Jason Hou
Associate Professor
Director of Advanced Reactor Design and Optimization Research (ARDOR) Lab
![](https://ne.ncsu.edu/wp-content/uploads/2016/10/Hou_Jason_MH11824_1-1-500x500.jpg)
- 919-513-6705
- jhou8@ncsu.edu
- Burlington Laboratory 1139
- Visit My Website
- View CV
Dr. Jason Hou is an advocate of nuclear energy and the mission of his research is to promote nuclear energy primarily by advancing scientific understanding of advanced nuclear reactor technologies. There are four main research thrust areas: computational reactor physics, multiphysics modeling and simulation capabilities, advanced reactor design and fuel cycle analysis, and machine learning for reactor operation and maintenance.
Dr. Hou is current teaching NE 403 Nuclear Reactor Laboratory, NE 412/512 Nuclear Fuel Cycle, and co-teaching NE 491/591 Metal Cooled Reactor.
Dr. Hou is the Director of the Advanced Reactor Design and Optimization Research (ARDOR) Lab. He also serves as the Coordinator of the Nuclear Simulation Laboratory.
Education
Nuclear Engineering
Pennsylvania State University
Nuclear Engineering
University of Michigan
Nuclear Engineering
University of Tennessee
Engineering Physics
Tsinghua University
Research Description
Dr. Hou's area of research interest includes multi-physics reactor simulation, advanced reactors, fuel cycle analysis, uncertainty quantification, machine learning in engineering applications, and nuclear power plant simulator. Presently he performs studies on the Hi2Lo informing scheme for multi-physics simulation, sensitivity and uncertainty (S/U) analysis in modeling of various reactor systems, high-fidelity reactor core simulator, hybrid Monte Carlo (MC) and deterministic method for core calculations, machine learning for plant prognosis and diagnosis. He is the coordinator of the NEA/OECD homogenization-free time-dependent neutron transport benchmark (C5G7-TD).
Publications
- A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation
- Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., Yang, J., & Hou, J. (2024), RELIABILITY ENGINEERING & SYSTEM SAFETY, 247. https://doi.org/10.1016/j.ress.2024.110121
- A system diagnostic and prognostic framework based on deep learning for advanced reactors
- Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., & Hou, J. (2024), PROGRESS IN NUCLEAR ENERGY, 170. https://doi.org/10.1016/j.pnucene.2024.105114
- Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems
- Mikouchi-Lopez, J., Delipei, G., & Hou, J. (2024), Nuclear Science and Technology Open Research. https://doi.org/10.12688/nuclscitechnolopenres.17464.2
- Prototyping of a Machine Learning-Based Burnup Measurement Capability for Pebble Bed Reactor Fuel
- Rollins, N., Allan, I., & Hou, J. (2024, April 4), NUCLEAR SCIENCE AND ENGINEERING, Vol. 4. https://doi.org/10.1080/00295639.2024.2328937
- Reinforcement Learning-Based Control Sequence Optimization for Advanced Reactors
- Nguyen, K. H. N., Rivas, A., Delipei, G. K., & Hou, J. (2024), Journal of Nuclear Engineering. https://doi.org/10.3390/jne5030015
- Source term analysis of FeCrAl accident tolerant fuel using MELCOR
- Baker, U., Choi, Y.-J., Rollins, N., Nguyen, K., Jung, W., Whitmeyer, A., … Lindley, B. (2024), ANNALS OF NUCLEAR ENERGY, 202. https://doi.org/10.1016/j.anucene.2024.110482
- A hybrid neutronics method with novel fission diffusion synthetic acceleration for criticality calculations
- Chen, J., Hou, J., & Ivanov, K. (2023), NUCLEAR ENGINEERING AND TECHNOLOGY, 55(4), 1428–1438. https://doi.org/10.1016/j.net.2022.12.022
- An Efficient High-to-Low Iterative Method for Light Water Reactor Analysis Based on NEAMS Tools
- Ni, K., & Hou, J. (2023), Nuclear Science and Engineering, 197(8), 1700–1716. https://doi.org/10.1080/00295639.2022.2158706
- A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models
- Andersen, B., Hou, J., Godfrey, A., & Kropaczek, D. (2022), Eng. https://doi.org/10.3390/eng3040036
- CTF-PARCS Core Multi-Physics Computational Framework for Efficient LWR Steady-State, Depletion and Transient Uncertainty Quantification
- Delipei, G. K., Rouxelin, P., Abarca, A., Hou, J., Avramova, M., & Ivanov, K. (2022), ENERGIES, 15(14). https://doi.org/10.3390/en15145226
Grants
- Development of LWR Fuel Reload Optimization Framework
- Battelle Energy Alliance, LLC (Idaho National Laboratory)(10/28/21 - 10/31/24)
- Machine Learning Based Lattice and Core Optimization Methodology for Multi-Cycle Operations ??? CNP Core Project 6
- Consortium for Nuclear Power (CNP)- Dept of Nuclear Engineering(7/01/22 - 6/30/24)
- Performance Evaluation of Accident Tolerant Fuel in Optimized LWR Designs
- US Dept. of Energy (DOE)(2/17/22 - 9/30/23)
- Xe-100 Burn Up Measurement System, CNP Core Project #5
- Consortium for Nuclear Power (CNP)- Dept of Nuclear Engineering(7/01/21 - 6/30/23)
- Machine Learning Model Development in 2022 (ARPA-E WP 2.3.3)
- US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(1/03/22 - 12/31/22)
- Machine Learning Methods for Xe-100 Plant Monitoring and Operations (ARPA-E WP 2.3.2)
- US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(9/01/21 - 12/31/22)
- Machine Learning for ARPA-E GEMINA Project (ARPA E WP 2.3.1)
- US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(1/29/21 - 12/31/22)
- Demonstration Of Utilization Of High-fidelity Neams Tools To Inform The Improved Use Of Conventional Tools Within The Neams Workbench On The 18-15104
- US Dept. of Energy (DOE)(10/01/18 - 9/30/22)
- Development of Simulation Capabilities and Validation Basis to Support Evaluation of Advanced Nuclear Fuel Concepts
- US Dept. of Energy (DOE)(10/01/20 - 3/11/22)
- Development of High-Fidelity Transient Model for Pulsed Plasma Reactor
- National Aeronautics & Space Administration (NASA)(10/01/20 - 2/28/21)