SAPIUM: A Generic Framework for a Practical and Transparent Quantification of Thermal-Hydraulic Code Model Input Uncertainty

Baccou, Jean; Zhang, Jinzhao; Fillion, Philippe; Damblin, Guillaume; Petruzzi, A.; Mendizábal, Rafael; Reventós, F.; Skorek, T.; Couplet, Mathieu; Iooss, Bertrand; Oh, Dong Yeol; Takeda, Takeshi; Sandberg, Nils

doi:10.1080/00295639.2020.1759310

Cited by 32 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these methods were introduced from other disciplines but have been adapted/improved for applications in system TH codes. The majority of these IUQ methods are based on statistical analysis, and they can be categorized by three main groups: frequentist, Bayesian, and empirical [33]. They are sometimes referred to as deterministic (optimization-based), probabilistic (sampling-based), and design-ofexperiments (DoE, forward propagation-based), respectively.…”

Section: Overview Of Available Iuq Methods For System Th Codesmentioning

confidence: 99%

“…OECD/NEA proposed another project in 2017, called SAPIUM (Systematic APproach for Input Uncertainty quantification Methodology) [32], to develop a systematic approach for quantification and validation of the uncertainty of the physical models in system TH codes. The main outcome of the project [33] is a first good-practices document that can be exploited for safety study in order to reach consensus among experts on recommended practices in IUQ.…”

Section: Overview Of International Benchmarks Related To Bepu and Uqmentioning

confidence: 99%

“…In this section, we briefly summarize the major elements of the SAPIUM approach. A comprehensive discussion on the technical aspects of each element can be found in [32] [33]. The five elements are further decomposed into 17 steps, as listed below:…”

Section: The Sapium Projectmentioning

confidence: 99%

See 2 more Smart Citations

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

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Overview Of Available Iuq Methods For System Th Codesmentioning

confidence: 99%

Section: Overview Of International Benchmarks Related To Bepu and Uqmentioning

confidence: 99%

See 1 more Smart Citation

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

Wu,

Xie,

Alsafadi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For many years, mathematical tools for the quantification of code uncertainties and sensitivities have been under development worldwide, for example, DAKOTA [7], RAVEN [8], SUNSET [9], SUSA [10], URANIE [11], and so on. There is a huge accumulated experience already in the nuclear community in performing UQ with Best Estimate (BE) thermalhydraulic system codes [12,13], and this is being extended to other fields, like fuel performance, neutronics, and sub-channel thermal hydraulics. So far, this has not been the case for severe accident codes, and few investigations have been focused on severe accidents and UQ in Europe [14] and elsewhere [15].…”

Section: Introductionmentioning

confidence: 99%

The EC MUSA Project on Management and Uncertainty of Severe Accidents: Main Pillars and Status

et al. 2021

View full text Add to dashboard Cite

In the current state of maturity of severe accident codes, the time has come to foster the systematic application of Best Estimate Plus Uncertainties (BEPU) in this domain. The overall objective of the HORIZON-2020 project on “Management and Uncertainties of Severe Accidents (MUSA)” is to quantify the uncertainties of severe accident codes (e.g., ASTEC, MAAP, MELCOR, and AC2) when modeling reactor and spent fuel pools accident scenarios of Gen II and Gen III reactor designs for the prediction of the radiological source term. To do so, different Uncertainty Quantification (UQ) methodologies are to be used for the uncertainty and sensitivity analysis. Innovative AM measures will be considered in performing these UQ analyses, in addition to initial/boundary conditions and model parameters, to assess their impact on the source term prediction. This paper synthesizes the major pillars and the overall structure of the MUSA project, as well as the expectations and the progress made over the first year and a half of operation.

show abstract

“…the higher the input dimensionality (e.g., in the presence of time series data), the higher the size of the training dataset should be to obtain metamodel accuracy. Rigorous (linear or nonlinear) approaches to reduce the input dimensionality (e.g., Principal Component Analysis, PCA, or Stacked Sparse Autoencoders) should be sought, with increased metamodel performances [137]; iii.…”

mentioning

confidence: 99%

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

et al. 2021

View full text Add to dashboard Cite

Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.

show abstract

SAPIUM: A Generic Framework for a Practical and Transparent Quantification of Thermal-Hydraulic Code Model Input Uncertainty

Cited by 32 publications

References 34 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

The EC MUSA Project on Management and Uncertainty of Severe Accidents: Main Pillars and Status

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

Contact Info

Product

Resources

About