Education

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

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Grants

Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project

NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/22)

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Probabilistic and AI/ML Approaches in Structural Engineering, CNEFS Core Project

Sponsor: NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)

Start Date: 1/01/22

End Date: 12/31/22

Abstract

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. This project proposes development of an Artificial Intelligence (AI) framework for the data-driven condition monitoring of nuclear structural systems and equipment, where the vibration response is governed by multiple localized modes unlike that in buildings and bridges. Hence, techniques such as signal processing and pattern recognition will be employed to extract degradation-sensitive features. Degraded locations can potentially exhibit damage such as localized yielding, cyclic fatigue, or initiation of cracking. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, this research proposes a framework, which utilizes sensor response to generate an AI database for predicting degraded locations and severity in nuclear structural systems and equipment. Degradation severity will be classified as minor, moderate, and severe, along with incorporation of uncertainty.

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COBRA-TF (CTF) Modeling Applicable to High Burnup High Enrichment (HBHE) Fuel

US Dept. of Energy (DOE)(3/22/22 - 3/21/23)

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COBRA-TF (CTF) Modeling Applicable to High Burnup High Enrichment (HBHE) Fuel

Sponsor: US Dept. of Energy (DOE)

Start Date: 3/22/22

End Date: 3/21/23

Abstract

Transient fuel rod analysis addresses fuel rod response during non-Loss of Coolant Accidents (non-LOCA) accidents. The analysis provides time-dependent fuel temperature, rod internal pressure, and rod surface heat flux as input to Departure from Nucleate Boiling (DNB) Ratio (DNBR), fuel melt and cladding stress evaluations under reactor design conditions ranging from nucleate boiling to post-DNB heat transfer as well as high fuel burnup. This project is expected to develop and validate full core CTF models and fluid solutions including the more robust two-phase flow and high-resolution modeling capabilities than the existing Thermal-Hydraulic (T/H) design code.

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Artificial Intelligence Based Process Control and Optimization for Advanced Manufacturing

US Dept. of Energy (DOE)(11/08/21 - 9/30/22)

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Artificial Intelligence Based Process Control and Optimization for Advanced Manufacturing

Sponsor: US Dept. of Energy (DOE)

Start Date: 11/08/21

End Date: 9/30/22

Abstract

The objective of this proposal is to develop a novel Artificial Intelligence (AI)-based Process Control and Optimization (PC&O) methodology for Advanced Manufacturing (AM). Process-informed design, which has the potential of enabling transformative manufacturing operations, requires online PC&O. This project will fill the gap of lacking an online interaction mechanism in the process-informed design by providing the capability to intelligently control and optimize AM processes instead of the existing trial and error approach. To achieve this goal, AI-based control algorithms will be developed by employing Deep Reinforcement Learning (DRL). To reduce the computational expense with AM models, physics- informed Reduced Order Models (ROMs) will be developed. The AI-based control algorithms will employ the ROMs’ predictions to adaptively inform processing decisions in a simulation environment. In support of this research, an online training integrated capability will be developed based on the Multiphysics Object Oriented Simulation Environment Stochastic Tools Module (MOOSE-STM). Preliminary demonstration and validation of the developed PC&O methodology will be carried out with Idaho National Laboratory (INL)’s laser additive manufacturing system (i.e., LENS).

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Development of PINN for AM optimization

US Dept. of Energy (DOE)(1/06/20 - 9/30/20)

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Development of PINN for AM optimization

Sponsor: US Dept. of Energy (DOE)

Start Date: 1/06/20

End Date: 9/30/20

Abstract

The major objective is to develop Physics-Informed Neural Networks (PINN) techniques to assist the optimization of Additive Manufacturing (AM) process parameters such as applied laser power, traveling speed and scan style, to achieve desired nuclear fuel thermal-mechanical properties and fracture toughness at engineering scale. The project will focus on two tasks: (1) developing and demonstrating efficient Physics-Informed Deep Learning (PIDL) algorithm to solve the underlying Partial Differential Equations (PDEs) in the AM process; (2) determining an optimal set of AM process parameters through optimization using the PINN model.

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