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Current system thermal-hydraulic codes have limited credibility in simulatingreal plant conditions, especially when the geometry and boundary conditions areextrapolated beyond the range of test facilities. This paper proposes adata-driven approach, Feature Similarity Measurement FFSM), to establish atechnical basis to overcome these difficulties by exploring local patternsusing machine learning. The underlying local patterns in multiscale data arerepresented by a set of physical features that embody the information from aphysical system of interest, empirical correlations, and the effect of meshsize. After performing a limited number of high-fidelity numerical simulationsand a sufficient amount of fast-running coarse-mesh simulations, an errordatabase is built, and deep learning is applied to construct and explore therelationship between the local physical features and simulation errors. Casestudies based on mixed convection have been designed for demonstrating thecapability of data-driven models in bridging global scale gaps.

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation