Variance reduction method based on adjoint discrete ordinate

Zheng Zheng; Ding Qianxue; Zhou Yan

doi:10.11884/HPLPB201830.170223

Volume 30 Issue 2

Feb. 2018

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Article Navigation > High Power Laser and Particle Beams > 2018 > 30(2): 026004

Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223

Citation:

Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223

Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223

Citation:

Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223

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Variance reduction method based on adjoint discrete ordinate

doi: 10.11884/HPLPB201830.170223

Shanghai Nuclear Engineering Research and Design Institute Ltd Co, Shanghai 200233, China

Received Date: 2017-06-20
Rev Recd Date: 2017-08-20
Publish Date: 2018-02-15

Abstract

Abstract

For deep-penetration shielding calculation, Monte Carlo method (MC method) requires modeling a great number of particles to obtain reliable results, thus huge computation time is the main problem of the MC method. Source biasing and weight window technique effectively decrease the tally error of deep penetration problem. This paper studies the variance reduction (VR) method based on adjoint Discrete Ordinate (SN), generates source biasing factors and weight window parameters for the MC method by using the adjoint fluence rates of the SN method, and develops source sampling subroutine for JMCT. The VR method was verified at phaseⅠ of Qinshan Nuclear Power Plant measurements. It was applied to CAP1400 pressure vessel fast neutron fluence rate and cavity neutron and photon dose rate calculations. Numerical results show that the VR method based on SN increases calculation efficiency by 1~2 orders for deep-penetration shielding calculation with high precision compared with unbiased MC method.
- deep-penetration,
- discrete ordinate method,
- Monte Carlo method,
- source biasing,
- weight window

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References(9)

References

[1]	Wagner J C. Acceleration of Monte Carlo shielding calculations with an automated variance reduction technique and parallel processing[R]. University Park, 1997.
[2]	Wagner J C, Haghighat A. Automated variance reduction of Monte Carlo shielding calculations using the discrete ordinates adjoint function[J]. Nuclear Science and Engineering, 1998, 128: 186-208. doi: 10.13182/NSE98-2
[3]	Haghighat A, Wagner J C. Monte Carlo variance reduction with deterministic importance functions[J]. Progress in Nuclear Energy, 2003, 42(1): 25-53. doi: 10.1016/S0149-1970(02)00002-1
[4]	Blakeman E D, Peplow D E, Wagner J C, et al. PWR facility dose modeling using MCNP5 and the CADIS/ADVANTG variance-reduction methodology[R]. ORNL/TM-2007/133, 2007.
[5]	Peplow D E, Evans T M, Wagner J C. Simultaneous optimization of tallies in difficult shielding problems[J]. Nuclear Technology, 2008, 168: 785-792.
[6]	Wagner J C, Peplow P E, Evans T M. Automated variance reduction applied to nuclear well-logging problems[J]. Nuclear Technology, 2009, 168: 799-809. doi: 10.13182/NT09-A9309
[7]	Vasiliev A, Ferroukhi H, Zimmermann M A, et al. Development of a CASMO-4/SIMULATE-3/MCNPX calculation scheme for PWR fast neutron fluence analysis and validation against RPV scraping test data[J]. Annals of Nuclear Energy, 2007, 34: 615-627. doi: 10.1016/j.anucene.2007.02.020
[8]	Fero A H. SORCERY user manual[R]. LTR- R EA-06-74, 2006.
[9]	X-5 Monte Carlo Team, MCNP - Ageneral Monte Carlo N-Particle transport code[R]. LA-UR-03-1987, 2003.