  Current system thermal-hydraulic codes have limited credibility in simulating
real plant conditions, especially when the geometry and boundary conditions are
extrapolated beyond the range of test facilities. This paper proposes a
data-driven approach, Feature Similarity Measurement FFSM), to establish a
technical basis to overcome these difficulties by exploring local patterns
using machine learning. The underlying local patterns in multiscale data are
represented by a set of physical features that embody the information from a
physical system of interest, empirical correlations, and the effect of mesh
size. After performing a limited number of high-fidelity numerical simulations
and a sufficient amount of fast-running coarse-mesh simulations, an error
database is built, and deep learning is applied to construct and explore the
relationship between the local physical features and simulation errors. Case
studies based on mixed convection have been designed for demonstrating the
capability of data-driven models in bridging global scale gaps.
