Stochastic modeling of a neutron imaging center at the Brazilian Multipurpose Reactor

TL;DR

This paper models the neutron imaging instrument at Brazil's upcoming Multipurpose Reactor using stochastic Monte Carlo simulations, analyzing its sensitivity and comparing it with international facilities to guide future enhancements.

Contribution

It introduces a stochastic Monte Carlo model for the Brazilian neutron imaging instrument and evaluates its performance relative to existing international systems.

Findings

Model successfully simulates neutron imaging parameters

Sensitivity analysis of L/D ratio and neutron flux conducted

Results indicate potential for future instrument improvements

Abstract

Neutron imaging is a non-destructive technique for analyzing a wide class of samples, such as archaeological or industrial material structures. In recent decades, technological advances have had a great impact on the neutron imaging technique, which has meant an evolution from simple radiographs using films (2D) to modern tomography systems with digital processing (3D). The 5 MW research nuclear reactor IEA-R1, which is located at the Instituto de Pesquisas Energ\'eticas e Nucleares (IPEN) in Brazil, possesses a neutron imaging instrument with $1.0 \times 10^{6}$ $n/cm^{2}s$ in the sample position. IEA-R1 is over 60 years old and the future of neutron science in Brazil, including imaging, will be expanded to a new facility called the Brazilian Multipurpose Reactor (RMB, Portuguese acronym), which will be built soon. The new reactor will house a suite of instruments at the Neutron National Laboratory, including the neutron imaging facility, viz., Neinei. Inspired by recent author's works, we model the Neinei instrument through stochastic Monte Carlo simulations. We investigate the sensitivity of the neutron imaging technique parameter ($L/D$ ratio) with the neutron flux, and the results are compared to data from the Neutra (PSI), Antares (FRM II), BT2 (NIST) and DINGO (OPAL) instruments. The results are promising and provide avenues for future improvements.