2026-09-20T13:51:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1125252026-06-17T14:46:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Kord Smith and Benoit Forget.
Boyd, William Robert Dawson, III
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering.
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
2017-12-05T19:16:47Z
2017-12-05T19:16:47Z
2017
2017
http://hdl.handle.net/1721.1/112525
1012939127
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 487-495).
A key challenge for full-core transport methods is reactor agnostic multi-group cross section (MGXS) generation. Monte Carlo (MC) presents the most accurate method for MGXS generation since it does not require any approximations to the neutron flux. This thesis develops novel methods that use MC to generate the fine-spatial mesh MGXS that are needed by high-fidelity transport codes. These methods employ either engineering-based or statistical clustering algorithms to accelerate the convergence of MGXS tallied on fine, heterogeneous spatial meshes by Monte Carlo. The traditional multi-level approach to MGXS generation is replaced by full-core MC calculations that generate MGXS for multi-group deterministic transport codes. Two pinwise spatial homogenization schemes are introduced to model the clustering of pin-wise MGXS due to spatial self-shielding spectral effects. The Local Neighbor Symmetry (LNS) scheme uses a nearest neighbor-like analysis of a reactor geometry to determine which fuel pins should be assigned the same MGXS. The inferential MGXS (iMGXS) scheme applies unsupervised machine learning algorithms to "noisy" MC tally data to identify clustering of pin-wise MGXS without any knowledge of the reactor geometry. Both schemes simultaneously account for spatial self-shielding effects while also accelerating the convergence of the MC tallies used to generate MGXS. The LNS and iMGXS schemes were used to model MGXS clustering from radial geometric heterogeneities in a suite of 2D PWR benchmarks. Both schemes reduced U-238 capture rate errors by up to a factor of four with respect to schemes which neglect to model MGXS clustering. In addition, the schemes required an order of magnitude fewer MC particle histories to converge MGXS for multi-group deterministic calculations than a reference MC calculation. These results demonstrate the potential for single-step MC simulations of the complete heterogeneous geometry as a means to generate reactor agnostic MGXS for deterministic transport codes. The LNS and iMGXS schemes may be valuable for reactor physics analyses of advanced LWR core designs and next generation reactors with spatial heterogeneities that are poorly modeled by the engineering approximations in today's methods for MGXS generation.
by William Robert Dawson Boyd, III.
Ph.D.
495 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Nuclear Science and Engineering.
Reactor agnostic multi-group cross section generation for fine-mesh deterministic neutron transport simulations
Reactor agnostic MGXC generation for fine-mesh deterministic neutron transport simulations
Thesis
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<Abstract>A key challenge for full-core transport methods is reactor agnostic multi-group cross section (MGXS) generation. Monte Carlo (MC) presents the most accurate method for MGXS generation since it does not require any approximations to the neutron flux. This thesis develops novel methods that use MC to generate the fine-spatial mesh MGXS that are needed by high-fidelity transport codes. These methods employ either engineering-based or statistical clustering algorithms to accelerate the convergence of MGXS tallied on fine, heterogeneous spatial meshes by Monte Carlo. The traditional multi-level approach to MGXS generation is replaced by full-core MC calculations that generate MGXS for multi-group deterministic transport codes. Two pinwise spatial homogenization schemes are introduced to model the clustering of pin-wise MGXS due to spatial self-shielding spectral effects. The Local Neighbor Symmetry (LNS) scheme uses a nearest neighbor-like analysis of a reactor geometry to determine which fuel pins should be assigned the same MGXS. The inferential MGXS (iMGXS) scheme applies unsupervised machine learning algorithms to "noisy" MC tally data to identify clustering of pin-wise MGXS without any knowledge of the reactor geometry. Both schemes simultaneously account for spatial self-shielding effects while also accelerating the convergence of the MC tallies used to generate MGXS. The LNS and iMGXS schemes were used to model MGXS clustering from radial geometric heterogeneities in a suite of 2D PWR benchmarks. Both schemes reduced U-238 capture rate errors by up to a factor of four with respect to schemes which neglect to model MGXS clustering. In addition, the schemes required an order of magnitude fewer MC particle histories to converge MGXS for multi-group deterministic calculations than a reference MC calculation. These results demonstrate the potential for single-step MC simulations of the complete heterogeneous geometry as a means to generate reactor agnostic MGXS for deterministic transport codes. The LNS and iMGXS schemes may be valuable for reactor physics analyses of advanced LWR core designs and next generation reactors with spatial heterogeneities that are poorly modeled by the engineering approximations in today's methods for MGXS generation.</Abstract>
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