OPTIMAL UNCERTAINTY QUANTIFICATION OF A RISK MEASUREMENT FROM A THERMAL-HYDRAULIC CODE USING CANONICAL MOMENTS

@article{Stenger2019OPTIMALUQ,
  title={OPTIMAL UNCERTAINTY QUANTIFICATION OF A RISK MEASUREMENT FROM A THERMAL-HYDRAULIC CODE USING CANONICAL MOMENTS},
  author={J{\'e}r{\^o}me Stenger and Fabrice Gamboa and Merlin Keller and Bertrand Iooss},
  journal={arXiv: Methodology},
  year={2019}
}
We study an industrial computer code related to nuclear safety. A major topic of interest is to assess the uncertainties tainting the results of a computer simulation. In this work we gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. To that extent, we evaluate the maximum quantile over a class of distributions defined only by constraints on their moments. Two options are available when dealing with… 

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References

SHOWING 1-10 OF 43 REFERENCES

Advanced Methodology for Uncertainty Propagation in Computer Experiments with Large Number of Inputs

TLDR
This work proposes a methodology which combines several advanced statistical tools: initial space-filling design, screening to identify the noninfluential inputs, and Gaussian process (Gp) metamodel building with the group of influential inputs as explanatory variables, which provides a more accurate estimation of the 95% quantile and associated confidence interval than the empirical approach.

Integrated Methodology for Thermal-Hydraulic Code Uncertainty Analysis with Application

TLDR
The key elements of the uncertainty analysis methodology are described and its application to the LOFT test facility LBLOCA is described, which uses an efficient Monte Carlo sampling technique for the propagation of uncertainty.

Uncertainties and probabilities in nuclear reactor regulation

The State-of-the-Art Theory and Applications of Best-Estimate Plus Uncertainty Methods

The approval of the revised rule on the acceptance of emergency core cooling system performance in 1988 triggered a significant interest in the development of codes and methodologies for uncertainty

Controlled stratification for quantile estimation

In this paper we propose and discuss variance reduction techniques for the estimation of quantiles of the output of a complex model with random input parameters. These techniques are based on the use

Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes

TLDR
The performance of the two techniques is compared on three well-known analytical benchmarks (Ishigami, G-Sobol and Morris functions) as well as on a realistic engineering application (deflection of a truss structure).

Estimating percentiles of uncertain computer code outputs

TLDR
A deterministic computer model is to be used in a situation where there is uncertainty about the values of some or all of the input parameters, which induces uncertainty in the output of the model.