Monte Carlo Shielding Analyses Using an Automated Biasing Procedure

Tang, J.S.; Hoffman, Thomas J.

doi:10.13182/nse88-a23562

Cited by 14 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The detector response is calculated simply by taking the inner product of the source and the adjoint flux at that position. Adjoint flux has been used for perturbation and optimization studies in areas such as radiation shielding (Tang and Hoffman 1988), satellite design (Kensek and Lorence 1996), and radiation therapy treatment planning (Difilippo 1998, Jeraj and Keall 1999, Kowalok and Henderson 2001, Yoo et al 2001.…”

Section: Development Of Adjoint Datamentioning

confidence: 99%

Treatment planning for prostate brachytherapy using region of interest adjoint functions and a greedy heuristic

et al. 2003

View full text Add to dashboard Cite

We have developed an efficient treatment-planning algorithm for prostate implants that is based on region of interest (ROI) adjoint functions and a greedy heuristic. For this work, we define the adjoint function for an ROI as the sensitivity of the average dose in the ROI to a unit-strength brachytherapy source at any seed position. The greedy heuristic uses a ratio of target and critical structure adjoint functions to rank seed positions according to their ability to irradiate the target ROI while sparing critical structure ROIs. This ratio is computed once for each seed position prior to the optimization process. Optimization is performed by a greedy heuristic that selects seed positions according to their ratio values. With this method, clinically acceptable treatment plans are obtained in less than 2 s. For comparison, a branch-and-bound method to solve a mixed integer-programming model took more than 50 min to arrive at a feasible solution. Both methods achieved good treatment plans, but the speedup provided by the greedy heuristic was a factor of approximately 1500. This attribute makes this algorithm suitable for intra-operative real-time treatment planning.

show abstract

Automated importance generation and biasing techniques for Monte Carlo shielding techniques by the TRIPOLI-3 code

Both¹,

Nimal²,

Vergnaud³

1990

Progress in Nuclear Energy

View full text Add to dashboard Cite

Monte Carlo Shielding Analyses Using an Automated Biasing Procedure

Cited by 14 publications

References 14 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

Treatment planning for prostate brachytherapy using region of interest adjoint functions and a greedy heuristic

Automated importance generation and biasing techniques for Monte Carlo shielding techniques by the TRIPOLI-3 code

Contact Info

Product

Resources

About