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A Shape and Mesh Adaptive Computational Methodology for Gamma Ray Dose from Volumetric Sources

Indoor external exposure to the population is dominated by gamma rays emitted from the walls and the floor of a room. A shape and mesh size adaptive flux calculational approach has been developed for a typical wall source. Parametric studies of the effect of mesh size on flux calculations have been...

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Bibliographic Details
Published in:Radiation protection dosimetry 1991-09, Vol.38 (4), p.307-314
Main Authors: Mirza, N.M., Ali, B., Mirza, S.M., Tufail, M., Ahmad, N.
Format: Article
Language:English
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Summary:Indoor external exposure to the population is dominated by gamma rays emitted from the walls and the floor of a room. A shape and mesh size adaptive flux calculational approach has been developed for a typical wall source. Parametric studies of the effect of mesh size on flux calculations have been done. The optimum value of the mesh size is found to depend strongly on distance from the source, permissible limits on uncertainty in flux predictions and on computer Central Processing Unit time. These calculations were performed on a VAX-11/730 machine. To test the computations, a typical wall source was reduced to a point, a line and an infinite volume source having finite thickness, and the computed flux values were compared with values from corresponding analytical expressions for these sources. Results indicate that the errors under optimum conditions remain less than 6% for the fluxes calculated from this approach when compared with the analytical values for the point and the line source approximations. Also, when the wall is simulated as an infinite volume source having finite thickness, the errors in computed to analytical flux ratios remain large for smaller wall dimensions. However, the errors become less that 10% when the wall dimensions are greater than ten mean free paths (~120 cm) for 3 MeV gamma rays. Also, specific dose rates from this methodology remain within the difference of 15% for the values obtained by Monte Carlo method.
ISSN:0144-8420
1742-3406
DOI:10.1093/oxfordjournals.rpd.a081106