To appear in an IEEE VGTC sponsored conference proceedings Parallel Vectors Criteria for Unsteady Flow Vortices

AbstractFeature-based ow visualization is naturally dependent on feature extraction. To extract ow features, often higher-order properties of the ow data are used such as the Jacobian or curvature properties, implicitly describing the ow features in terms of their inherent ow characteristics (e.g., collinear ow and vorticity vectors). In this paper we present recent research which leads to the conclusion that feature extraction algorithms need to be extended to a time-dependent analysis framework (in terms of time derivatives) when dealing with unsteady ow data. Accordingly, we present extensions of the parallel vectors vortex criteria to the time-dependent domain and show the improvements of the feature-based ow visualization results in comparison to the steady versions of this extraction algorithm both in the context of a high-resolution dataset, i.e. a simulation specically designed to evaluate our new approach, as well as for a real-world dataset from a concrete application