Discrimination of spurious self-correlation in nondimensionalized analyses of fluid dynamical data

Nondimensionalization of variables enables us to treat experiment data much more simply and efficiently by decreasing the number of variables. In some cases, trivial conclusions (which Kenney, 1982, called spurious self-correlation) result from a formal application of dimensional analyses. In contrast, in some cases fully significant conclusions can be derived. We first discuss how to construct nondimensional variables retaining the physical meanings of variables. We then propose simple and efficient methods, especially the use of “spurious triangle (SpT)”, to discriminate between significant conclusions and spurious self-correlations in the analysis of nondimensionalized variables.