Profile_photo_Xingang_Zhao

Xingang Zhao

R&D Associate Staff

Contact

zhaox2@ornl.gov | 865.314.1618

Bio

Dr. Xingang Zhao is an R&D Associate Staff member in the Nuclear Energy and Fuel Cycle Division at Oak Ridge National Laboratory (ORNL). He received his Ph.D. in Nuclear Science and Engineering from Massachusetts Institute of Technology. His research interests span multiple disciplines of clean energy systems and their intersections with artificial intelligence and decision science. He has been a major contributor to a diverse portfolio of DOE, NRC, and ORNL funded research projects that advance the state of the art of modeling & simulation and digital engineering for nuclear and renewable energy applications. He has authored more than 45 publications, including journal articles, conference papers, book chapters, and technical reports. He is a member of the American Nuclear Society and its Alpha Nu Sigma National Honor Society.

Research Interests

Clean Energy | Nuclear Engineering | Digital Technologies

  • Reactor and systems engineering, computational thermal hydraulics (system/subchannel/computational fluid dynamics), instrumentation and controls (I&C), safety and reliability analysis, probabilistic risk assessment, multiphysics integration, reactor design, advanced reactors, hybrid/integrated energy systems
  • Modeling and simulation, artificial intelligence and machine learning, digital twin, data science and data analytics, validation and verification, uncertainty quantification, sensitivity analysis, optimization, numerical methods, high performance computing
  • Techno-economic analysis, life cycle assessment, low-carbon energy, climate & environment

Proposals

Led successful research proposals for the following awards with total funding of more than $10.3M:

  • DOE-SC-BES – Accelerator & Detector Research Program – "Machine Learning for Improving Accelerator and Target Performance" (Contributor, 2023-26)
  • DOE-EERE – Advanced Materials & Manufacturing Technologies Office – "Portside 3D Printed Lightweight Concrete Foundations for Offshore Wind Turbines" (Co-PI, 2023-26)
  • ORNL Director's Research & Development Program – Fusion Initiative – "Engineering Optimization of First Wall Protection Limiters" (Contributor, 2023-24)
  • ORNL Director's Research & Development Program – Decision Science for Transformational Decarbonization Initiative – "Process Analysis to Guide Research toward Achieving Economically Feasible and Sustainable Direct Air Capture Technology" (Co-PI, 2023-24)
  • ORNL Director's Research & Development Program – AI Initiative – "Foundations of Artificial Intelligence for Robust Engineering and Science" (Contributor, 2022-25)
  • ORNL Director's Research & Development Program – Decision Science for Transformational Decarbonization Initiative – "Integrated Techno-Economic and Life Cycle Assessment of Concrete Alternatives" (PI, 2022-23)
  • ORNL – Climate Change Science Institute – "Carbon Handprint: Adopting Nuclear and Renewables in Comparison to Continued Use of Fossil Fuels" (PI, 2021)

Publications

Peer-Reviewed Journal Articles

  1. Zhao, X., Wang, X., and Golay, M. (2023). Bayesian Network–Based Fault Diagnostic System for Nuclear Power Plant Assets. Nuclear Technology, 209:401–418. doi
  2. Salko, R.K., Wysocki, A., Blyth, T., Toptan, A., Hu, J., Kumar, V., Dances, C., Dawn, W., Sung, Y., Kucukboyaci, V., Gurecky, W., Lange, T., Zhao, X. et al. (2022). CTF: A Modernized, Production-Level, Thermal Hydraulic Solver for the Solution of Industry-Relevant Challenge Problems in Pressurized Water Reactors. Nuclear Engineering and Design, 397:111927. doi
  3. Zhao, X., Huning, A.J., Burek, J., Guo, F., Kropaczek, D.J., and Pointer, W.D. (2022). The Pursuit of Net-Positive Sustainability for Industrial Decarbonization with Hybrid Energy Systems. Journal of Cleaner Production, 362:132349. doi
  4. Guo, F., Zhao, X., Gregory, J., and Kirchain, R. (2022). A Weighted Multi-Output Neural Network Model for the Prediction of Rigid Pavement Deterioration. International Journal of Pavement Engineering, 23(8):2631-2643. doi
  5. Kim, J., Zhao, X., Shah, A., and Kang, H.G. (2021). System Risk Quantification and Decision Making Support Using Functional Modeling and Dynamic Bayesian Network. Reliability Engineering & System Safety, 215:107880. doi
  6. Zhao, X., Kim, J., Warns, K., Wang, X., Ramuhalli, P., Cetiner, S., Kang, H.G., and Golay, M. (2021). Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review with Special Focus on Data-Driven Methods. Frontiers in Energy Research, 9:696785. doi
  7. Zhao, X., Salko, R.K., and Shirvan, K. (2021). Improved Departure from Nucleate Boiling Prediction in Rod Bundles Using a Physics-Informed Machine Learning-Aided Framework. Nuclear Engineering and Design, 374:111084. doi
  8. Zhao, X., Shirvan, K., Salko, R.K., and Guo, F. (2020). On the Prediction of Critical Heat Flux Using a Physics-Informed Machine Learning-Aided Framework. Applied Thermal Engineering, 164:114540. doi
  9. Zhao, X., Wysocki, A., Shirvan, K., and Salko, R.K. (2019). Assessment of the Subchannel Code CTF for Single- and Two-Phase Flows. Nuclear Technology, 205:338–351. doi
  10. Zhao, X., Shirvan, K., Wu, Y., and Kazimi, M.S. (2016). Critical Power and Void Fraction Prediction of Tight Bundle Designs. Nuclear Technology, 196:553–567. doi

Refereed Conference Proceedings and Transactions

  1. Zhao, X., Puente, B.M., Liu, S., Lim, S.-H., Gurecky, W., Lu, D., Howell, M., Liu, F., Williams, W., and Ramuhalli, P. Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems. In: Proceedings of the 13th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2023). Knoxville, TN, USA, July 15–20, 2023
  2. Kim, J., Mikkelson, D., Wang, X., Zhao, X., and Kang, H.G. Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework. In: Proceedings of the 13th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2023). Knoxville, TN, USA, July 15–20, 2023
  3. Yadav, V., Agarwal, V., Jain, P., Ramuhalli, P., Zhao, X., Ulmer, C., Carlson, J., Eskins, D., and Iyengar, R. Technical Challenges and Gaps in Integration of Advanced Sensors, Instrumentation, and Communication Technologies with Digital Twins for Nuclear Application. In: Proceedings of the 13th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2023). Knoxville, TN, USA, July 15–20, 2023
  4. Zhao, X., Huning, A.J., Burek, J., Guo, F., Kropaczek, D.J., and Pointer, W.D. The Role of Hybrid Energy Systems in Decarbonizing Industry: A Carbon Handprint–Based Case Study. In: Transactions of the American Nuclear Society (2022 American Nuclear Society Annual Meeting). Anaheim, CA, USA, June 12–16, 2022
  5. Phathanapirom, B., Zhao, X., and Rader, J. A Decision Theoretic Framework to Developing Autonomous Control in Advanced Reactors. In: Transactions of the American Nuclear Society (2022 American Nuclear Society Annual Meeting). Anaheim, CA, USA, June 12–16, 2022
  6. Jin, Y., Zhao, X., and Shirvan, K. Constructing a New CHF Look-Up Table Based on the Domain Knowledge Informed Machine Learning Methodology. In: Proceedings of the 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19). Virtual, March 6–11, 2022
  7. Djeddou, M., Zhao, X., Hameed, I.A., and Rahmani, A. Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF). In: Proceedings of the 28th International Conference on Nuclear Engineering (ICONE28). Virtual, August 4–6, 2021
  8. Zhao, X. and Golay, M. Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network. In: Proceedings of the 12th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021). Virtual, June 14–17, 2021
  9. Kim, J., Zhao, X., Shah, A., and Kang, H.G. Physics-Informed Machine Learning-Aided System Space Discretization. In: Proceedings of the 12th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021). Virtual, June 14–17, 2021
  10. Jin, Y., Zhao, X., and Shirvan, K. Unified Domain Knowledge Informed Machine Learning Model for CHF Prediction. In: Transactions of the American Nuclear Society (2021 American Nuclear Society Annual Meeting). Virtual, June 14–16, 2021
  11. Zhao, X. and Shirvan, K. Physics-Informed Machine Learning-Aided Framework for CASL Challenge Problem Solving – A Demonstration and Prospects. In: Proceedings of the Consortium for Advanced Simulation of Light Water Reactors Virtual Meeting. Virtual, November 16–19, 2020
  12. Zhao, X., Shirvan, K., and Salko, R.K. A Physics-Informed Machine Learning-Aided Framework for Predicting Departure from Nucleate Boiling in Rod Bundles. In: Transactions of the American Nuclear Society (2019 American Nuclear Society Winter Meeting). Washington, DC, USA, November 17–21, 2019
  13. Zhao, X., Shirvan, K., and Salko, R.K. A Robust Mechanistic Approach to Prediction of Departure from Nucleate Boiling. In: Proceedings of the 2019 Light Water Reactor Fuel Performance Conference (Top Fuel 2019). Seattle, WA, USA, September 22–26, 2019
  14. Zhao, X., Shirvan, K., and Salko, R.K. Machine Learning–Based Critical Heat Flux Predictors in Subcooled and Low-Quality Flow Boiling. In: Proceedings of the 2018 International Topical Meeting on Advances in Thermal Hydraulics (ATH 2018). Orlando, FL, USA, November 11–15, 2018
  15. Wang, Y., Zhao, X., Dave, A.J., Shirvan, K., Hu, L., Nielsen, J.W., Murray, P., and Marlow, R. Implication of Transient CHF Model on ATR Fueled Experiments Safety Margin. In: Proceedings of the 2018 International Topical Meeting on Advances in Thermal Hydraulics (ATH 2018). Orlando, FL, USA, November 11–15, 2018
  16. Zhao, X., Wysocki, A., Salko, R.K., and Shirvan, K. Mechanistic Modeling of Departure from Nucleate Boiling under Transient Scenarios. In: Proceedings of the 2018 International Congress on Advances in Nuclear Power Plants (ICAPP 2018). Charlotte, NC, USA, April 8–11, 2018
  17. Zhao, X., Wysocki, A., Salko, R.K., and Shirvan, K. Validation and Benchmarking of CTF for Single- and Two-Phase Flow. In: Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-17). Xi’an, China, September 3–8, 2017
  18. Zhao, X., Shirvan, K., Wu, Y., and Kazimi, M.S. An Updated Approach to the Prediction of Dryout and Void Fraction for RBWR Bundles. In: Proceedings of the 16th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-16). Chicago, IL, USA, August 30–September 4, 2015

Book Chapters

  1. Zhao, X. (2020). Subchannel Codes: CTF and VIPRE-01. In: Nuclear Power Plant Design and Analysis Codes: Development, Validation, and Application. J. Wang, X. Li, C. Allison, and J. Hohorst, eds. (Woodhead Publishing), pp. 235–258. doi
  2. Gui, M. and Zhao, X. (2020). Multiphysics Coupling Plan. In: Nuclear Power Plant Design and Analysis Codes: Development, Validation, and Application. J. Wang, X. Li, C. Allison, and J. Hohorst, eds. (Woodhead Publishing), pp. 75–94. doi

Technical Reports, Theses, and Other Publications

  1. Gurecky, W., Ramuhalli, P., Williams, W., Zhao, X., Lim, S.-H., and Liu, S. (2024). AIRES-NODE. Computer Software, Version 0.0.1, Oak Ridge National Laboratory. doi
  2. Salko, R.K., Wysocki, A., Zhao, X., Hizoum, B., Gosdin, C., Kumar, V., Palmtag, S., and Avramova, M. (2023). CTF Validation and Verification: Version 4.4. ORNL/SPR-2023/3140, Oak Ridge National Laboratory
  3. Kumar, V., Williams, W., Zhao, X., and Gurecky, W. (2023). Digital Twin Development of FASTR. ORNL/SPR-2023/2958, Oak Ridge National Laboratory
  4. Yadav, V., Agarwal, V., Jain, P., Ramuhalli, P., Zhao, X., Ulmer, C., Carlson, J., Eskins, D., and Iyengar, R. (2023). State-of-Technology and Technical Challenges in Advanced Sensors, Instrumentation, and Communication to Support Digital Twin for Nuclear Energy Application. TLR-RES/DE/REB-2023-02, U.S. Nuclear Regulatory Commission. link
  5. Salko, R.K., Wysocki, A., Toptan, A., Porter, N., Zhao, X., Hizoum, B., Blyth, T., Magedanz, J., Dances, C., Gergar, M. et al. (2023). CTF Validation and Verification: Version 4.3. ORNL/SPR-2022/2500, Oak Ridge National Laboratory
  6. Wang, X., Golay, M., Kim, J., Warns, K., Kang, H.G., Zhao, X., and Phathanapirom, B. (2022). Final Report for Design of Risk-Informed Autonomous Operation for Advanced Reactor. DOE NEUP Project 19-17435
  7. Wang, X., Golay, M., Kim, J., Warns, K., Kang, H.G., Zhao, X., and Phathanapirom, B. (2022). Selection of SSC Degradation Scenarios and Case Studies for Demonstration of Operator Decision Support. DOE NEUP Project 19-17435 Milestone Report
  8. Zhong, X., Wang, J., Zhao, X., Liu, Y., and Revankar, S.T. (2022). Editorial: Artificial Intelligence Applications in Nuclear Energy. Frontiers in Energy Research, 10:965581
  9. Wang, X., Golay, M., Kim, J., Kang, H.G., Phathanapirom, B., and Zhao, X. (2021). Development of Candidate Reasoning Methods and Associated Decision-Making Metrics. DOE NEUP Project 19-17435 Milestone Report
  10. Wang, J., Zhong, X., Zhao, X., Yurko, J.P., and Revankar, S.T. (2021). Editorial: Nuclear Power Plant Equipment Prognostics and Health Management Based on Data-Driven Methods. Frontiers in Energy Research, 9:719245
  11. Salko, R.K., Wysocki, A., Toptan, A., Porter, N., Zhao, X., Blyth, T., Magedanz, J., Dances, C., Gergar, M., Gosdin, C. et al. (2020). CTF Validation and Verification: Version 4.2. CASL-U-2019-1887-002, DOE Consortium for Advanced Simulation of Light Water Reactors
  12. Zhao, X. and Golay, M. (2020). Symptom-Based Conditional Failure Probability Estimation for Selected Structures, Systems, and Components. DOE NEUP Project 19-17435 Milestone Report
  13. Zhao, X. (2020). Data for: On the prediction of critical heat flux using a physics-informed machine learning-aided framework. Mendeley Data, Version 1. link
  14. Zhao, X. (2019). Prediction of Departure from Nucleate Boiling in Subchannel Applications: from Mechanistic Modeling to Hybrid Framework. Ph.D. Thesis, Massachusetts Institute of Technology. link
  15. Zhao, X. (2018). Predicting Departure from Nucleate Boiling with Advanced Data- and Physics-Driven Approaches. CASL-U-2018-1676-000, DOE Consortium for Advanced Simulation of Light Water Reactors
  16. Zhao, X. (2018). Prediction of Steam-Water Flow Boiling Critical Heat Flux in Tubes and Annuli Using Physics-Informed Deep Feed-Forward Neural Networks. Massachusetts Institute of Technology
  17. Salko, R.K., Delchini, M.-O., Zhao, X., Pointer, W.D., and Gurecky, W. (2017). Summary of CTF Accuracy and Fidelity Improvements in FY17. CASL-U-2017-1428-000, DOE Consortium for Advanced Simulation of Light Water Reactors
  18. Zhao, X. and Salko, R.K. (2016). Validation and Benchmarking of CTF for Two-Phase Flow Using VIPRE-01. CASL-U-2016-1184-000, DOE Consortium for Advanced Simulation of Light Water Reactors
  19. Zhao, X. (2016). Critical Power Characteristics of Axially Heterogeneous Tight Bundle Designs. S.M. Thesis, Massachusetts Institute of Technology. link

Awards

  • Winner (mentor), Small Modular Reactor Workshop Pitch Competition, OECD Nuclear Energy Agency & Idaho National Laboratory (2023)
  • Judges' Award, "Your Science in a Nutshell" Research Talk Competition, ORNL (2022)
  • Winner, Human Factors, Instrumentation, and Controls Division Video Contest, American Nuclear Society (2022)
  • Postdoctoral Award for Outstanding Scholarly Output, Nuclear Energy and Fuel Cycle Division, ORNL (2021)
  • Best Team Presentation Award, Modeling, Experimentation, and Validation (MeV) Summer School, Idaho National Laboratory (2020)
  • Winner, Young Professional Thermal Hydraulics Research Competition, American Nuclear Society (2018)
  • Alpha Nu Sigma National Honor Society, American Nuclear Society (2016-present)
  • Academic Excellence Scholarship, European Foundation for Tomorrow's Energies, Institute of France (2012-2013)

Education

Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA

M.S., Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA

Dipl. Ing., Nuclear Engineering, National Institute for Nuclear Science and Technology (INSTN), Saclay, France

Dipl. Ing., Energy and Environmental Engineering, National Institute of Applied Sciences (INSA), Lyon, France

Professional Service

  • I&C Technical Chair, NPIC&HMIT 2025
  • Student Paper Competitions Organizer and Chair, NPIC&HMIT 2023 and PSA 2023
  • Chair, 2023 American Nuclear Society Young Professional Thermal Hydraulics Research Competition
  • Postdoctoral Engagement Committee Member, ORNL (2023-present)
  • Expert Group Member, OECD Nuclear Energy Agency AI & ML Task Force (2022-present)
    • Critical Heat Flux Exercises – Phase 1

Publications

View All

Key Links

Google Scholar
ORCID
LinkedIn
ResearchGate

Organizations