EVALUATING THE EFFECTIVENESS OF FLINN'S K-VALUE AND LODE'S RATIO USING SYNTHETIC STRAIN DATA
To test whether one of these parameters is more effective at characterizing strain for different strain geometries, we produced synthetic data for a series of ellipsoid shapes. For a given ellipsoid, the axial ratio (R) and the angular orientation (φ) were determined for three mutually perpendicular sections. Using these data as mean-values, we generated a random, normal-distribution dataset for each R and φ and the best-fit ellipsoid was determined from these datasets. Repeating this process (typically 100 times), we were able to calculate the standard deviations of the k and ν-values of the best-fit ellipsoids. ν and k have the same magnitude in general flattening strains, i.e. 0 < k < 1; 0 < ν < 1. There is an approximately linear relationship between standard deviation (σ) versus k and ν. The ν vs. σ relationship demonstrates larger σ as ellipsoids approach perfectly oblate shapes while the k vs. σ relationship demonstrates larger σ as the ellipsoid approaches plain strain. This suggests that Flinn's k-value is a more effective parameter for nearly oblate strains while Lode's ratio is a more effective parameter for general flattening strains that are nearly plain strain.