UNCERTAINTY PROPAGATION FOR LWR BURNUP BENCHMARK USING SAMPLING BASED CODE SCALE/SAMPLER

Daniel Campolina,Jan Frybort

doi:10.14311/app.2018.14.0008

Daniel Campolina, Jan Frybort

Open Access

https://doi.org/10.14311/app.2018.14.0008

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Abstract

Sampling based method is adopted in many fields of engineering and it is currently used to propagate uncertainties from physical parameters and from nuclear data, to integral indicators of nuclear systems. The total uncertainty associated with a model simulation is of major importance for safety analysis and to guide vendors about acceptable tolerance limits for nuclear installations parts. This work presents some calculations to propagate uncertainties for a nuclear reactor fuel element modeled in SCALE/TRITON, using the sampling tool SCALE/SAMPLER. Results showed that that the influence of input uncertainties on kinf is more pronounced in the fresh core other than the depleted core and the contribution from studied manufacturing uncertainties is smaller than the contribution of nuclear data uncertainties.

Highlights

In a global picture, uncertainty quantification (UQ) is the process of characterizing input uncertainties, forward propagating these uncertainties through a computational model, and performing statistical assessments on the resulting responses
Uncertainty propagation analysis was performed for the burnup pin-cell exercise I-b of the Uncertainty Analysis in Modeling (UAM) benchmarks
The SAMPLER code using SCALE 6.2 56groupcov covariance library had the results compared against participant-averaged result from the benchmark exercise I-b

Summary

Introduction

Uncertainty quantification (UQ) is the process of characterizing input uncertainties, forward propagating these uncertainties through a computational model, and performing statistical assessments on the resulting responses. In this forward propagation, illustrated in Fig., probabilistic or interval information on parametric inputs are mapped through the computational model to assess statistics or intervals on outputs. Uncertainties and correlations among experimentally measured cross-sections constitute the so called nuclear data covariance libraries. The sampling based approach can be used to sample physical parameters like dimensions and densities and joint probability density functions given in the nuclear data covariance libraries. The last produces a random sample for the nuclear cross-sections that are used in a transport calculation

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Results

Conclusion