Methods of Monte Carlo biasing using two-dimensional discrete ordinates adjoint flux

Tang, J.S.; Hoffman, Thomas J.; Stevens, P.N.

doi:10.2172/7362794

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…One of the earliest literatures in this area by Tang [46], [47] Given the weight-window maps for the two responses, the total computational time with the brute force method is equal to the sum of the times required to separately reduce the variance for each detector to the desired level, e.g., 4%…”

Section: Monte-carlo-deterministic Hybrid Methodsmentioning

confidence: 99%

Development of SUBSPACE-Based Hybrid Monte Carlo-Deterministic Algorithms for Reactor Physics Calculations

Zhang

Abdel-Khalik

2013

Volume 6: Beyond Design Basis Events; Student Paper Competition

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This paper presents an innovative hybrid Monte-Carlo-Deterministic method denoted by the SUBSPACE method designed for improving the efficiency of hybrid methods for reactor analysis applications. The SUBSPACE method achieves its high computational efficiency by taking advantage of the existing correlations between desired responses. Recently, significant gains in computational efficiency have been demonstrated using this method for source driven problems. Within this work the mathematical theory behind the SUBSPACE method is introduced and the performance is demonstrated based on a fixed-source problem. Furthermore, the SUBSPACE method has been successfully extended to address core wide level k-eigenvalue problems. The method’s efficiency is demonstrated based on a three-dimensional quarter-core problem, where responses are sought on the pin cell level. The SUBSPACE method is compared to the FW-CADIS method and is found to be more efficient for the utilized test problem because of the reason that the FW-CADIS (Forward Weighted Consistent Adjoint Driven Importance Sampling) method solves a forward eigenvalue problem and an adjoint fixed-source problem while the SUBSPACE method only solves an adjoint fixed-source problem. Based on the favorable results obtained here, we are confident that the applicability of Monte Carlo for large scale reactor analysis could be realized in the near future.

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Section: Monte-carlo-deterministic Hybrid Methodsmentioning

confidence: 99%

Development of SUBSPACE-Based Hybrid Monte Carlo-Deterministic Algorithms for Reactor Physics Calculations

Zhang

Abdel-Khalik

2013

Volume 6: Beyond Design Basis Events; Student Paper Competition

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“…Carlo calculations [6] using importance parameters determined from adjoint 2-D S N calculations. This work follows from a 1976 technical report by the same authors [7]. In both documents the authors perform a cylindrical straight-duct streaming analysis through a concrete shield using various combinations of source, importance-splitting and rouletting, exponential-transform, and collision-emergence biasing.…”

Section: Tang Hoffman and Stevensmentioning

confidence: 99%

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

2023

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I appreciate Ms. Peggy Jo Gramer's and Dr. Garnette Roberts's ceaseless work to ensure that all needs, academic and personal, were addressed during my time as a doctoral student.My gratitude also goes to mentors from earlier in my career, Mr. Stan Anderson, Mr. Arnie Fero, and Dr. Gianluca Longoni, for instilling strong engineering principles and for encouraging me to follow a path that lead me to this point.

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“…These approximations are often based on deterministic methods such as discrete ordinates calculations using a simplified geometry [Tang et al 1976]. These approximations are often based on deterministic methods such as discrete ordinates calculations using a simplified geometry [Tang et al 1976].…”

Section: Importance Samplingmentioning

confidence: 99%

Monte Carlo Simulation of Neutral Particle Transport

Dunn¹,

Shultis²

2012

Exploring Monte Carlo Methods

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Methods of Monte Carlo biasing using two-dimensional discrete ordinates adjoint flux

Cited by 9 publications

References 9 publications

Development of SUBSPACE-Based Hybrid Monte Carlo-Deterministic Algorithms for Reactor Physics Calculations

Development of SUBSPACE-Based Hybrid Monte Carlo-Deterministic Algorithms for Reactor Physics Calculations

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo Simulation of Neutral Particle Transport

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