Efficiency analysis of multiple detector effects on MCNP 6.2 simulations


AKÇALI Ö. , TOKER O. , Bilmez B., İÇELLİ O.

Progress in Nuclear Energy, vol.139, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 139
  • Publication Date: 2021
  • Doi Number: 10.1016/j.pnucene.2021.103892
  • Title of Journal : Progress in Nuclear Energy
  • Keywords: MCNP, Variance reduction, Radiation transport, Monte Carlo, Simplified simulation method, AUTOMATED VARIANCE REDUCTION, TRANSPORT

Abstract

© 2021 Elsevier LtdAn analysis of some variance reduction methods was introduced and compared with the conventional narrow beam geometry for radiation transport problems. Although the Monte Carlo methods provide accurate results in radiation transport problems, their usability requires a high processing infrastructure. Simulation algorithms need miscellaneous mathematical tricks named as variance reduction to achieve precise results with reduced computing resources. These techniques mostly require experience, intuition, and iteration. For this reason, an alternative approach that may be adopted by all users is needed. Proposed method using source-biased quadruplet and octuplet detection geometries need neither additional code nor advanced experience. The designed geometries were tested on various selected materials, energy values and source definitions to prove its availability. The accuracy and the stability of the method were analyzed with all possible parameters such as material and energy dependence (tasks-error/CPU time, detector fluctuation-NPS, relative error-NPS, deviation-NPS, etc.). Besides, to verify the analysis results, some radiography applications were performed. The efficiency of multiple detector usage was found to be dependent on the complexity of the geometry as expected. It may be concluded that the multi-detector usage is efficient for simplified geometries in terms of relative error and computing time. The firstly introduced geometric designs worked steadily over ~10M histories on all trials. For the advised design, up to ~7 times improvement achieved in terms of computing time based on the figure of merit.