G4-STORK: A Geant4-based Monte Carlo reactor kinetics simulation code

2014 ◽  
Vol 71 ◽  
pp. 451-461 ◽  
Author(s):  
Liam Russell ◽  
Adriaan Buijs ◽  
Guy Jonkmans
Keyword(s):  
Monte Carlo ◽  
Simulation Code ◽  
Reactor Kinetics ◽  
Kinetics Simulation

G4-STORK: A Monte Carlo Reactor Kinetics Simulation Code

2014 ◽  
Vol 176 (3) ◽  
pp. 370-375 ◽  
Author(s):  
Liam Russell ◽  
Adriaan Buijs ◽  
Guy Jonkmans
Keyword(s):  
Monte Carlo ◽  
Simulation Code ◽  
Reactor Kinetics ◽  
Kinetics Simulation

McChasy2: New Monte Carlo RBS/C simulation code designed for use with large crystalline structures

2021 ◽  
Vol 498 ◽  
pp. 9-14
Author(s):  
Lech Nowicki ◽  
Jacek Jagielski ◽  
Cyprian Mieszczyński ◽  
Kazimierz Skrobas ◽  
Przemysław Jóźwik ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Simulation Code ◽  
Crystalline Structures

scholarly journals Estimating Air Shower Fluctuations from the Monte Carlo Simulation Code CORSIKA

2013 ◽  
Vol 10 ◽  
pp. 236-241
Author(s):  
R.K. Dey ◽  
T. Sarkar ◽  
A. Bhadra
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Code ◽  
Air Shower

Development of multidimensional Monte Carlo simulation code for H− ion and neutral transport in H− ion sources

2002 ◽  
Vol 73 (2) ◽  
pp. 910-913 ◽  
Author(s):  
A. Hatayama ◽  
T. Sakurabayashi ◽  
Y. Ishi ◽  
K. Makino ◽  
M. Ogasawara ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ion Sources ◽  
Simulation Code

Calculation of gamma ray attenuation coefficients of some granite samples using a Monte Carlo simulation code

2018 ◽  
Vol 144 ◽  
pp. 271-275 ◽  
Author(s):  
O. Ozyurt ◽  
N. Altinsoy ◽  
Ş.İ. Karaaslan ◽  
A. Bora ◽  
B. Buyuk ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gamma Ray ◽  
Simulation Code ◽  
Attenuation Coefficients ◽  
Gamma Ray Attenuation ◽  
Granite Samples

scholarly journals A Monte-Carlo Simulation of the Behaviour of Electron Swarms in Hydrogen Using an Anisotropic Scattering Model

1978 ◽  
Vol 31 (4) ◽  
pp. 299 ◽  
Author(s):  
HA Blevin ◽  
J Fletcher ◽  
SR Hunter
Keyword(s):  
Experimental Data ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electron Transport ◽  
Anisotropic Scattering ◽  
Isotropic Scattering ◽  
Transport Parameters ◽  
Simulation Code ◽  
Scattering Model ◽  
Good Agreement

Hunter (1977) found that a Monte-Carlo simulation of electron swarms in hydrogen, based on an isotropic scattering model, produced discrepancies between the predicted and measured electron transport parameters. The present paper shows that, with an anisotropic scattering model, good agreement is obtained between the predicted and experimental data. The simulation code is used here to calculate various parameters which are not directly measurable.

ISSDE: A Monte Carlo implicit simulation code based on Stratonovich SDE approach of Coulomb collision

2021 ◽  
Author(s):  
Yifeng Zheng ◽  
Jianyuan Xiao ◽  
Yanpeng Wang ◽  
Jiangshan Zheng ◽  
Ge Zhuang
Keyword(s):  
Monte Carlo ◽  
Coulomb Collision ◽  
Simulation Code

Production Yield Estimation by the Metamodel Method With a Boundary-Focused Experiment Design

1997 ◽  
Author(s):  
Chinghsin Tu ◽  
Russell R. Barton
Keyword(s):  
Monte Carlo ◽  
Robust Design ◽  
Simulation Models ◽  
Experiment Design ◽  
Production Yield ◽  
Design Stage ◽  
Yield Estimation ◽  
Simulation Code ◽  
New Approach ◽  
A Priority

Abstract The need for yield estimation strategies in the design stage is a priority recognized by industry. Yield estimates can be employed to assess the manufacturability of a design, and allow for modification to produce a robust design. Therefore, low yield of products can be avoided and costs for manufacturing can be reduced. This paper presents an accurate and time-efficient yield estimation approach for use with simulation models. We use a metamodel-based method, which is time-efficient compared to crude Monte Carlo yield estimation using the original simulation code. The approach employs a boundary-focused experiment design, which overcomes the inaccuracy of yield estimates that can occur when using a metamodel method. The results of two examples demonstrate the effectiveness of this new approach.

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