Bayesian Monte Carlo Method for Nuclear Data Evaluation

2015 ◽  
Vol 123 ◽  
pp. 207-213 ◽  
Author(s):  
A.J. Koning
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Nuclear Data ◽  
Data Evaluation ◽  
Bayesian Monte Carlo

scholarly journals Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data

2019 ◽  
Vol 211 ◽  
pp. 07007
Author(s):  
Henrik Sjöstrand ◽  
Georg Schnabel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Marginal Likelihood ◽  
Nuclear Data ◽  
Model Calculations ◽  
Adjustment Procedure ◽  
Inconsistent Data ◽  
Bayesian Monte Carlo ◽  
Data Libraries ◽  
Nuclear Data Libraries

Integral experiments can be used to adjust nuclear data libraries. Here a Bayesian Monte Carlo method based on assigning weights to the different random files is used. If the experiments are inconsistent within them-self or with the nuclear data it is shown that the adjustment procedure can lead to undesirable results. Therefore, a technique to treat inconsistent data is presented. The technique is based on the optimization of the marginal likelihood which is approximated by a sample of model calculations. The sources to the inconsistencies are discussed and the importance to consider correlation between the different experiments is emphasized. It is found that the technique can address inconsistencies in a desirable way.

scholarly journals Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

2019 ◽  
Vol 254 ◽  
pp. 113591 ◽  
Author(s):  
Xiaopeng Tang ◽  
Changfu Zou ◽  
Ke Yao ◽  
Jingyi Lu ◽  
Yongxiao Xia ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Trajectory Prediction ◽  
Model Migration ◽  
Bayesian Monte Carlo

scholarly journals Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

2020 ◽  
Vol 239 ◽  
pp. 18004
Author(s):  
Axel Laureau ◽  
Vincent Lamirand ◽  
Dimitri Rochman ◽  
Andreas Pautz
Keyword(s):  
Monte Carlo ◽  
Reaction Rate ◽  
Nuclear Data ◽  
Experimental Results ◽  
The Other ◽  
Nuisance Parameters ◽  
Strong Constraint ◽  
Experimental Programme ◽  
Bayesian Monte Carlo ◽  
Assimilation Process

This article presents the methodology developed to generate and use dosimeter covariances and to estimate nuisance parameters for the PETALE experimental programme. In anticipation of the final experimental results, this work investigates the consideration of these experimental correlations in the Bayesian assimilation process on nuclear data. Results show that the assimilation of a given set of dosimeters provides a strong constraint on some of the posterior reaction rate predictions of the other dosimeters. It confirms that, regarding the assimilation process, the different sets of dosimeters are correlated.

scholarly journals Predicting Battery Aging Trajectory via a Migrated Aging Model and Bayesian Monte Carlo Method

2019 ◽  
Vol 158 ◽  
pp. 2456-2461 ◽  
Author(s):  
Xiaopeng Tang ◽  
Ke Yao ◽  
Changfu Zou ◽  
Boyang Liu ◽  
Furong Gao
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Aging Model ◽  
Bayesian Monte Carlo

scholarly journals Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation

2019 ◽  
Vol 211 ◽  
pp. 07008 ◽  
Author(s):  
Oscar Cabellos ◽  
Luca Fiorito
Keyword(s):  
Monte Carlo ◽  
Large Scale ◽  
Uncertainty Propagation ◽  
Nuclear Data ◽  
Monte Carlo Technique ◽  
Data Uncertainty ◽  
Design Parameters ◽  
Monte Carlo Techniques ◽  
Bayesian Monte Carlo ◽  
The Impact

The aim of this work is to review different Monte Carlo techniques used to propagate nuclear data uncertainties. Firstly, we introduced Monte Carlo technique applied for Uncertainty Quantification studies in safety calculations of large scale systems. As an example, the impact of nuclear data uncertainty of JEFF-3.3 235U, 238U and 239Pu is demonstrated for the main design parameters of a typical 3-loop PWR Westinghouse unit. Secondly, the Bayesian Monte Carlo technique for data adjustment is presented. An example for 235U adjustment using criticality and shielding integral benchmarks shows the importance of performing joint adjustment based on different set of integral benchmarks.

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