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.

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

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
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
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
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

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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)