Ray effects mitigation using the Monte Carlo first collision source method and application to the Kobayashi benchmark problems

2022 ◽  
Vol 169 ◽  
pp. 108902
Author(s):  
Guangchun Zhang ◽  
Congyu Hao ◽  
Kun Liu ◽  
Yulan Zhao ◽  
Hongchun Ding ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Benchmark Problems ◽  
Source Method ◽  
Ray Effects

scholarly journals A Comparison of Angular Discretization Techniques for the Radiative Transport Equation

2015 ◽  
Author(s):  
John Tencer
Keyword(s):  
Spherical Harmonics ◽  
Radiation Transport ◽  
Radiative Flux ◽  
Benchmark Problems ◽  
Discrete Ordinates ◽  
Flux Divergence ◽  
Discontinuous Boundary ◽  
All Solutions ◽  
Mesh Resolution ◽  
Ray Effects

Two of the most popular deterministic radiation transport methods for treating the angular dependence of the radiative intensity for heat transfer: the discrete ordinates and simplified spherical harmonics approximations are compared. A problem with discontinuous boundary conditions is included to evaluate ray effects for discrete ordinates solutions. Mesh resolution studies are included to ensure adequate convergence and evaluate the effects of the contribution of false scattering. All solutions are generated using finite element spatial discretization. Where applicable, any stabilization used is included in the description of the approximation method or the statement of the governing equations. A previous paper by the author presented results for a set of 2D benchmark problems for the discrete ordinates method using the PN-TN quadrature of orders 4, 6, and 8 as well as the P1, M1, and SP3 approximations. This paper expands that work to include the Lathrop-Carlson level symmetric quadrature of order up to 20 as well as the Lebedev quadrature of order up to 76 and simplified spherical harmonics of odd orders from 1 to 15. Two 3D benchmark problems are considered here. The first is a canonical problem of a cube with a single hot wall. This case is used primarily to demonstrate the potentially unintuitive interaction between mesh resolution, quadrature order, and solution error. The second case is meant to be representative of a pool fire. The temperature and absorption coefficient distributions are defined analytically. In both cases, the relative error in the radiative flux or the radiative flux divergence within a volume is considered as the quantity of interest as these are the terms that enter into the energy equation. The spectral dependence of the optical properties and the intensity is neglected.

scholarly journals INVESTIGATION ON DETERMINISTIC TRUNCATION TO CONTINUOUS ENERGY MONTE CARLO NEUTRON TRANSPORT CALCULATION

2021 ◽  
Vol 247 ◽  
pp. 04023
Author(s):  
Inhyung Kim ◽  
Yonghee Kim
Keyword(s):  
Monte Carlo ◽  
Figure Of Merit ◽  
Solution Method ◽  
Energy Transport ◽  
Neutron Transport ◽  
Computing Time ◽  
Computational Cost ◽  
Benchmark Problems ◽  
Continuous Energy ◽  
Transport Calculation

This paper presents the application and evaluation of a deterministic truncation of Monte Carlo (DTMC) solution method in a whole core reactor problem based on a continuous energy transport calculation. The DTMC method has been studied and developed as a systematic way to truncate the high-fidelity Monte Carlo (MC) solution to reduce the computational cost without compromising the essential reliability of the solution. Its fea-sibility and capability were preliminarily validated in several benchmark problems using a multi-group energy MC code. In this paper, further study has been conducted in the more practical application with the continuous-energy based MC calculation. The con-cept of the DTMC method is briefly described. Improvements to enhance the numerical stability and efficiency are specified in details. The DTMC method is applied to an SMR problem, in which reactor parameters are estimated to characterize the numerical per-formance and are compared to the standard MC method. Last, the computing time and corresponding figure-of-merit are evaluated.

scholarly journals EFFECT OF THE UNIFORM FISSION SOURCE METHOD ON LOCAL POWER VARIANCE IN FULL CORE SERPENT CALCULATION

2021 ◽  
Vol 247 ◽  
pp. 04024
Author(s):  
Yurii Bilodid ◽  
Jaakko Leppänen
Keyword(s):  
Monte Carlo ◽  
Power Distribution ◽  
Reactor Core ◽  
Monte Carlo Code ◽  
Local Power ◽  
Axial Distribution ◽  
Source Method ◽  
Power Variance ◽  
Full Core ◽  
Statistical Variance

One of challenges of the Monte Carlo full core simulations is to obtain acceptable statistical variance of local parameters throughout the whole reactor core at a reasonable computation cost. The statistical variance tends to be larger in low-power regions. To tackle this problem, the Uniform-Fission-Site method was implemented in Monte Carlo code MC21 and its effectiveness was demonstrated on NEA Monte Carlo performance benchmark. The very similar method is also implemented in Monte Carlo code Serpent under the name Uniform Fission Source (UFS) method. In this work the effect of UFS method implemented in Serpent is studied on the BEAVRS benchmark which is based on a real PWR core with relatively flat radial power distribution and also on 3x3 PWR mini-core simulated with thermo-hydraulic and thermo-mechanic feedbacks. It is shown that the application of the Uniform Fission Source method has no significant effect on radial power variance but equalizes axial distribution of variance of local power.

scholarly journals Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search

2013 ◽  
Vol 48 ◽  
pp. 841-883 ◽  
Author(s):  
A. Guez ◽  
D. Silver ◽  
P. Dayan
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Search Space ◽  
Search Tree ◽  
Benchmark Problems ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
The Face ◽  
Almost All ◽  
Infinite State

Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, planning optimally in the face of uncertainty is notoriously taxing, since the search space is enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach avoids expensive applications of Bayes rule within the search tree by sampling models from current beliefs, and furthermore performs this sampling in a lazy manner. This enables it to outperform previous Bayesian model-based reinforcement learning algorithms by a significant margin on several well-known benchmark problems. As we show, our approach can even work in problems with an infinite state space that lie qualitatively out of reach of almost all previous work in Bayesian exploration.

A Monte Carlo Perturbation Source Method for Reactivity Calculations

1978 ◽  
Vol 66 (1) ◽  
pp. 60-66 ◽  
Author(s):  
T. J. Hoffman ◽  
L. M. Petrie ◽  
N. F. Landers
Keyword(s):  
Monte Carlo ◽  
Source Method ◽  
Perturbation Source
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