A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes
Uncertainty Quantification (UQ) is an essential step in computational model validation
because assessment of the model accuracy requires a concrete, quantifiable measure of …
because assessment of the model accuracy requires a concrete, quantifiable measure of …
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory
In nuclear reactor system design and safety analysis, the Best Estimate plus Uncertainty
(BEPU) methodology requires that computer model output uncertainties must be quantified …
(BEPU) methodology requires that computer model output uncertainties must be quantified …
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE
Abstract Inverse Uncertainty Quantification (UQ) is a process to quantify the uncertainties in
random input parameters while achieving consistency between code simulations and …
random input parameters while achieving consistency between code simulations and …
Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release
One of the advanced nuclear fuel concepts for current commercial water-cooled reactors
focuses on microstructural modification of UO 2 fuel via dopants. Dopants can effectively …
focuses on microstructural modification of UO 2 fuel via dopants. Dopants can effectively …
Gaussian process–based inverse uncertainty quantification for trace physical model parameters using steady-state psbt benchmark
In the framework of Best Estimate Plus Uncertainty methodology, the uncertainties involved
in model predictions must be quantified to prove that the investigated design is reasonable …
in model predictions must be quantified to prove that the investigated design is reasonable …
A kriging surrogate model for uncertainty analysis of graphene based on a finite element method
Due to the inevitable presence of random defects, unpredictable grain boundaries in
macroscopic samples, stress concentration at clamping points, and unknown load …
macroscopic samples, stress concentration at clamping points, and unknown load …
Evaluation of public dose from FHR tritium release with consideration of meteorological uncertainties
Tritium management is a potentially significant issue in fluoride-salt-cooled high-
temperature reactors (FHRs), as these reactors can produce tritium at high rates. Potential …
temperature reactors (FHRs), as these reactors can produce tritium at high rates. Potential …
[PDF][PDF] Inverse uncertainty quantification by hierarchical bayesian inference for trace physical model parameters based on bfbt benchmark
In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the uncertainties
involved in simulations must be quantified to prove that the investigated design is …
involved in simulations must be quantified to prove that the investigated design is …
Bayesian perspective in BEPU licensing analysis
R Mendizábal - Nuclear Engineering and Design, 2019 - Elsevier
The main objective of this paper is giving insights about the Bayesian formulation of Best-
Estimate-Plus-Uncertainty (BEPU) methodologies of Nuclear Safety. It is written from a …
Estimate-Plus-Uncertainty (BEPU) methodologies of Nuclear Safety. It is written from a …
[PDF][PDF] Surrogate-based bayesian calibration of thermal-hydraulics models based on psbt time-dependent benchmark data
The uncertainties in thermal-hydraulics code predictions are usually calculated by
propagating input uncertainties through the best-estimate codes. However, the input …
propagating input uncertainties through the best-estimate codes. However, the input …