A data-driven approach to scale bridging in system thermal-hydraulic simulation

The scaling issues have become the stumbling blocks to build the credibility of system modeling and simulation that supports risk-informed safety analysis on reactor systems. Current computer codes have limited capabilities to simulate real plant conditions, especially for extrapolative conditions, since the empirical correlations applied are mostly determined by curve fitting and strongly depending on geometry and boundary conditions. Although some advanced coarse-mesh codes are widely used in system-level safety analysis due to their balance on computational efficiency and simulation accuracy, the Verification & Validation (V&V) of these codes still suffers from the lack of prototypic validation data. Considering mesh size is one of the model parameters for these coarse-mesh codes with simplified boundary-layer treatment, the mesh-induced error and model error are tightly connected which makes it difficult to analyze the mesh effect or the code/model scalability separately. This paper proposes a data-driven approach to establish a technical basis to overcome these difficulties by exploring local patterns with the usage of machine learning. The underlying local patterns in multi-scale data are represented by a set of physical features, which integrate the information from the physical system of interest, empirical correlations and the effect of mesh size. After performing limited high-fidelity numerical simulations and sufficient fast-running coarse-mesh simulations to generate an error database, advanced machine learning algorithms are applied to explore the relationship between the local physical features and local simulation errors to develop a data-driven model that bridges the global scale gap. Similarity of training data and testing data is measured and visualized using several metrics. Case studies based on mixed convection show that the prediction by well-trained data-driven model has high accuracy as the similarity increases.