A variationally-based variance reduction method for Monte Carlo neutron transport calculations

The transport of neutrons in long beamlines at spallation neutron sources presents a unique challenge for Monte-Carlo transport calculations. This is due to the need to accurately model the deep-penetration of high-energy neutrons through meters of thick dense shields close to the source and at the same time to model the transport of low- energy neutrons across distances up to around 150 m in length. Typically, such types of calculations may be carried out with MCNP-based codes or alternatively PHITS. However, in recent years there has been an increased interest in the suitability of Geant4 for such types of calculations. Therefore, we have implemented supermirror physics, a neutron chopper module and the duct-source variance reduction technique for low- energy neutron transport from the PHITS Monte-Carlo code into Geant4. In the current work, we present a series of benchmarks of these extensions with the PHITS software, which demonstrates the suitability of Geant4 for simulating long neutron beamlines at a spallation neutron source, such as the European Spallation Source, currently under construction in Lund, Sweden.

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Global variance reduction method for global Monte Carlo particle transport simulations of CFETR

Nuclear Science and Techniques ◽

10.1007/s41365-017-0270-3 ◽

2017 ◽

Vol 28 (8) ◽

Cited By ~ 2

Author(s):

Xing-Chen Nie ◽

Jia Li ◽

Song-Lin Liu ◽

Xiao-Kang Zhang ◽

Ping-Hui Zhao ◽

...

Keyword(s):

Monte Carlo ◽

Particle Transport ◽

Variance Reduction ◽

Reduction Method ◽

Global Variance Reduction

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ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

E-Jurnal Matematika ◽

10.24843/mtk.2021.v10.i04.p342 ◽

2021 ◽

Vol 10 (4) ◽

pp. 192

Author(s):

IRENE MAYLINDA PANGARIBUAN ◽

KOMANG DHARMAWAN ◽

I WAYAN SUMARJAYA

Keyword(s):

At Risk ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Value At Risk ◽

Variance Reduction ◽

Reduction Method ◽

Random Numbers ◽

Control Variates ◽

Popular Method ◽

Maximum Loss

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation or normal VaR method gives US$30.0949.

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