Preliminary Results for Particle Tracking on Weight Window Isosurface Geometries for Monte Carlo Variance Reduction

2019 ◽  
Author(s):  
K. Kiesling ◽  
P. Wilson
Keyword(s):  
Monte Carlo ◽  
Particle Tracking ◽  
Variance Reduction ◽  
Preliminary Results ◽  
Weight Window

A Method to Alleviate the Long History Problem Encountered in Monte Carlo Simulations via Weight Window Variance Reduction

2017 ◽  
Vol 36 (6) ◽  
pp. 204-212 ◽  
Author(s):  
Xingchen Nie ◽  
Jia Li ◽  
Yuxiao Wu ◽  
Hengquan Zhang ◽  
Songlin Liu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Variance Reduction ◽  
Weight Window

scholarly journals PERFORMANCE STUDY OF GLOBAL WEIGHT WINDOW GENERATOR BASED ON PARTICLE DENSITY UNIFORMITY

2021 ◽  
Vol 247 ◽  
pp. 18005
Author(s):  
Peng He ◽  
Bin Wu ◽  
Lijuan Hao ◽  
Guangyao Sun ◽  
Bin Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Particle Density ◽  
Complex Model ◽  
Deep Penetration ◽  
Detailed Calculation ◽  
Performance Study ◽  
Global Weight ◽  
Weight Window ◽  
Calculation Results

The variance reduction techniques are necessary for Monte Carlo calculations in which obtaining a detailed calculation result for a large and complex model is required. The GVR method named as global weight window generator (GWWG) was proposed by the FDS team. In this paper, two typical calculation examples, ISPRA-Fe benchmark in SINBAD (Shielding Integral Benchmark Archive Database) and TF Coils (Toroidal Field coils) of European HCPB DEMO (Helium Cooled Pebble Bed demonstration fusion plant), are used to study the performance of GWWG method. It can be seen from the calculation results that the GWWG method has a significant effect in accelerating the Monte Carlo calculation. Especially when the global convergence calculation results are needed, the acceleration effect (FOMG) can reach 105 or more. It proves that the GWWG method is an effective tool for deep-penetration simulations using Monte Carlo method.

scholarly journals Analysis of DD, TT and DT Neutron Streaming Experiments with the ADVANTG Code

2020 ◽  
Vol 225 ◽  
pp. 02003
Author(s):  
Bor Kos ◽  
Theodora Vasilopoulou ◽  
Scott W. Mosher ◽  
Ivan A. Kodeli ◽  
Robert E. Grove ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Variance Reduction ◽  
Neutron Transport ◽  
Plasma Source ◽  
Sampling Methodology ◽  
Transport Code ◽  
Weight Window ◽  
Monte Carlo Transport ◽  
Hybrid Methodology

The paper presents an analysis of DD, TT and DT neutron streaming benchmark experiments with the recently released hybrid transport code ADVANTG (AutomateD VAriaNce reducTion Generator). ADVANTG combines the deterministic neutron transport solver Denovo with the Monte Carlo transport code MCNP via the principle of variance reduction. It automatically produces weight-window and source biasing variance reduction parameters based on the CADIS (Consistent Adjoint Driven Importance Sampling) methodology. Using this novel hybrid methodology Monte Carlo simulations of realistic complex fusion streaming geometries have become possible. In this paper the experimental results from the 2016 DD campaign using measurements with TLDs and activation foils up to 40 m from the plasma source are analyzed. New detailed models of the detector assemblies were incorporated into the JET 360° MCNP model for this analysis. In preparation of the TT and DTE2 campaigns at JET a pre-analysis for these campaigns is also presented.

Variance Reduction for Monte Carlo Methods

2018 ◽  
Vol 482 (6) ◽  
pp. 627-630
Author(s):  
D. Belomestny ◽  
◽  
L. Iosipoi ◽  
N. Zhivotovskiy ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Variance Reduction
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