefore, the system is in fact a thermalized neutron source taking advantage of the backscattered neutrons, achieving thermal fluence rates of up to 5.3x102 cm-2 s-1, with dominantly thermal spectra. Samples can be placed there for several hours and thereafter be measured to identify their component elements by NAA (Neutron Activation Analysis). Through Monte Carlo techniques employing the MCNP6 code (Pelowitz et al., 2014), four different configurations with polyethylene cylinders were simulated to choose the most adequate geometry. The theoretical model was then replicated in the neutronics hall of the Neutron Measurements Laboratory of the Energy Engineering Department of Universidad Politécnica de Madrid (LMN-UPM), carrying out experimental measurements using a BF3 neutron detector. A high agreement between MCNP6 results and the experimental values measured was observed. Consequently, the system developed could be employed in future laboratory experiments, both for the identification of trace substances by NAA and for the calibration of neutron detection equipment.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  (241)Am/(9)Be neutron source; MCNP6; NAA; Trace detection

Year:  2019        PMID: 31154075     DOI: 10.1016/j.apradiso.2019.04.041

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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