Improved algorithms and coupled neutron-photon transport for auto-importance sampling method

Xin Wang; Jun-Li Li; Zhen Wu; Rui Qiu; Chun-Yan Li; Man-Chun Liang; Hui Zhang; Zhi Gang; Hong Xu

doi:10.1088/1674-1137/41/1/014103

Chinese Physics C> 2017, Vol. 41> Issue(1) : 014103 DOI: 10.1088/1674-1137/41/1/014103

Improved algorithms and coupled neutron-photon transport for auto-importance sampling method

Xin Wang ^1,2 ,
Jun-Li Li ^1,2 ,
Zhen Wu ^1,3 ,
Rui Qiu ^1,2,, ,
Chun-Yan Li ^1,3 ,
Man-Chun Liang ¹ ,
Hui Zhang ^1,2 ,
Zhi Gang ⁴ ,
Hong Xu ⁴

1.
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
2.
Key Laboratory of Particle &
3.
Nuctech Company Limited, Beijing 100084, China
4.
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
5.
State Nuclear Hua Qing(Beijing) Nuclear Power Technology R &

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Abstract：
The Auto-Importance Sampling (AIS) method is a Monte Carlo variance reduction technique proposed for deep penetration problems, which can significantly improve computational efficiency without pre-calculations for importance distribution. However, the AIS method is only validated with several simple examples, and cannot be used for coupled neutron-photon transport. This paper presents improved algorithms for the AIS method, including particle transport, fictitious particle creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. These improvements allow the AIS method to be applied to complicated deep penetration problems with complex geometry and multiple materials. A Completely coupled Neutron-Photon Auto-Importance Sampling (CNP-AIS) method is proposed to solve the deep penetration problems of coupled neutron-photon transport using the improved algorithms. The NUREG/CR-6115 PWR benchmark was calculated by using the methods of CNP-AIS, geometry splitting with Russian roulette and analog Monte Carlo, respectively. The calculation results of CNP-AIS are in good agreement with those of geometry splitting with Russian roulette and the benchmark solutions. The computational efficiency of CNP-AIS for both neutron and photon is much better than that of geometry splitting with Russian roulette in most cases, and increased by several orders of magnitude compared with that of the analog Monte Carlo.
- Monte Carlo ,
- deep penetration ,
- auto-importance sampling ,
- coupled neutron-photon transport

References

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Xin Wang, Jun-Li Li, Zhen Wu, Rui Qiu, Chun-Yan Li, Man-Chun Liang, Hui Zhang, Zhi Gang and Hong Xu. Improved algorithms and coupled neutron-photon transport for auto-importance sampling method[J]. Chinese Physics C, 2017, 41(1): 014103. doi: 10.1088/1674-1137/41/1/014103

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Received: 2016-03-23

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Supported by the subject of National Science and Technology Major Project of China (2013ZX06002001-007, 2011ZX06004-007) and National Natural Science Foundation of China (11275110, 11375103)}

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Improved algorithms and coupled neutron-photon transport for auto-importance sampling method

Corresponding author: Rui Qiu,

1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China

2. Key Laboratory of Particle &

3. Nuctech Company Limited, Beijing 100084, China

4. Department of Engineering Physics, Tsinghua University, Beijing 100084, China

5. State Nuclear Hua Qing(Beijing) Nuclear Power Technology R &

Received Date: 2016-03-23

Available Online: 2017-01-05

Fund Project: Supported by the subject of National Science and Technology Major Project of China (2013ZX06002001-007, 2011ZX06004-007) and National Natural Science Foundation of China (11275110, 11375103)}

Abstract: The Auto-Importance Sampling (AIS) method is a Monte Carlo variance reduction technique proposed for deep penetration problems, which can significantly improve computational efficiency without pre-calculations for importance distribution. However, the AIS method is only validated with several simple examples, and cannot be used for coupled neutron-photon transport. This paper presents improved algorithms for the AIS method, including particle transport, fictitious particle creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. These improvements allow the AIS method to be applied to complicated deep penetration problems with complex geometry and multiple materials. A Completely coupled Neutron-Photon Auto-Importance Sampling (CNP-AIS) method is proposed to solve the deep penetration problems of coupled neutron-photon transport using the improved algorithms. The NUREG/CR-6115 PWR benchmark was calculated by using the methods of CNP-AIS, geometry splitting with Russian roulette and analog Monte Carlo, respectively. The calculation results of CNP-AIS are in good agreement with those of geometry splitting with Russian roulette and the benchmark solutions. The computational efficiency of CNP-AIS for both neutron and photon is much better than that of geometry splitting with Russian roulette in most cases, and increased by several orders of magnitude compared with that of the analog Monte Carlo.

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Improved algorithms and coupled neutron-photon transport for auto-importance sampling method

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