Jason Hou

Assistant Professor, Director of Reactor Dynamics and Fuel Modeling Group,

  • 919-513-6705
  • Burlington Laboratory 1139
  • View CV

Dr. Jason Hou is an advocate of nuclear energy and the mission of his research is to promote nuclear energy by investigating advanced reactor designs and developing improved reactor modeling and simulation methods. In particular, he develops accurate yet efficient numerical models to improve the nuclear reactor design in various aspects, including the economics, safety, proliferation resistance, and sustainability.

His area of research interest includes multi-physics reactor simulation, advanced reactors, fuel cycle analysis, uncertainty quantification, artificial intelligence, and 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 monitoring and control, and homogenization-free time-dependent neutron transport benchmark. He also serves as the coordinator of the Nuclear Simulation Laboratory.

Dr. Hou is current teaching NE 403 Nuclear Reactor Laboratory, NE 412/512 Nuclear Fuel Cycle.

Education

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

His area of research interest includes multi-physics reactor simulation, advanced reactor design, fuel cycle analysis, plant simulator, uncertainty analysis, machine learning.

Publications

Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors
Zeng, K., Stauff, N. E., Hou, J., & Kim, T. K. (2020), Progress in Nuclear Energy, 120, 103184. https://doi.org/10.1016/j.pnucene.2019.103184
Nuclear-data uncertainty propagation in transient simulation for the C5G7-TD benchmark problem
Wan, C., Sui, Z., Wang, B., Cao, L., Liu, Z., & Hou, J. (2020), Annals of Nuclear Energy, 140, 107122. https://doi.org/10.1016/j.anucene.2019.107122
Further development of methodology to model TRISO fuel and BISO absorber particles and related uncertainty quantification using SCALE 6
Sihlangu, S. F., Naicker, V. V., Hou, J., & Reitsma, F. (2019), Journal of Nuclear Science and Technology, 5, 1–20. https://doi.org/10.1080/00223131.2019.1617204
Uncertainty Quantification and Propagation of Multiphysics Simulation of the Pressurized Water Reactor Core
Zeng, K., Hou, J., Ivanov, K., & Jessee, M. A. (2019), Nuclear Technology, 3, 1–20. https://doi.org/10.1080/00295450.2019.1580533
Development, verification and application of a new model for active nucleation site density in boiling systems
Li, Q., Jiao, Y. J., Avramova, M., Chen, P., Yu, J. C., Chen, J., & Hou, J. (2018), Nuclear Engineering and Design, 328, 1–9. https://doi.org/10.1016/j.nucengdes.2017.12.027
Development, verification and application of a new model for active nucleation site density in boiling systems
Li, Q., Jiao, Y., Avramova, M., Chen, P., Yu, J., Chen, J., & Hou, J. (2018), Nuclear Engineering and Design, 328, 1–9. https://doi.org/https://doi.org/10.1016/j.nucengdes.2017.12.027
OECD/NEA benchmark for time-dependent neutron transport calculations without spatial homogenization
Hou, J., Ivanov, K. N., Boyarinov, V. F., & Fomichenko, P. A. (2017), Nuclear Engineering and Design, 317, 177–189. https://doi.org/10.1016/j.nucengdes.2017.02.008
3D in-core fuel management optimization for breed-and-burn reactors
Hou, J., Qvist, S., Kellogg, R., & Greenspan, E. (2016), Progress in Nuclear Energy, 88, 58–74. https://doi.org/10.1016/j.pnucene.2015.12.002
Design and performance of 2D and 3D-shuffled breed-and-burn cores
Qvist, S., Hou, J., & Greenspan, E. (2015), Annals of Nuclear Energy, 85, 93–114. https://doi.org/10.1016/j.anucene.2015.04.007
Development of an iterative diffusion-transport method based on MICROX-2 cross section libraries
Hou, J., Choi, H., & Ivanov, K. N. (2015), Annals of Nuclear Energy, 77, 335–342. https://doi.org/10.1016/j.anucene.2014.11.014

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Grants

Modeling SFR-UAM Benchmark Services On the NEAMS Workbench Project
US Dept. of Energy (DOE)(1/29/19 - 9/30/19)
Demonstration of utilization of high-fidelity NEAMS tools to inform the improved use of conventional tools within the NEAMS Workbench on the NEA/OECD C5G7-TD benchmark
US Dept. of Energy (DOE)(10/01/18 - 9/30/21)
Development of Information Trustworthiness and Integrity Algorithms for Cybersecurity Defenses of Nuclear Power Reactors Technical Workscope Identification: NE-1
US Dept. of Energy (DOE)(10/01/17 - 9/30/20)