MC21 v.6.0 – A continuous-energy Monte Carlo particle transport code with integrated reactor feedback capabilities

2015 ◽  
Vol 82 ◽  
pp. 29-40 ◽  
Author(s):  
D.P. Griesheimer ◽  
D.F. Gill ◽  
B.R. Nease ◽  
T.M. Sutton ◽  
M.H. Stedry ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Continuous Energy ◽  
Transport Code

scholarly journals Evaluation of Single-Node Performance of Parallel Algorithms for Multigroup Monte Carlo Particle Transport Methods

2021 ◽  
Vol 9 ◽  
Author(s):  
Donghui Ma ◽  
Bo Yang ◽  
Qingyang Zhang ◽  
Jie Liu ◽  
Tiejun Li
Keyword(s):  
Monte Carlo ◽  
Parallel Algorithms ◽  
Particle Transport ◽  
Data Locality ◽  
Bandwidth Utilization ◽  
Single Node ◽  
Continuous Energy ◽  
Transport Code ◽  
Event Based ◽  
The Impact

Monte Carlo (MC) methods have been widely used to solve the particle transport equation due to their high accuracy and capability of processing complex geometries. History-based and event-based algorithms that are applicable to different architectures are two methods for parallelizing the MC code. There is a large work on evaluating and optimizing parallel algorithms with continuous-energy schemes. In this work, we evaluate the single-node performance of history-based and event-based algorithms for multigroup MC methods on both CPUs and GPUs with Quicksilver, a multigroup MC transport code that has already implemented the history-based algorithms. We first implement and optimize the event-based algorithm based on Quicksilver and then perform the evaluation work extensively on the Coral2 benchmark. Numerical results indicate that contrary to continuous-energy schemes, the history-based approach with multigroup schemes outperforms the event-based algorithm on both architectures in all cases. We summarize that the performance loss of the event-based algorithm is mainly due to: 1) extra operations to reorganize particles, 2) batched atomic operations, and 3) poor particle data locality. Despite the poor performance, the event-based algorithm achieves higher memory bandwidth utilization. We further discuss the impact of memory access patterns and calculation of cross sections (xs) on the performance of the GPU. Built on the analytics, and shed light on the algorithm choice and optimizations for paralleling the MC transport code on different architectures.

scholarly journals Depletion capabilities in the OpenMC Monte Carlo particle transport code

2021 ◽  
Vol 152 ◽  
pp. 107989
Author(s):  
Paul K. Romano ◽  
Colin J. Josey ◽  
Andrew E. Johnson ◽  
Jingang Liang
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Transport Code

A New Prototype Display Tool for the Monte Carlo Particle Transport Code TRIPOLI-4

2011 ◽  
Vol 2 (0) ◽  
pp. 851-854 ◽  
Author(s):  
Francois-Xavier HUGOT ◽  
Yi-Kang LEE
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Transport Code

scholarly journals The OpenMC Monte Carlo particle transport code

2013 ◽  
Vol 51 ◽  
pp. 274-281 ◽  
Author(s):  
Paul K. Romano ◽  
Benoit Forget
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Transport Code

scholarly journals 4H-SiC Schottky Barrier Diodes for Efficient Thermal Neutron Detection

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5105
Author(s):  
Robert Bernat ◽  
Luka Bakrač ◽  
Vladimir Radulović ◽  
Luka Snoj ◽  
Takahiro Makino ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Thermal Neutron ◽  
Schottky Barrier ◽  
Particle Transport ◽  
Neutron Detection ◽  
Schottky Barrier Diodes ◽  
Optimal Thickness ◽  
Transport Code ◽  
Neutron Converter

In this work, we present the improved efficiency of 4H-SiC Schottky barrier diodes-based detectors equipped with the thermal neutron converters. This is achieved by optimizing the thermal neutron converter thicknesses. Simulations of the optimal thickness of thermal neutron converters have been performed using two Monte Carlo codes (Monte Carlo N–Particle Transport Code and Stopping and Range of Ions in Matter). We have used 6LiF and 10B4C for the thermal neutron converter material. We have achieved the thermal neutron efficiency of 4.67% and 2.24% with 6LiF and 10B4C thermal neutron converters, respectively.

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