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

Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Application in Nuclear System Thermal-Hydraulics Codes

Wang, C., Wu, X., & Kozlowski, T. (2023). , (ArXiv Preprint No. 2305.16622). https://doi.org/10.48550/arXiv.2305.16622

Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks

Moloko, L. E., Bokov, P. M., Wu, X., & Ivanov, K. N. (2023), ANNALS OF NUCLEAR ENERGY, 188. https://doi.org/10.1016/j.anucene.2023.109813

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, 165. https://doi.org/10.1016/j.anucene.2021.108782

Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models

Yaseen, M., & Wu, X. (2022, November 4), NUCLEAR SCIENCE AND ENGINEERING, Vol. 11. https://doi.org/10.1080/00295639.2022.2123203

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, 384. 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

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Yan, E., Sandhu, H., Bodda, S., Gupta, A., Wu, X., & Sabharwall, P. (2021). , . https://doi.org/10.2172/1824205

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, 383. 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

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Grants

Using Machine Learning to Predict Locations with Crud Buildup

Consortium for Nuclear Power (CNP)(7/01/22 - 6/30/23)

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Using Machine Learning to Predict Locations with Crud Buildup

Sponsor: Consortium for Nuclear Power (CNP)

Start Date: 7/01/22

End Date: 6/30/23

Abstract

The goal of this project is to develop Machine Learning algorithms based on convolutional neural networks to predict the locations of crud accumulation in a typical pressurized water reactor. The training data will be derived nodal data from VERA simulation for Catawba 1 cycle 6-8 and measured Crud Induced Power Shift (CIPS) index values for cycle 8. The output from the trained machine learning model will be the predicted CPIS Index.

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Statistical Approaches to Reduce Uncertainty in PSHA, CNEFS Enhancement Project

Electricite de France (EDF/DER)(1/01/23 - 12/31/23)

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Statistical Approaches to Reduce Uncertainty in PSHA, CNEFS Enhancement Project

Sponsor: Electricite de France (EDF/DER)

Start Date: 1/01/23

End Date: 12/31/23

Abstract

Probabilistic seismic hazard analysis (PSHA) is a key component in seismic design and performance assessment of engineering components. Accurate representations of rupture characteristics, wave propagation, and subsurface soil behavior are necessary to perform an accurate PSHA. However, in traditional PSHA, simplified empirical Ground Motion Models (GMMs) are used to estimate the ground motion levels. These GMMs neglect the inherent physical complexities in earthquake rupture and ground motion properties, such as slip heterogeneity, rupture directivity, and basin depth, among others. In addition, the paucity of ground motions recorded from large magnitude ruptures in the near-fault region, and from stable continentals regions (e.g. French Context) makes GMMs unsuitable for several contexts and applications. The objective of this research is to: 1) apply seismic ground motion methods to generate synthetic ground motions data set based on a real ground motion data set, 2) make use of synthetic ground motions to update GMMs using the Bayesian method already developed in a precedent collaboration (NCSU-EDF) and/or to develop a specific synthetic GMM (using only simulated motions) and/or to develop a hybrid GMM (using synthetic and real ground motions). The challenge of this research is to propose new alternatives to the PSHA based on simulated ground motions to complete the logic tree branches. The weight of logic tree branches of different simulated approaches and GMMs will be assigned to the validity of simulated and empirical models, with respect to the observed ground motions in regions under evaluation.

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

NCSU Center for Nuclear Energy Facilities and Structures (CNEFS)(1/01/22 - 12/31/23)

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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: 12/31/23

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 - 8/31/23)

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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: 8/31/23

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 - 9/30/23)

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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: 9/30/23

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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