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Title: The method of modular characteristic direction probabilities in MPACT

Abstract

The method of characteristic direction probabilities (CDP) is based on a modular ray tracing technique which combines the benefits of the collision probability method (CPM) and the method of characteristics (MOC). This past year CDP was implemented in the transport code MPACT for 2-D and 3-D transport calculations. By only coupling the fine mesh regions passed by the characteristic rays in the particular direction, the scale of the probabilities matrix is much smaller compared to the CPM. At the same time, the CDP has the same capacity of dealing with the complicated geometries with the MOC, because the same modular ray tracing techniques are used. Results from the C5G7 benchmark problems are given for different cases to show the accuracy and efficiency of the CDP compared to MOC. For the cases examined, the CDP and MOC methods were seen to differ in k{sub eff} by about 1-20 pcm, and the computational efficiency of the CDP appears to be better than the MOC for some problems. However, in other problems, particularly when the CDP matrices have to be recomputed from changing cross sections, the CDP does not perform as well. This indicates an area of future work. (authors)

Authors:
 [1]; ; ;  [2];  [1]
  1. School of Nuclear Science and Technology, Xi'an Jiaotong University, No. 28 Xianning west road, Xi'an, Shaanxi 710049 (China)
  2. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, 2200 Bonisteel, Ann Arbor, MI 48109 (United States)
Publication Date:
Research Org.:
American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
OSTI Identifier:
22212845
Resource Type:
Conference
Resource Relation:
Conference: M and C 2013: 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, Sun Valley, ID (United States), 5-9 May 2013; Other Information: Country of input: France; 7 refs.; Related Information: In: Proceedings of the 2013 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering - M and C 2013| 3016 p.
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; ACCURACY; COLLISION PROBABILITY METHOD; COMPARATIVE EVALUATIONS; CROSS SECTIONS; GEOMETRY; MATRICES; PROBABILITY

Citation Formats

Liu, Z., Kochunas, B., Collins, B., Downar, T., and Wu, H. The method of modular characteristic direction probabilities in MPACT. United States: N. p., 2013. Web.
Liu, Z., Kochunas, B., Collins, B., Downar, T., & Wu, H. The method of modular characteristic direction probabilities in MPACT. United States.
Liu, Z., Kochunas, B., Collins, B., Downar, T., and Wu, H. Mon . "The method of modular characteristic direction probabilities in MPACT". United States.
@article{osti_22212845,
title = {The method of modular characteristic direction probabilities in MPACT},
author = {Liu, Z. and Kochunas, B. and Collins, B. and Downar, T. and Wu, H.},
abstractNote = {The method of characteristic direction probabilities (CDP) is based on a modular ray tracing technique which combines the benefits of the collision probability method (CPM) and the method of characteristics (MOC). This past year CDP was implemented in the transport code MPACT for 2-D and 3-D transport calculations. By only coupling the fine mesh regions passed by the characteristic rays in the particular direction, the scale of the probabilities matrix is much smaller compared to the CPM. At the same time, the CDP has the same capacity of dealing with the complicated geometries with the MOC, because the same modular ray tracing techniques are used. Results from the C5G7 benchmark problems are given for different cases to show the accuracy and efficiency of the CDP compared to MOC. For the cases examined, the CDP and MOC methods were seen to differ in k{sub eff} by about 1-20 pcm, and the computational efficiency of the CDP appears to be better than the MOC for some problems. However, in other problems, particularly when the CDP matrices have to be recomputed from changing cross sections, the CDP does not perform as well. This indicates an area of future work. (authors)},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {7}
}

Conference:
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