Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 6 of 6 for “"Inverse Uncertainty Quantification"”.
-
Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
Forward quantification of uncertainties in code responses require knowledge of input model parameter uncertainties. Nuclear thermal-hydraulics codes such as RELAP5 and TRACE do not provide any information on physical model parameter uncertainties. A framework was developed to quantify input model …
-
Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… performance or safety margins. Forward uncertainty propagation requires knowledge in the statistical information for computer model random inputs, for example, the mean, variance, Probability Density Functions (PDFs), upper and lower bounds, etc. Historically, ``expert judgment'' or …
-
Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
Uncertainty quantification of computer simulation response requires knowledge of input model parameter uncertainty. However, like most system codes, nuclear thermal-hydraulics code TRACE does not provide any information on statistical properties of input model parameters. Moreover, the input model …
-
Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning
Inverse problems and inverse uncertainty quantification (UQ) are challenging issues when dealing with complex and highly non-linear functions. Methods have been developed to decrease the computational burden by using the Gaussian Process (GP) emulator model framework to approximate the input-output …
-
Scientific deep learning for efficient modeling and uncertainty quantification in engineering systems
… on the quantities of interest. Conducting uncertainty quantification with Monte Carlo methods are often infeasible because of the need to execute a large number of forward model evaluations to achieve converged distributions or statistics. For systems characterized by numerous input …
-
A hierarchical Bayesian calibration framework for quantifying input uncertainties in thermal-hydraulics simulation models
In the framework of Best Estimate plus Uncertainty (BEPU) methodology, the uncertainties involved in simulations must be quantified to prove that the investigated design is reasonable and acceptable. The predictive uncertainties are usually calculated by propagating input uncertainties through the …