Jason Hou
Associate Professor
Director of Advanced Reactor Design and Optimization Research (ARDOR) Lab
- 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. 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
- 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
- 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
- Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design
- Rivas, A., Martin, N. P., Bays, S. E., Palmiotti, G., Xu, Z., & Hou, J. (2022), NUCLEAR ENGINEERING AND DESIGN, 392. https://doi.org/10.1016/j.nucengdes.2022.111744
- Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network
- Rivas, A., Delipei, G. K., & Hou, J. (2022), PROGRESS IN NUCLEAR ENERGY, 146. https://doi.org/10.1016/j.pnucene.2022.104143
- Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification
- Avramova, M., Abarca, A., Hou, J., & Ivanov, K. (2021), Journal of Nuclear Engineering. https://doi.org/10.3390/jne2010005