A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes

  title={A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes},
  author={Xu Wu and Ziyu Xie and Farah Alsafadi and Tomasz Kozlowski},
  journal={Nuclear Engineering and Design},
3 Citations
Digital Twin Concepts with Uncertainty for Nuclear Power Applications
For nuclear power applications, DT development should rely first on mechanistic model-based methods to leverage the extensive experience and understanding of these systems, and model-free techniques can then be adopted to selectively, and correctively, augment limitations in the model- based approaches.


Bayesian Uncertainty Quantification of Physical Models in Thermal-Hydraulics System Codes
The present doctoral research aims to quantify the uncertainty of physical model parameters implemented in a nuclear thermal-hydraulics system code based on experimental data by developing a methodology to use experimental data to inform these uncertainties in a more objective manner.
SAPIUM: A Generic Framework for a Practical and Transparent Quantification of Thermal-Hydraulic Code Model Input Uncertainty
The SAPIUM project has been proposed toward the construction of a clear and shared systematic approach for IUQ, which consists of five elements in a step-by-step approach to perform a meaningful model IUQ and validation as well as some good-practice guideline recommendations for each step.
Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark
The issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark).
Uncertainties in Predictions by System Thermal-Hydraulic Codes: The CASUALIDAD Method
The present paper deals with the description of the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes, based on the Bayesian inference technique, and on the availability of experimental data by which computer model predictions can be improved and the ranges of variation of ‘all’ input parameters can be characterized.
The CASUALIDAD Method for Uncertainty Evaluation of Best-Estimate System Thermal-Hydraulic Calculations
Abstract Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis (UA) that reduces the subjective and user-defined ways to manage experimental data and
Effect of mesh refinement on the estimation of model input parameters using Inverse Uncertainty Quantification