Xu Wu
Assistant Professor of Nuclear Engineering
- 919-515-6570
- xwu27@ncsu.edu
- Burlington Laboratory 2110
- View CV
Dr. Wu received his B.S. degree in nuclear engineering from the Shanghai Jiao Tong University in China in 2011. He obtained his Ph.D. degree in nuclear engineering in 2017 from the University of Illinois at Urbana – Champaign. Prior to joining NC State, he was a Postdoctoral Research Associate in the Department of Nuclear Science and Engineering at Massachusetts Institute of Technology. Dr. Wu’s research has revolved around uncertainty/sensitivity analysis, calibration, validation and reduced order modeling. The goal is to build a comprehensive framework that integrates calibration and validation to improve prediction. This framework accounts for all major sources of quantifiable uncertainties in Modeling & Simulation (M&S), i.e., uncertainties from parameter, experiment, model and code. It also builds an intelligent feedback between simulations and experiments and essentially bridges the gap between models and data. This framework provides a potential solution to the ANS Nuclear Grand Challenge on “Simulation & Experimentation”.
Dr. Wu’s current and future research aims at combining scientific Machine Learning, experimentation and M&S into a unified approach to improve the predictive capabilities of computer models and enable reduced reliance on expensive measurement data. Additionally, the application of such research will be focused on risk and economics evaluations of advanced nuclear reactors, such as small modular reactors and micro-reactors. The ultimate goal is to dramatically reduce the capital and operating costs of nuclear systems to maintain global technology leadership for nuclear energy.
Education
Nuclear Engineering
Shanghai Jiao Tong University
Nuclear, Plasma and Radiological Engineering
University of Illinois at Urbana - Champaign
Nuclear, Plasma and Radiological Engineering
University of Illinois at Urbana - Champaign
Research Description
Dr. Wu's research interests include: (1) Calibration, Validation, Data Assimilation, Uncertainty and Sensitivity Analysis; (2) Computational Statistics, Reduced Order Modeling; (3) Bayesian Inference and Model Inversion; (4) Scientific Machine Learning; (5) System Thermal-Hydraulics, Nuclear Fuel Performance modeling, Multi-physics coupled simulation; (6) Small Modular Reactors, Micro-reactors
Publications
- Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data
- Xie, Z., Jiang, W., Wang, C., & Wu, X. (2022), ANNALS OF NUCLEAR ENERGY, 1. https://doi.org/10.1016/j.anucene.2021.108782
- A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes
- Wu, X., Xie, Z., Alsafadi, F., & Kozlowski, T. (2021), NUCLEAR ENGINEERING AND DESIGN, 12. https://doi.org/10.1016/j.nucengdes.2021.111460
- Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release
- Che, Y., Wu, X., Pastore, G., Li, W., & Shirvan, K. (2021), ANNALS OF NUCLEAR ENERGY, 153. https://doi.org/10.1016/j.anucene.2020.108046
- Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods
- Lu, C., Wu, Z., & Wu, X. (2021), Nuclear Technology, 10, 1–19. https://doi.org/10.1080/00295450.2020.1805259
- Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing
- Xie, Z., Alsafadi, F., & Wu, X. (2021), NUCLEAR ENGINEERING AND DESIGN, 11. https://doi.org/10.1016/j.nucengdes.2021.111423
- System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents
- Jin, Y., Wu, X., & Shirvan, K. (2020), NUCLEAR ENGINEERING AND DESIGN, 368. https://doi.org/10.1016/j.nucengdes.2020.110814
- Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification
- Wu, X., Shirvan, K., & Kozlowski, T. (2019), Journal of Computational Physics, 396, 12–30. https://doi.org/10.1016/j.jcp.2019.06.032
- Gaussian Process–Based Inverse Uncertainty Quantification for TRACE Physical Model Parameters Using Steady-State PSBT Benchmark
- Wang, C., Wu, X., & Kozlowski, T. (2019), Nuclear Science and Engineering, 193(1-2), 100–114. https://doi.org/10.1080/00295639.2018.1499279
- Bayesian calibration and uncertainty quantification for trace based on PSBT benchmark
- Wang, C., Wu, X., Borowiec, K., & Kozlowski, T. (2018), Transactions of the American Nuclear Society, 118, 419–422.
- Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE
- Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018), Nuclear Engineering and Design, 335, 417–431. https://doi.org/10.1016/j.nucengdes.2018.06.003
Grants
- Artificial Intelligence Based Process Control and Optimization for Advanced Manufacturing
- US Dept. of Energy (DOE)(11/08/21 - 9/30/22)
- Development of PINN for AM optimization
- US Dept. of Energy (DOE)(1/06/20 - 9/30/20)