Uso de Métodos Monte Carlo na Modelagem de uma Nuvem Radioativa Natural

Gutemberg Luiz Sales Claudino; José Wilson Vieira; Viriato Leal Neto; Fernando Roberto Andrade Lima; Alisson Fernando Silva; Joana Caroline Lopes Soares

doi:10.15392/bjrs.v3i1A.154

Autores/as

Gutemberg Luiz Sales Claudino Universidade Potiguar - UnP
José Wilson Vieira Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco - IFPE
Viriato Leal Neto Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco - IFPE
Fernando Roberto Andrade Lima CRCN-NE
Alisson Fernando Silva
Joana Caroline Lopes Soares

DOI:

https://doi.org/10.15392/bjrs.v3i1A.154

Palabras clave:

Natural Radiation, Cloud Radioactive, Exposure Computational Models

Resumen

The level of natural radiation in some regions of Brazil, and the world, have become high. One of the places that have these high levels are underground mines. At these locations, the radiation may come from the ground, the walls, and the environment in the form of gases. The Group of Numerical Dosimetry (GDN), already developed, and utilizes various of Computational Models Exposure (MCE) to simulate situations where people are exposed to ionizing radiation. In one study, the GDN utilized the MCEs to to simulate the soil contamination by natural elements.This work aims to continue the study of contamination by natural elements, taking into account the energy interval range of gaseous natural elements. For this, the algorithm developed by GDN in 2004, was upgraded to simulate the irradiation of a person in the standing position, where its craniocaudal axis coincides with the axis of a cylinder circumscribing the body, where they emerge, isotropically, photonsThis update was done in the model MSTA (Mash Standard) To describe this situation we used a MCE composed of the font algorithm described by Vieira the phantom MASH and the MC code EGSnrcThe simulation results were organized in formats absorbed dose / air Kerma. These data may be used for other situations where the user that knows the energies, and their abundances, can interpolate the data to obtain new results without the need to conduct new simulations.

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