Xu Wu

Assistant Professor of Nuclear Engineering

  • Burlington Laboratory 2110
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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 (ROM). 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 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 Data Analytics, 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 Accident Tolerant Plants (ATPs), Small Modular Reactors (SMRs) and Gen-IV 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

B.S. 2011

Nuclear Engineering

Shanghai Jiao Tong University

M.S. 2013

Nuclear Engineering

University of Illinois at Urbana - Champaign

Ph.D. 2017

Nuclear 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, Bayesian Inference and Model Inversion; (3) Mathematical representations of model discrepancy to improve model predictive capabilities; (4) Physics-Informed Machine Learning, Deep Learning; (5) System Thermal-Hydraulics, Nuclear Fuel Performance modeling, Multi-physics coupled simulation; (6) Accident Tolerant Fuels (ATFs) modeling, etc.

Publications

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), In Transactions of the American Nuclear Society (Vol. 118, pp. 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
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory
Wu, X., Kozlowski, T., Meidani, H., & Shirvan, K. (2018), Nuclear Engineering and Design, 335, 339–355. https://doi.org/10.1016/j.nucengdes.2018.06.004
Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data
Wu, X., Kozlowski, T., & Meidani, H. (2018), Reliability Engineering and System Safety, 169, 422–436. https://doi.org/10.1016/j.ress.2017.09.029
On the connection between sensitivity and identifiability for inverse uncertainty quantification
Wu, X., Shirvan, K., & Kozlowski, T. (2018), In Transactions of the American Nuclear Society (Vol. 118, pp. 411–414).
Sensitivity and uncertainty analysis for fuel performance evaluation of Cr 2 O 3 -doped UO 2 fuel under LB-LOCA
Che, Y., Wu, X., Pastore, G., Hales, J., & Shirvan, K. (2018), In Transactions of the American Nuclear Society (Vol. 119, pp. 440–443).
System code evaluation of accident tolerant claddings during BWR station blackout accident
Wu, X., & Shirvan, K. (2018), In Transactions of the American Nuclear Society (Vol. 119, pp. 444–447).
Validating trace void fraction predictive capability using the quantitative area validation metric
Wu, X., Shirvan, K., & Kozlowski, T. (2018), In Transactions of the American Nuclear Society (Vol. 118, pp. 423–426).

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