Nam Dinh
Distinguished Professor of Nuclear Engineering

- 919-515-5421
- ntdinh@ncsu.edu
- Burlington Laboratory 3145
- Visit My Website
Dr. Nam Dinh has over thirty years of R&D and engineering experience in areas of nuclear reactor thermal hydraulics and nuclear power safety. His research is focused on multiphase flow systems with phase change and their application in nuclear power plant safety analysis, and severe accident risk assessment and management. Dr. Dinh’s work led to advances in nuclear reactor safety experimentation, modeling, simulation, and analysis. Dr. Dinh’s research on severe accident phenomenology contributed to resolution of safety issues in existing LWR plants and severe accident treatment in Generation III+ ALWR designs. Dr. Dinh’s current research is focused on data-driven modeling, validation, and application of digital twins, machine learning, and artificial intelligence in advanced reactor’s safety analysis and nearly autonomous control.
Prior to NCSU, Dr. Dinh was a researcher with Center for Risk Studies and Safety at University of California, Santa Barbara, a Chair Professor of Nuclear Power Safety at Sweden’s Royal Institute of Technology, and a Laboratory Fellow at Idaho National Laboratory. He is a NURETH Scholar, a recipient of ANS-THD Technical Achievement award, and a Fellow of the American Nuclear Society (ANS).
Education
Nuclear Engineering
Moscow Power Engineering Institute
Nuclear Engineering
Moscow Power Engineering Institute
Thermal Physics/Nuclear Engineering
Moscow Power Engineering Institute
Research Description
A general area of Dr. Dinh's research interest is modeling and analysis of multi-phase thermal-fluid phenomena of importance to nuclear reactor design and safety. Of particular interest are physics and prediction of boiling heart transfer, critical heat flux, and other intense multi-phase interactions that govern nuclear reactor safety margins. Dr. Dinh’s current research is focused on data-driven modeling, validation, and application of digital twins, machine learning, and artificial intelligence in advanced reactor’s safety analysis and nearly autonomous control.
Publications
';Grants
- Data Driven Analysis Tool for Predicting High-Speed Airbreathing Engine Performance
- US Dept. of Defense (DOD)(1/10/23 - 1/09/26)
- Trustworthiness of Digital-Twin-based Automation Technology in Nuclear Power Plant Operation
- US Nuclear Regulatory Commission(9/30/22 - 9/29/25)
- Center for Thermal-fluids Application in Nuclear Energy: Establishing the Knowledge Base for Thermal-hydraulic Multiscale Simulation to Accelerate the Deployment of Advanced Reactors.
- US Dept. of Energy (DOE)(10/01/20 - 9/30/24)
- Digital Engineering and Predictive Capability Maturity Model
- US Dept. of Energy (DOE)(8/16/21 - 6/30/24)
- Risk Assessment of Digital Human System Interface in Nuclear Power Plants
- US Dept. of Energy (DOE)(12/15/20 - 9/30/23)
- Release No, 00002 NUC Contract Proposal for FY 15 (2014-2015). Formerly ACE - Academic Center for Excellence
- US Dept. of Energy (DOE)(10/15/14 - 9/30/23)
- Pilot Study on Two-Phase Flow DNS Application to Heat Transfer Enhancement Pipe
- Mitsubishi Heavy Industries, Ltd.(10/01/22 - 5/15/23)
- Two-Phase Flow DNS Phase 2 Project
- Mitsubishi Heavy Industries, Ltd.(5/01/21 - 10/31/22)
- Development of a Nearly Autonomous Management and Control System for Advanced Reactors
- US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(10/01/18 - 9/30/21)
- Severe Accidents in Nuclear Power Plants (Research and Technical Assistance Related to Severe Accidents in Nuclear Power Plants)
- US Nuclear Regulatory Commission(9/08/16 - 9/07/21)