scholarly journals FAST REACTOR MULTIPHYSICS AND UNCERTAINTY PROPAGATION WITHIN WIMS

2021 ◽  
Vol 247 ◽  
pp. 06002
Author(s):  
Ben Lindley ◽  
Brendan Tollit ◽  
Peter Smith ◽  
Alan Charles ◽  
Robert Mason ◽  
...  
Keyword(s):  
Fast Reactor ◽  
Uncertainty Propagation ◽  
Predictive Modelling ◽  
Nuclear Data ◽  
Reactor Physics ◽  
Fuel Performance ◽  
Horizon 2020 ◽  
Calculation Methodology ◽  
International Projects ◽  
Fast Reactor Fuel

For liquid metal-cooled fast reactors (LMFRs), improved predictive modelling is desirable to facilitate reactor licensing and operation and move towards a best estimate plus uncertainty (BEPU) approach. A key source of uncertainty in fast reactor calculations arises from the underlying nuclear data. Addressing the propagation of such uncertainties through multiphysics calculations schemes is therefore of importance, and is being addressed through international projects such as the Sodium-cooled Fast Reactor Uncertainty Analysis in Modelling (SFR-UAM) benchmark. In this paper, a methodology for propagation of nuclear data uncertainties within WIMS is presented. Uncertainties on key reactor physics parameters are calculated for selected SFR-UAM benchmark exercises, with good agreement with previous results. A methodology for coupled neutronic-thermal-hydraulic calculations within WIMS is developed, where thermal feedback is introduced to the neutronic solution through coupling with the ARTHUR subchannel code within WIMS and applied to steady-state analysis of the Horizon 2020 ESFR-SMART project reference core. Finally, integration of reactor physics and fuel performance calculations is demonstrated through linking of the WIMS reactor physics code to the TRAFIC fast reactor fuel performance code, through a Fortran-C-Python (FCP) interface. Given the 3D multiphysics calculation methodology, thermal-hydraulic and fuel performance uncertainties can ultimately be sampled alongside the nuclear data uncertainties. Together, these developments are therefore an important step towards enabling propagation of uncertainties through high fidelity, multiphysics SFR calculations and hence facilitate BEPU methodologies.

scholarly journals Fast Reactor Fuel Performance Model Development

1970 ◽  
Vol 9 (3) ◽  
pp. 326-337 ◽  
Author(s):  
A. Boltax ◽  
P. Murray ◽  
A. Biancheria
Keyword(s):  
Fast Reactor ◽  
Model Development ◽  
Performance Model ◽  
Reactor Fuel ◽  
Fuel Performance ◽  
Fast Reactor Fuel

Nuclear Data Uncertainty Propagation for a Sodium Fast Reactor

2011 ◽  
Vol 59 (2(3)) ◽  
pp. 1191-1194 ◽  
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

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

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

scholarly journals New features and improvements in the NEA nuclear data tool suite

2020 ◽  
Vol 239 ◽  
pp. 19003
Author(s):  
M. Fleming ◽  
I. Hill ◽  
J. Dyrda ◽  
L. Fiorito ◽  
N. Soppera ◽  
...  
Keyword(s):  
Relational Databases ◽  
Uncertainty Propagation ◽  
Nuclear Data ◽  
Data Types ◽  
Reactor Physics ◽  
Energy Agency ◽  
Order Of Magnitude ◽  
Data Files ◽  
Improved Performance ◽  
Data Libraries

The OECD Nuclear Energy Agency (NEA) has developed and maintains several products that are used in the verification and validation of nuclear data, including the Java-based Nuclear Data Information System (JANIS) and the Nuclear Data Sensitivity Tool (NDaST). These integrate other collections of the NEA, including the International Handbooks of benchmark experiments on Criticality Safety and Reactor Physics (ICSBEP and IRPhEP) and their supporting relational databases (DICE and IDAT). Recent development of the JANIS, DICE and NDaST systems have resulted in the ability to perform uncertainty propagation utilising Legendre polynomial sensitivities, calculation of case-to-case covariances and correlations, use of spectrum weighting in perturbations, calculation of statistical results with suites of randomly sampled nuclear data files and new command-line interfaces to automate analyses and generate XML outputs. All of the most recent, major nuclear data libraries have been fully processed and incorporated, along with new visualisation features for covariances and sensitivities, an expanded set of reaction channel definitions, and new EXFOR data types defined by the NRDC. Optimisation of numerical methods has also improved performance, with over order-of-magnitude speed-up in the case of sensitivity-uncertainty calculations.

scholarly journals UNCERTAINTY ANALYSIS TO C5G7-TD BENCHMARK BASED ON THE COST METHOD

2021 ◽  
Vol 247 ◽  
pp. 15007
Author(s):  
Liangzhi Cao ◽  
Zhuojie Sui ◽  
Bo Wang ◽  
Chenghui Wan ◽  
Zhouyu Liu
Keyword(s):  
Uncertainty Analysis ◽  
Nuclear Reactor ◽  
Uncertainty Propagation ◽  
Nuclear Data ◽  
Reactor Physics ◽  
Oriented Sample ◽  
Neutron Kinetics ◽  
Minimal Sample ◽  
Physics Simulation ◽  
The Cost

A method of Covariance-Oriented Sample Transformation (COST) has been proposed in our previous work to provide the converged uncertainty analysis results with a minimal sample size. The transient calculation of nuclear reactor is a key part of the reactor-physics simulation, so the accuracy and confidence of the neutron kinetics results have attracted much attention. In this paper, the Uncertainty Quantification (UQ) function of the high fidelity neutronics code NECP-X has been developed based on our home-developed uncertainty analysis code UNICORN, building a platform for the UQ of the transient calculation. Furthermore, the well-known space-time heterogeneous neutron kinetics benchmark C5G7 and its uncertainty propagation from the nuclear data to the interested key parameters of the core have been investigated. To address the problem of “the curse of dimensionality” caused by the large number of input parameters, the COST method has been applied to generate multivariate normal-distribution samples in uncertainty analysis. As a result, the law of the assembly/pin normalized power and their uncertainty with respect to time after introducing an instantaneous perturbation has been obtained. From the numerical results, it can be observed that the maximum relative uncertainties for the assembly normalized power can up to be about 1.65% and the value for the pin-wise power distributions can be about 2.71%.

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