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Xu Wu
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Neutronics and fuel performance evaluation of accident tolerant FeCrAl cladding under normal operation conditions
X Wu, T Kozlowski, JD Hales
Annals of Nuclear Energy 85, 763-775, 2015
1242015
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory
X Wu, T Kozlowski, H Meidani, K Shirvan
Nuclear Engineering and Design 335, 339–355, 2018
1072018
Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data
X Wu, T Kozlowski, H Meidani
Reliability Engineering & System Safety 169, 422-436, 2017
722017
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE
X Wu, T Kozlowski, H Meidani, K Shirvan
Nuclear Engineering and Design 335, 417-431, 2018
552018
Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model
X Wu, T Mui, G Hu, H Meidani, T Kozlowski
Nuclear Engineering and Design 319, 185-200, 2017
372017
Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion
X Wu, T Kozlowski
Nuclear Engineering and Design 313, 29-52, 2017
362017
Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification
X Wu, K Shirvan, T Kozlowski
Journal of computational physics 396, 12-30, 2019
322019
A Comprehensive Survey of Inverse Uncertainty Quantification of Physical Model Parameters in Nuclear System Thermal-Hydraulics Codes
X Wu, Z Xie, F Alsafadi, T Kozlowski
Nuclear Engineering and Design 384, 111460, 2021
312021
Coupling of system thermal–hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error
X Wu, T Kozlowski
Annals of Nuclear Energy 75, 377-387, 2015
302015
Gaussian process–based inverse uncertainty quantification for trace physical model parameters using steady-state psbt benchmark
C Wang, X Wu, T Kozlowski
Nuclear Science and Engineering 193 (1-2), 100-114, 2019
222019
System code evaluation of near-term accident tolerant claddings during boiling water reactor short-term and long-term station blackout accidents
X Wu, K Shirvan
Nuclear Engineering and Design 356, 110362, 2020
212020
Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release
Y Che, X Wu, G Pastore, W Li, K Shirvan
Annals of Nuclear Energy 153, 108046, 2021
192021
Kriging-based surrogate models for uncertainty quantification and sensitivity analysis
X Wu, C Wang, T Kozlowski
182017
Neutronics and fuel performance evaluation of accident tolerant fuel under normal operation conditions
X Wu, P Sabharwall, J Hales
Idaho National Lab.(INL), Idaho Falls, ID (United States), 2014
162014
Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
X Wu
University of Illinois at Urbana-Champaign, 2017
142017
Surrogate-based inverse uncertainty quantification of TRACE physical model parameters using steady-state PSBT void fraction data
C Wang, X Wu, T Kozlowski
Proc. 17th Int. Topl. Mtg. Nuclear Reactor Thermal Hydraulics (NURETH-17), 3-8, 2017
142017
Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data
Z Xie, W Jiang, C Wang, X Wu
Annals of Nuclear Energy 165, 108782, 2022
112022
Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing
Z Xie, F Alsafadi, X Wu
Nuclear Engineering and Design 383, 111423, 2021
112021
System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents
Y Jin, X Wu, K Shirvan
Nuclear Engineering and Design 368, 110814, 2020
112020
Global sensitivity analysis of trace physical model parameters based on bfbt benchmark
X Wu, C Wang, T Kozlowski
92017
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