Jason Hou

Associate Professor

Director of Advanced Reactor Design and Optimization Research (ARDOR) Lab

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.


Ph.D. 2013

Nuclear Engineering

Pennsylvania State University

M.S. 2010

Nuclear Engineering

University of Michigan

M.S. 2007

Nuclear Engineering

University of Tennessee

B.S. 2005

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).


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
Methodology for Discontinuity Factors Generation for Simplified P-3 Solver Based on Nodal Expansion Formulation
Xu, Y., Hou, J., & Ivanov, K. (2021), ENERGIES, 14(20). https://doi.org/10.3390/en14206478
Propagating neutronic uncertainties for FFTF LOFWOS Test #13
Rivas, A., Stauff, N., Sumner, T., & Hou, J. (2021), NUCLEAR ENGINEERING AND DESIGN, 375. https://doi.org/10.1016/j.nucengdes.2020.111047
Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I
Delipei, G. K., Hou, J., Avramova, M., Rouxelin, P., & Ivanov, K. (2021), NUCLEAR ENGINEERING AND DESIGN, 384. https://doi.org/10.1016/j.nucengdes.2021.111474

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