Direct Monte Carlo simulation of scattering processes of keV conversion electrons in a radioactive source

Author(s):  
Antonín Špalek
2020 ◽  
Author(s):  
Takakiyo Tsujiguchi ◽  
Yoko Suzuki ◽  
Mizuki Sakamoto ◽  
Kazuki Narumi ◽  
Katsuhiro Ito ◽  
...  

Abstract Emergency medical responders (EMRs), who save victims in a radiation emergency, are at risk of radiation exposure. In this study, the exposure dose to EMRs assisting contaminated patients was estimated using a Monte Carlo simulation, and will produce data that contributes to EMR education and anxiety reduction. Using the Monte Carlo simulation, we estimated radiation doses for adult computational phantoms with radioactive contamination conditions radiation dosages were based on findings from previous studies. At the contamination condition corresponding to the typical upper limit of general GM survey meters, the radiation doses of EMRs were estimated to be less than μSv per hour. In case of a heavier contamination due to mishandling of an intense radioactive source with hundreds of GBq or more, their radiation doses would be close to 100 mSv per hour. The results have implied that the radiological accident with a highly radioactive source would expose EMR to the risk of significant radiation exposure exceeding the dose limit. It is thus crucial that the authority or other party who are responsible for the health of EMRs ensures that they shall have necessary education and training on the effective measures for protecting themselves from the possible, excessive radiation exposure.


1997 ◽  
Vol 119 (3) ◽  
pp. 368-374 ◽  
Author(s):  
S. Charles Liu ◽  
S. Jack Hu

Traditional variation analysis methods, such as Root Sum Square method and Monte Carlo simulation, are not applicable to sheet metal assemblies because of possible part deformation during the assembly process. This paper proposes the use of finite element methods (FEM) in developing mechanistic variation simulation models for deformable sheet metal parts with complex two or three dimensional free form surfaces. Mechanistic variation simulation provides improved analysis by combining engineering structure models and statistical analysis in predicting the assembly variation. Direct Monte Carlo simulation in FEM is very time consuming, because hundreds or thousands of FEM runs are required to obtain a realistic assembly distribution. An alternative method, based on the Method of Influence Coefficients, is developed to improve the computational efficiency, producing improvements by several orders of magnitude. Simulations from both methods yield almost identical results. An example illustrates the developed methods used for evaluating sheet metal assembly variation. The new approaches provide an improved understanding of sheet metal assembly processes.


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