Monte Carlo Perturbation Analysis of Isothermal Temperature Reactivity Coefficient in Kyoto University Critical Assembly

2015 ◽  
Vol 191 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Byoung Kyu Jeon ◽  
Cheol Ho Pyeon ◽  
Hyung Jin Shim
Keyword(s):  
Monte Carlo ◽  
Perturbation Analysis ◽  
Critical Assembly ◽  
Isothermal Temperature ◽  
Reactivity Coefficient

Validating the Serpent-Ants Calculation Chain Using BEAVRS Fresh Core HZP Data

2021 ◽  
Author(s):  
Ville Valtavirta ◽  
Antti Rintala ◽  
Unna Lauranto
Keyword(s):  
Monte Carlo ◽  
Best Practices ◽  
Power Distribution ◽  
Measured Data ◽  
Monte Carlo Code ◽  
Generation Process ◽  
Isothermal Temperature ◽  
Control Rod ◽  
Temperature Coefficients ◽  
Group Constant

Abstract The Serpent Monte Carlo code and the Serpent-Ants two step calculation chain are used to model the hot zero power physics tests described in the BEAVRS benchmark. The predicted critical boron concentrations, control rod group worths and isothermal temperature coefficients are compared between Serpent and Serpent-Ants as well as against the experimental measurements. Furthermore, radial power distributions in the unrodded and rodded core configurations are compared between Serpent and Serpent-Ants. In addition to providing results using a best practices calculation chain, the effects of several simplifications or omissions in the group constant generation process on the results are estimated. Both the direct and two-step neutronics solutions provide results close to the measured values. Comparison between the measured data and the direct Serpent Monte Carlo solution yields RMS differences of 12.1 mg/kg, 25.1 × 10-5 and 0.67 × 10-5 K-1 for boron, control rod worths and temperature coefficients respectively. The two-step Serpent-Ants solution reaches a similar level of accuracy with RMS differences of 17.4 mg/kg, 23.6 × 10-5 and 0.29 × 10-5 K-1. The match in the radial power distribution between Serpent and Serpent-Ants was very good with the RMS and maximum for pin power errors being 1.31 % and 4.99 % respectively in the unrodded core and 1.67 %(RMS) and 8.39 % (MAX) in the rodded core.

scholarly journals Analysis of Void Reactivity Coefficient for 3D BWR Assembly Model

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Andrius Slavickas ◽  
Raimondas Pabarčius ◽  
Aurimas Tonkūnas ◽  
Eugenijus Ušpuras
Keyword(s):  
Monte Carlo ◽  
Fuel Assembly ◽  
Density Profile ◽  
3D Model ◽  
Functional Modules ◽  
Assembly Model ◽  
Density Values ◽  
The Difference ◽  
Reactivity Coefficient ◽  
Assembly Models

The effect of BWR fuel assembly 3D model on void reactivity coefficient (VRC) estimation is investigated. VRC values were calculated for different BWR assembly models applying deterministic T-NEWT and Monte Carlo KENO-VI functional modules of SCALE 6.1 code package. The difference between deterministic T-NEWT and Monte Carlo KENO-VI simulations is negligible (0.18 pcm/%). The influence of the assumed more detailed coolant density profile was estimated as well. VRC increases with the application of a larger number of coolant density values across fuel assembly height. It was shown that the coolant density profile described by 6 values per height could be considered sufficient from prospect of VRC estimation, as a more detailed density profile has impact below 1% on total assembly void effects. VRC values were decomposed to values for individual nodes and isotopes, since decomposition provides useful insights to describe the overall behaviour of VRC in detail.

A Normalized Modal Eigenvalue Approach for Resolving Modal Interaction

1997 ◽  
Vol 119 (3) ◽  
pp. 647-650 ◽  
Author(s):  
M.-T. Yang ◽  
J. H. Griffin
Keyword(s):  
Monte Carlo ◽  
Eigenvalue Problem ◽  
Perturbation Analysis ◽  
Material Properties ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Modal Interaction ◽  
Eigenvalue Approach ◽  
The Difference

Modal interaction refers to the way that the modes of a structure interact when its geometry and material properties are perturbed. The amount of interaction between the neighboring modes depends on the closeness of the natural frequencies, the mode shapes, and the magnitude and distribution of the perturbation. By formulating the structural eigenvalue problem as a normalized modal eigenvalue problem, it is shown that the amount of interaction in two modes can be simply characterized by six normalized modal parameters and the difference between the normalized frequencies. In this paper, the statistical behaviors of the normalized frequencies and modes are investigated based on a perturbation analysis. The results are independently verified by Monte Carlo simulations.

scholarly journals A Normalized Modal Eigenvalue Approach for Resolving Modal Interaction

1996 ◽  
Author(s):  
Ming-Ta Yang ◽  
Jerry H. Griffin
Keyword(s):  
Monte Carlo ◽  
Eigenvalue Problem ◽  
Perturbation Analysis ◽  
Material Properties ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Modal Interaction ◽  
Eigenvalue Approach ◽  
The Difference

Modal interaction refers to the way that the modes of a structure interact when its geometry and material properties are perturbed. The amount of interaction between the neighboring modes depends on the closeness of the natural frequencies, the mode shapes, and the magnitude and distribution of the perturbation. By formulating the structural eigenvalue problem as a normalized modal eigenvalue problem, it is shown that the amount of interaction in two modes can be simply characterized by six normalized modal parameters and the difference between the normalized frequencies. In this paper, the statistical behaviors of the normalized frequencies and modes are investigated based on a perturbation analysis. The results are independently verified by Monte Carlo simulations.

Convergence of perturbation analysis estimates for discontinuous sample functions: a general approach

1988 ◽  
Vol 20 (1) ◽  
pp. 59-78 ◽  
Author(s):  
Reuven Y. Rubinstein ◽  
Ferenc Szidarovszky
Keyword(s):  
Monte Carlo ◽  
Rate Of Convergence ◽  
Performance Measures ◽  
Dynamic Systems ◽  
Perturbation Analysis ◽  
Sample Path ◽  
Single Sample ◽  
Parameter Vector ◽  
Discrete Events ◽  
Convergence Conditions

Generalized perturbation analysis (PA) estimates to study sensitivity of performance measures of discrete events dynamic systems for discontinuous sample functions are introduced. Their convergence conditions and rate of convergence are given. It is shown that the PA estimates based on a single sample path always converge faster to the unknown sensitivity parameter (vector of parameters) than their counterpart—crude Monte Carlo ones.

Monte Carlo simulations of the water draining experimentof Giacint critical assembly

2021 ◽  
Vol 164 ◽  
pp. 108505 ◽  
Author(s):  
Yan Cao ◽  
Alberto Talamo ◽  
Yousry Gohar ◽  
S. Sikorin ◽  
S. Mandzik ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Assembly
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