Simulating variable source problems via post processing of individual particle tallies

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Monte Carlo is an extremely powerful method of simulating complex, three dimensional environments without excessive problem simplification. However, it is often time consuming to simulate models in which the source can be highly varied. Similarly difficult are optimization studies involving sources in which many input parameters are variable, such as particle energy, angle, and spatial distribution. Such studies are often approached using brute force methods or intelligent guesswork. One field in which these problems are often encountered is accelerator-driven Boron Neutron Capture Therapy (BNCT) for the treatment of cancers. Solving the reverse problem of determining the best neutron source for ... continued below

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Bleuel, D.L.; Donahue, R.J.; Ludewigt, B.A. & Vujic, J. October 20, 2000.

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Description

Monte Carlo is an extremely powerful method of simulating complex, three dimensional environments without excessive problem simplification. However, it is often time consuming to simulate models in which the source can be highly varied. Similarly difficult are optimization studies involving sources in which many input parameters are variable, such as particle energy, angle, and spatial distribution. Such studies are often approached using brute force methods or intelligent guesswork. One field in which these problems are often encountered is accelerator-driven Boron Neutron Capture Therapy (BNCT) for the treatment of cancers. Solving the reverse problem of determining the best neutron source for optimal BNCT treatment can be accomplished by separating the time-consuming particle-tracking process of a full Monte Carlo simulation from the calculation of the source weighting factors which is typically performed at the beginning of a Monte Carlo simulation. By post-processing these weighting factors on a recorded file of individual particle tally information, the effect of changing source variables can be realized in a matter of seconds, instead of requiring hours or days for additional complete simulations. By intelligent source biasing, any number of different source distributions can be calculated quickly from a single Monte Carlo simulation. The source description can be treated as variable and the effect of changing multiple interdependent source variables on the problem's solution can be determined. Though the focus of this study is on BNCT applications, this procedure may be applicable to any problem that involves a variable source.

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INIS; OSTI as DE00782537

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  • Monte Carlo 2000 Conference, Lisbon (PT), 10/23/2000--10/26/2000

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  • Report No.: LBNL--47733
  • Grant Number: AC03-76SF00098
  • Office of Scientific & Technical Information Report Number: 782537
  • Archival Resource Key: ark:/67531/metadc718312

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  • October 20, 2000

Added to The UNT Digital Library

  • Sept. 29, 2015, 5:31 a.m.

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  • April 4, 2016, 2:24 p.m.

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Bleuel, D.L.; Donahue, R.J.; Ludewigt, B.A. & Vujic, J. Simulating variable source problems via post processing of individual particle tallies, article, October 20, 2000; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc718312/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.