Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods

Lu, Cihang; Wu, Zeyun; Wu, Xu

doi:10.1080/00295450.2020.1805259

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…It was shown that IUQ with biased data can lead to posterior distributions that cannot be extrapolated to different set of experimental condition. In a more recent work, Lu et al [86] used MBA to an one-dimensional thermal stratification model for pool-type sodium-cooled fast reactors. The FUQ results showed that the quantified parameter uncertainties effectively improved the predictive capability of the model.…”

Section: Marginalization Is Not Neededmentioning

confidence: 99%

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

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Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear community generally means forward UQ (FUQ), in which the information flow is from the inputs to the outputs. Inverse UQ (IUQ), in which the information flow is from the model outputs and experimental data to the inputs, is an equally important component of UQ but has been significantly underrated until recently. FUQ requires knowledge in the input uncertainties which has been specified by expert opinion or user self-evaluation. IUQ is defined as the process to inversely quantify the input uncertainties based on experimental data.This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes. IUQ methods can be categorized by three main groups: frequentist (deterministic), Bayesian (probabilistic), and empirical (design-ofexperiments). We used eight metrics to evaluate an IUQ method, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. Twelve IUQ methods are reviewed, compared, and evaluated based on these eight metrics. Such comparative evaluation will provide a good guidance for users to select a proper IUQ method based on the IUQ problem under investigation.

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Section: Marginalization Is Not Neededmentioning

confidence: 99%

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Sensitivity analysis of the 1-D SFR thermal stratification model via discrete adjoint sensitivity method

2020

Nuclear Engineering and Design

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Uncertainty Quantification of the 1-D SFR Thermal Stratification Model via the Latin Hypercube Sampling Monte Carlo Method

2021

Nuclear Technology

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Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods

Cited by 4 publications

References 17 publications

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes

Sensitivity analysis of the 1-D SFR thermal stratification model via discrete adjoint sensitivity method

Uncertainty Quantification of the 1-D SFR Thermal Stratification Model via the Latin Hypercube Sampling Monte Carlo Method

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