Investigation of similarity metrics for simulation based scaling analysis
Scaling analysis is a technique for characterizing degree of similarity between processes to occur in a real prototypic system and those to occur in its reduced scale (experimental) analog. In the context of nuclear reactor thermal-hydraulics and safety, scaling analysis is instrumental in assessing the applicability of data obtained from a scaled test facility for calibration of models and validation of simulation codes. Quantification of scale-related distortions is the key element in scaling analysis. In this work, we examine an approach for scaling analysis using computer simulation codes. Similarity analysis using information-theoretic approach is performed by comparing performance of experiment and application to variation of system conditions. Three different information theoretic measures namely mutual information, Kullback-Leibler divergence and overlap coefficient are selected for their potential use as metric for similarity analysis. We test the metrics by comparing them against physics-based scaling ratios, which are well known in fundamental thermal-hydraulic processes. In this exploratory analysis, a case of pressure drop of a steady-state flow through a channel is employed to test the proposed approach. It was found that in the absence of model uncertainties, mutual information serve as an effective measure of the scale effects present in the experiment. However, model uncertainty can significantly impact the quality of similarity analysis. Our analysis indicates that model uncertainties can have significant impact on the information theoretic metrics used for scaling analysis. Furthermore, if the model uncertainties exceeds beyond a critical value, the scale effect present in the experiment cannot be adequately evaluated. (authors)