A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation

Bao, Han; Dinh, Nam T.; Lane, Jeffrey W.; Youngblood, Robert W.

doi:10.1016/j.nucengdes.2019.04.023

A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation

Journal Article · Mon Apr 22 00:00:00 EDT 2019 · Nuclear Engineering and Design

DOI:https://doi.org/10.1016/j.nucengdes.2019.04.023· OSTI ID:1974874

^[1]; Dinh, Nam T. ^[2]; Lane, Jeffrey W. ^[3]; ^[4]

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)

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.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517; NE0008530

OSTI ID:: 1974874

Alternate ID(s):: OSTI ID: 1636106
OSTI ID: 22893833

Report Number(s):: INL/JOU-19-52701-Revision-0

Journal Information:: Nuclear Engineering and Design, Journal Name: Nuclear Engineering and Design Journal Issue: - Vol. 349; ISSN 0029-5493

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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