Wen Jiang
Assistant Professor of Nuclear Engineering, Joint Faculty Appointment with INL
- 919-515-5877
- wjiang8@ncsu.edu
- Burlington Laboratory 2156
Dr. Wen Jiang is an Assistant Professor of Nuclear Engineering at the NCSU and holds a joint faculty appointment with INL. Prior to coming to the NCSU, Dr. Jiang was a computational scientist in the Computational Mechanics and Materials Department at the Idaho National Laboratory. He joined INL in 2015 after Ph.D from the Duke University to work on computational methods for nuclear material modeling and multi-physics simulation. Dr. Jiang is a key developer for the BISON nuclear fuel simulation code which won R&D 100 Award in 2022, and he is leading developer for additive manufacturing simulation code in MOOSE Application Library for Advanced Manufacturing Utilities (MALAMUTE). He currently leads the development of multi-scale TRISO particle fuels modeling for DOE’s Nuclear Energy Advanced Modeling and Simulation program (NEAMS). He is also involved in DOE’s Advanced Gas Reactor (AGR) Fuel Development and Qualification Program where he performs TRISO fuel performance calculations with BISON in support of analysis of the AGR irradiation tests.
Education
Aeronautical Science and Engineering
Beijing University of Aeronautics and Astronautics
Aeronautical Science and Engineering
Beijing University of Aeronautics and Astronautics
Mechanical Engineering and Materials Science
Duke University
Publications
- A comparative study of two numerical approaches for solving Kim–Kim–Suzuki phase-field models
- Bognarova, X., Jiang, W., Schwen, D., & Tonks, M. R. (2023), Computational Materials Science. https://doi.org/10.1016/j.commatsci.2023.112375
- A phase-field study of stainless-steel oxidation from high-temperature carbon dioxide exposure
- Wu, X., Abdallah, I., Jiang, W., Ullberg, R. S., Phillpot, S. R., Couet, A., … Tonks, M. R. (2023), Computational Materials Science. https://doi.org/10.1016/j.commatsci.2022.111996
- Comparing the impact of thermal stresses and bubble pressure on intergranular fracture in UO2 using 2D phase field fracture simulations
- Zhang, S., Jiang, W., Gamble, K. A., & Tonks, M. R. (2023), Journal of Nuclear Materials. https://doi.org/10.1016/j.jnucmat.2022.154158
- General Multifidelity Surrogate Models: Framework and Active-Learning Strategies for Efficient Rare Event Simulation
- Chakroborty, P., Dhulipala, S. L. N., Che, Y., Jiang, W., Spencer, B. W., Hales, J. D., & Shields, M. D. (2023), Journal of Engineering Mechanics. https://doi.org/10.1061/JENMDT.EMENG-7111
- MOOSE Stochastic Tools: A module for performing parallel, memory-efficient in situ stochastic simulations
- Slaughter, A. E., Prince, Z. M., German, P., Halvic, I., Jiang, W., Spencer, B. W., … Gaston, D. R. (2023), SoftwareX. https://doi.org/10.1016/j.softx.2023.101345
- Multi-scale fission product release model with comparison to AGR data
- Simon, P.-C., Aagesen, L., Jr., Bhave, C., Jiang, C., Jiang, W., Ke, J.-H., & Yang, L. (2023). , . https://doi.org/10.2172/2203700
- TRISO fuel performance analysis: Uncertainty quantification toward optimization
- Baghdasaryan, N., Jiang, W., Hales, J., Kozlowski, T., & Krajewska, Z. (2023), Nuclear Engineering and Design. https://doi.org/10.1016/j.nucengdes.2023.112401
- Verification of Bison fission product species conservation under TRISO reactor conditions
- Toptan, A., Jiang, W., Hales, J. D., Spencer, B. W., & Novascone, S. (2023), Journal of Nuclear Materials. https://doi.org/10.1016/j.jnucmat.2022.154105
- A phase-field model of quasi-brittle fracture for pressurized cracks: Application to UO2 high-burnup microstructure fragmentation
- Jiang, W., Hu, T., Aagesen, L. K., Biswas, S., & Gamble, K. A. (2022), Theoretical and Applied Fracture Mechanics. https://doi.org/10.1016/j.tafmec.2022.103348
- Accelerated statistical failure analysis of multifidelity TRISO fuel models
- Dhulipala, S. L. N., Jiang, W., Spencer, B. W., Hales, J. D., Shields, M. D., Slaughter, A. E., … Chakroborty, P. (2022), Journal of Nuclear Materials, 563, 153604. https://doi.org/10.1016/j.jnucmat.2022.153604