“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo

2015 ◽  
Vol 123 ◽  
pp. 201-206 ◽  
Author(s):  
E. Bauge
Keyword(s):  
Monte Carlo ◽  
Evaluation Process ◽  
Nuclear Data ◽  
Full Model

scholarly journals Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO Due to Nuclear Data Uncertainties

2021 ◽  
Vol 11 (11) ◽  
pp. 5234
Author(s):  
Jin Hun Park ◽  
Pavel Pereslavtsev ◽  
Alexandre Konobeev ◽  
Christian Wegmann
Keyword(s):  
Monte Carlo ◽  
Fusion Reactor ◽  
Nuclear Data ◽  
Data File ◽  
Nuclear Model ◽  
Model Parameters ◽  
Tritium Breeding ◽  
Tritium Breeding Ratio ◽  
Nuclear Data Library ◽  
Breeding Ratio

For the stable and self-sufficient functioning of the DEMO fusion reactor, one of the most important parameters that must be demonstrated is the Tritium Breeding Ratio (TBR). The reliable assessment of the TBR with safety margins is a matter of fusion reactor viability. The uncertainty of the TBR in the neutronic simulations includes many different aspects such as the uncertainty due to the simplification of the geometry models used, the uncertainty of the reactor layout and the uncertainty introduced due to neutronic calculations. The last one can be reduced by applying high fidelity Monte Carlo simulations for TBR estimations. Nevertheless, these calculations have inherent statistical errors controlled by the number of neutron histories, straightforward for a quantity such as that of TBR underlying errors due to nuclear data uncertainties. In fact, every evaluated nuclear data file involved in the MCNP calculations can be replaced with the set of the random data files representing the particular deviation of the nuclear model parameters, each of them being correct and valid for applications. To account for the uncertainty of the nuclear model parameters introduced in the evaluated data file, a total Monte Carlo (TMC) method can be used to analyze the uncertainty of TBR owing to the nuclear data used for calculations. To this end, two 3D fully heterogeneous geometry models of the helium cooled pebble bed (HCPB) and water cooled lithium lead (WCLL) European DEMOs were utilized for the calculations of the TBR. The TMC calculations were performed, making use of the TENDL-2017 nuclear data library random files with high enough statistics providing a well-resolved Gaussian distribution of the TBR value. The assessment was done for the estimation of the TBR uncertainty due to the nuclear data for entire material compositions and for separate materials: structural, breeder and neutron multipliers. The overall TBR uncertainty for the nuclear data was estimated to be 3~4% for the HCPB and WCLL DEMOs, respectively.

Assessment and Propagation of the237Np Nuclear Data Uncertainties in Integral Calculations by Monte Carlo Techniques

2008 ◽  
Vol 160 (1) ◽  
pp. 108-122 ◽  
Author(s):  
Gilles Noguere ◽  
David Bernard ◽  
Cyrille De Saint Jean ◽  
Bertrand Iooss ◽  
Frank Gunsing ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Nuclear Data ◽  
Monte Carlo Techniques

scholarly journals Analysis of dynamic reactivity by Monte Carlo methods: The impact of nuclear data

2017 ◽  
Vol 110 ◽  
pp. 11-24 ◽  
Author(s):  
Andrea Zoia ◽  
Cédric Jouanne ◽  
Patricia Siréta ◽  
Pierre Leconte ◽  
George Braoudakis ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Nuclear Data ◽  
The Impact

Nuclear data uncertainty for criticality-safety: Monte Carlo vs. linear perturbation

2016 ◽  
Vol 92 ◽  
pp. 150-160 ◽  
Author(s):  
D. Rochman ◽  
A. Vasiliev ◽  
H. Ferroukhi ◽  
T. Zhu ◽  
S.C. van der Marck ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Nuclear Data ◽  
Data Uncertainty ◽  
Linear Perturbation ◽  
Criticality Safety

Uncertainty Evaluation in Distance Measurement by CMM Based on Monte Carlo Method

2013 ◽  
Vol 684 ◽  
pp. 429-433 ◽  
Author(s):  
Hong Li Li ◽  
Xiao Huai Chen ◽  
Hong Tao Wang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Evaluation Process ◽  
Mathematic Model ◽  
Distance Measurement ◽  
Uncertainty Evaluation ◽  
Measured Quantity ◽  
Complete Uncertainty ◽  
End Distance ◽  
Influence Measurement

There is presented a complete uncertainty evaluation process of end distance measurement by CMM. To begin with, the major sources of uncertainty, which would influence measurement result, are found out after analyzing, then, the general mathematic model of end distance measurement is established. Furthermore, Monte Carlo method (MCM) is used, and the uncertainty of the measured quantity is obtained. The complete results are given out, so the value of CMM is enhanced. Moreover, seen from the evaluation example, the results of uncertainty evaluation obtained from MCM method and from GUM method are compared, the comparison result indicates that the mathematic model is feasible, and using MCM method to evaluate uncertainty is easy and efficient, having practical value.

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