Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling

TL;DR

This paper explores two data-driven methods to enhance coarse-grid thermal hydraulic simulations in nuclear reactors, aiming to reduce errors and improve accuracy by leveraging high-fidelity data and machine learning techniques.

Contribution

It introduces two novel data-driven approaches—turbulence closure with eddy viscosity and neural network error mapping—to improve coarse-grid thermal hydraulic modeling accuracy.

Findings

Both methods improve simulation results using high-fidelity data.

Neural networks effectively map low-fidelity features to errors.

Mesh-induced errors remain complex and need further study.

Abstract

Traditional 1D system thermal hydraulic analysis has been widely applied in nuclear industry for licensing purposes due to its numerical efficiency. However, such codes are inherently deficient for modeling of multiscale multidimensional flows. For such scenarios coarse-grid 3D simulations are useful due to the balance between the cost and the amount of information a modeler can extract from the results. At the same time, coarse grids do not allow to accurately resolve and capture turbulent mixing in reactor enclosures, while existing turbulence models (closures for the Reynolds stresses or turbulent viscosity) have large model form uncertainties. Thus, there is an interest in the improvement of such solvers. In this work two data-driven high-to-low methodologies to reduce mesh and model-induced errors are explored using a case study based on the Texas A&M upper plenum of high-temperature gas-cooled reactor facility. The first approach relies on the usage of turbulence closure for eddy viscosity with higher resolution/fidelity in a coarse grid solver. The second methodology employs artificial neural networks to map low-fidelity input features with errors in quantities of interest (velocity fields). Both methods have shown potential to improve the coarse grid simulation results by using data with higher fidelity (Reynolds-averaged Navier-Stokes and large eddy simulations), though, influence of the mesh-induced (discretization) error is quite complex and requires further investigations.