A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
Abstract
We report over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire framework.
- Authors:
-
- Idaho National Laboratory (INL), Idaho Falls, ID (United States). Systems Integration
- North Carolina State University, Raleigh, NC (United States)
- Zachry Nuclear Engineering Inc., Cary, NC (United States)
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Publication Date:
- Research Org.:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Org.:
- USDOE Office of Nuclear Energy (NE); USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1974874
- Alternate Identifier(s):
- OSTI ID: 1636106
- Report Number(s):
- INL/JOU-19-52701-Revision-0
Journal ID: ISSN 0029-5493; TRN: US2314003
- Grant/Contract Number:
- AC07-05ID14517; NE0008530
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nuclear Engineering and Design
- Additional Journal Information:
- Journal Volume: 349; Journal Issue: -; Journal ID: ISSN 0029-5493
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; coarse mesh; error estimation; system-level modeling and simulation; machine learning; physical feature
Citation Formats
Bao, Han, Dinh, Nam T., Lane, Jeffrey W., and Youngblood, Robert W. A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation. United States: N. p., 2019.
Web. doi:10.1016/j.nucengdes.2019.04.023.
Bao, Han, Dinh, Nam T., Lane, Jeffrey W., & Youngblood, Robert W. A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation. United States. https://doi.org/10.1016/j.nucengdes.2019.04.023
Bao, Han, Dinh, Nam T., Lane, Jeffrey W., and Youngblood, Robert W. Mon .
"A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation". United States. https://doi.org/10.1016/j.nucengdes.2019.04.023. https://www.osti.gov/servlets/purl/1974874.
@article{osti_1974874,
title = {A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation},
author = {Bao, Han and Dinh, Nam T. and Lane, Jeffrey W. and Youngblood, Robert W.},
abstractNote = {We report over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire framework.},
doi = {10.1016/j.nucengdes.2019.04.023},
journal = {Nuclear Engineering and Design},
number = -,
volume = 349,
place = {United States},
year = {Mon Apr 22 00:00:00 EDT 2019},
month = {Mon Apr 22 00:00:00 EDT 2019}
}
Web of Science
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