Validation and Uncertainty Quantification of Transient Reflood Models Using COBRA-TF and Machine Learning Techniques Based on the NRC/PSU RBHT Benchmark
Name
Validation and Uncertainty Quantification of Transient Reflood Models Using COBRA-TF and Machine Learning Techniques Based on the NRC PSU RBHT Benchma.pdf
Description
Published version
Size
10.34 MB
Format
Adobe PDF
Checksum (MD5)
e54d72bc2da470f37ce5abd145427280
Author(s)
Jin, Yue
Bajorek, Stephen M
Cheung, Fan-Bill
Date Issued
July 28, 2022
Journal
Nuclear Science and Engineering
Publisher
Taylor & Francis
Citation
Jin, Y., Bajorek, S. M., & Cheung, F. B. (2022). Validation and Uncertainty Quantification of Transient Reflood Models Using COBRA-TF and Machine Learning Techniques Based on the NRC/PSU RBHT Benchmark. Nuclear Science and Engineering, 197(5), 967–986.
Version
Final published version
Abstract
The accurate prediction of the fluid flow mass and the heat transfer process as well as the system response during reflood transients has long been a critical and challenging issue for reactor system safety analyses. Accurate characterization of the flow and energy transport can also significantly facilitate the various system/component design and optimization tasks. In the current study based on the U.S. Nuclear Regulatory Commission/Pennsylvania State University Rod Bundle Heat Transfer (RBHT) reflood experimental data, a comprehensive uncertainty analysis framework is developed using DAKOTA. The developed framework is used to perform an in-depth reflood model validation and verification for the subchannel analysis code COBRA-TF. In the meantime, the artificial intelligence (AI)–based machine learning (ML) model for rod cladding temperature prediction during reflood is also developed and evaluated using the current framework. Key input parametric effects for reflood thermal-hydraulic prediction include the system pressure, inlet liquid temperature/enthalpy, inlet mass flow rate, and average bundle power input. The figure of merit under consideration is the peak cladding temperature variations. It is found in the current study that, while further model improvement is needed, COBRA-TF can predict the correct parametric trends when compared with the RBHT data. On the other hand, it is challenging for the pure AI-based ML models to correctly reflect the parametric trends. Suggestions for future ML model development are provided in the end.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00295639.2022.2087834
