Nucleonic analysis of the ETF neutral-beam-injector-duct and vacuum-pumping-duct shields

Urban, W.T.; Seed, T.J.; Dudziak, D.J.

doi:10.2172/6476073

Cited by 2 publications

(2 citation statements)

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“…In the CNP-AIS simulation, NPS was 5×10 6 , and T was 221 minutes. Four neutron and four photon cylindrical fictitious surfaces, whose radii were 188, 215, 230 and 300 cm, were introduced.…”

Section: Simulation Set-upmentioning

confidence: 99%

“…To solve deep penetration problems in radiation shielding calculation using Monte Carlo (MC) simulation, many methods have been developed based on the theory of coupling with deterministic method. These methods include different types of Monte Carlo variance reduction techniques [1][2][3][4] and coupled MC/discreteordinates methods [5][6][7]. However, these solutions have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Li²,

Wu³

et al. 2017

Chinese Phys. C

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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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Section: Simulation Set-upmentioning

confidence: 99%