| This study investigates methods that can be used for tracking features in computational-fluid-dynamics datasets. The two approaches of overlap based feature tracking and attribute-based feature tracking are studied. Overlap based techniques use the actual degree of overlap between successive time steps to conclude a match. Attribute-based techniques use characteristics native to the feature being studied, like size, orientation, speed, etc., to conclude a match between candidate features. Due to limitations on the number of time steps that can be held in a computer's memory, it may be possible to load only a time-subsampled data set. This might result in a decrease in the overlap obtained, and hence a subsequent decrease in the confidence of the match.; This study looks into using specific attributes of features, like rotational velocity, linear velocity to predict the presence of that feature in a future time step. (Abstract shortened by UMI.)... |
