Improvement of fission source distribution by correlated sampling method in Monte Carlo perturbation calculations

2018 ◽  
Vol 55 (8) ◽  
pp. 945-954 ◽  
Author(s):  
Song Hyun Kim ◽  
Masao Yamanaka ◽  
Cheol Ho Pyeon
Keyword(s):  
Monte Carlo ◽  
Sampling Method ◽  
Source Distribution ◽  
Correlated Sampling

Implementation of correlated sampling method for Monte Carlo simulations of small-sized solid-state dosimetric response

2014 ◽  
Vol 30 ◽  
pp. e137-e138
Author(s):  
R. Wang ◽  
P. Pittet ◽  
G.-N. Lu ◽  
P. Guiral ◽  
A. Ahnesjö
Keyword(s):  
Monte Carlo ◽  
Solid State ◽  
Monte Carlo Simulations ◽  
Sampling Method ◽  
Correlated Sampling

Research on Monte Carlo Perturbation Calculation Methods Applied in Reactor Physics

2009 ◽  
Author(s):  
Ze-guang Li ◽  
Kan Wang ◽  
Gang-lin Yu
Keyword(s):  
Monte Carlo ◽  
Differential Operator ◽  
Sampling Method ◽  
Perturbation Methods ◽  
Calculation Model ◽  
Perturbation Calculation ◽  
Reactor Physics ◽  
Tracking Method ◽  
Monte Carlo Techniques ◽  
Sensitivity Studies

In the reactor design and analysis, there is often a need to calculate the effects caused by perturbations of temperature, components and even structure of reactors on reactivity. And in sensitivity studies, uncertainty analysis of target quantities and unclear data adjustment, perturbation calculations are also widely used. To meet the need of different types of reactors (complex, multidimensional systems), Monte Carlo perturbation methods have been developed. In this paper, several kinds of perturbation methods are investigated. Specially, differential operator sampling method and correlated tracking method are discussed in details. MCNP’s perturbation calculation capability is discussed by calculating certain problems, from which some conclusions are obtained on the capabilities of the differential operator sampling method used in the perturbation calculation model of MCNP. Also, a code using correlated tracking method has been developed to solve certain problems with cross-section changes, and the results generated by this code agree with the results generated by straightforward Monte Carlo techniques.

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

Accelerated Monte Carlo based dose calculations for brachytherapy planning using correlated sampling

2002 ◽  
Vol 47 (3) ◽  
pp. 351-376 ◽  
Author(s):  
Håkan Hedtjärn ◽  
Gudrun Alm Carlsson ◽  
Jeffrey F Williamson
Keyword(s):  
Monte Carlo ◽  
Dose Calculations ◽  
Correlated Sampling

scholarly journals An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management

2018 ◽  
Vol 98 ◽  
pp. 11-26 ◽  
Author(s):  
Alejandro Peña ◽  
Isis Bonet ◽  
Christian Lochmuller ◽  
Francisco Chiclana ◽  
Mario Góngora
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Fuzzy System ◽  
Sampling Method ◽  
Operational Risk ◽  
Monte Carlo Sampling ◽  
Operational Risk Management ◽  
Neural Fuzzy
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