Mississippi State University
Date of Degree
Graduate Thesis - Open Access
This study investigates methods that can be used for tracking features in computationalluid-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 sucessive 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. The use of predictive techniques is tested on swirling features, i.e., vortices. An ellipse-like representation is assumed to be a good approximation of any such feature. The location of a feature in previous time-steps are used to predict its position in a future time-step. The ellipse-like representation of the feature is translated over to the predicted location and aligned in the predicted orientation. An overlap test is then done. Use of predictive techniques will help increase the overlap, and subsequently the confidence in the match obtained. The techniques were tested on an artificial data set for linear velocity and rotation and on a real data set of simulation of flow past a cylinder. Regions of swirling flow, detected by computing the swirl parameter, were taken as features for study. The degree of overlap obtained by a basic overlap and by the use of predictive methods were tabulated. The results show that the use of predictive techniques improved the overlap.
Thampy, Sajjit, "Feature Tracking in Two Dimensional Time Varying Datasets" (2003). Theses and Dissertations. 2260.