Nuclear Data Uncertainty Propagation in Complex Fusion Geometries

The ASUSD program package was designed to automate and simplify the process of deterministic nuclear data sensitivity and uncertainty quantification. The program package couples Denovo, a discrete ordinate 3D transport solver, as part of ADVANTG and SUSD3D, a deterministic first order perturbation theory based Sensitivity/Uncertainty code, using several auxiliary programs used for input data preparation and post processing. Because of the automation employed in ASUSD, it is useful for Sensitivity/Uncertainty analysis of complex fusion geometries. In this paper, ASUSD was used to quantify uncertainties in the JET KN2 irradiation position. The results were compared to previously obtained probabilistic-based uncertainties determined using TALYS-based random nuclear data samples and MCNP in a Total Monte Carlo computation scheme. Results of the two approaches, deterministic and probabilistic, to nuclear data uncertainty propagation are compared and discussed. ASUSD was also used to perform preliminary Sensitivity/Uncertainty (S/U) analyses of three JET3-NEXP streaming benchmark experimental positions (A1, A4 and A7).

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Analysis of Modeling Uncertainty Impacts on Nuclear Data Uncertainty Propagation in Core Simulation

10.13182/t123-33308 ◽

2020 ◽

Author(s):

H. Abdel-Khalik ◽

D. Huang

Keyword(s):

Uncertainty Propagation ◽

Nuclear Data ◽

Data Uncertainty ◽

Modeling Uncertainty ◽

Core Simulation

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Nuclear Data Uncertainty Propagation for a Sodium Fast Reactor

Journal of the Korean Physical Society ◽

10.3938/jkps.59.1191 ◽

2011 ◽

Vol 59 (2(3)) ◽

pp. 1191-1194 ◽

Cited By ~ 2

Author(s):

D. Rochman ◽

A. J. Koning ◽

D. F. Dacruz ◽

S. C. van der Marck

Keyword(s):

Fast Reactor ◽

Uncertainty Propagation ◽

Nuclear Data ◽

Data Uncertainty ◽

Sodium Fast Reactor

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Nuclear Data Uncertainty Propagation in Depletion Calculations Using Cross Section Uncertainties in One-group or Multi-group

Nuclear Data Sheets ◽

10.1016/j.nds.2014.12.014 ◽

2015 ◽

Vol 123 ◽

pp. 79-83

Author(s):

C.J. Díez ◽

O. Cabellos ◽

J.S. Martínez

Keyword(s):

Cross Section ◽

Uncertainty Propagation ◽

Nuclear Data ◽

Data Uncertainty

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Inventory calculation and nuclear data uncertainty propagation on light water reactor fuel using ALEPH-2 and SCALE 6.2

Annals of Nuclear Energy ◽

10.1016/j.anucene.2015.03.046 ◽

2015 ◽

Vol 83 ◽

pp. 137-146 ◽

Cited By ~ 7

Author(s):

L. Fiorito ◽

D. Piedra ◽

O. Cabellos ◽

C.J. Diez

Keyword(s):

Uncertainty Propagation ◽

Nuclear Data ◽

Reactor Fuel ◽

Light Water Reactor ◽

Data Uncertainty ◽

Light Water ◽

Water Reactor

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Nuclear Data Uncertainty Propagation to Reactivity Coefficients of a Sodium Fast Reactor

Nuclear Data Sheets ◽

10.1016/j.nds.2014.04.127 ◽

2014 ◽

Vol 118 ◽

pp. 535-537

Author(s):

J.J. Herrero ◽

R. Ochoa ◽

J.S. Martínez ◽

C.J. Díez ◽

N. García-Herranz ◽

...

Keyword(s):

Fast Reactor ◽

Uncertainty Propagation ◽

Nuclear Data ◽

Data Uncertainty ◽

Sodium Fast Reactor ◽

Reactivity Coefficients

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Enhancement of Stochastic Sampling Capability in RMC Code

Volume 3: Nuclear Fuel and Material, Reactor Physics, and Transport Theory ◽

10.1115/icone26-81982 ◽

2018 ◽

Author(s):

Guanlin Shi ◽

Yishu Qiu ◽

Kan Wang

Keyword(s):

Uncertainty Analysis ◽

Sampling Method ◽

Nuclear Data ◽

Data Uncertainty ◽

Monte Carlo Code ◽

Tabular Data ◽

Stochastic Sampling ◽

First Order ◽

Fission Spectrum ◽

Sensitivity And Uncertainty Analysis

As people pay more attention to nuclear safety analysis, sensitivity and uncertainty analysis has become a research hotspot. In our previous research, we had developed an integrated, built-in stochastic sampling module in the Reactor Monte Carlo code RMC [1]. Using this module, we can perform nuclear data uncertainty analysis. But at that time the uncertainty of fission spectrum was not considered. So, in this work, the capability of computing the uncertainty of keff induced by the uncertainty of fission spectrum, including tabular data form and formula form, is implemented in RMC code based on the stochastic sampling method. The algorithms and capability of computing keff uncertainty induced by uncertainty of fission spectrum in RMC are verified by comparison with the results calculated by the first order uncertainty quantification method [2].

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Nuclear-data uncertainty propagation in transient simulation for the C5G7-TD benchmark problem

Annals of Nuclear Energy ◽

10.1016/j.anucene.2019.107122 ◽

2020 ◽

Vol 140 ◽

pp. 107122

Author(s):

Chenghui Wan ◽

Zhuojie Sui ◽

Bo Wang ◽

Liangzhi Cao ◽

Zhouyu Liu ◽

...

Keyword(s):

Uncertainty Propagation ◽

Nuclear Data ◽

Transient Simulation ◽

Data Uncertainty ◽

Benchmark Problem

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Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation

EPJ Web of Conferences ◽

10.1051/epjconf/201921107008 ◽

2019 ◽

Vol 211 ◽

pp. 07008 ◽

Cited By ~ 1

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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