Typically, computational fluid dynamic (CFD) solutions produce large amounts of data that can be used for analysis. The enormous amount of data produces new challenges for effective exploration. The prototype system EVITA, based on ranked access of application-specific regions of interest, provides an effective tool for this purpose. Automated feature detection techniques are needed to identify the features in the dataset. Automated techniques for detecting shocks, expansion regions, vortices, separation lines, and attachment lines have already been developed. A new approach for identifying the regions of flow separation is proposed. This technique assumes that each pair of separation and attachment lines has a vortex core associated with it. It is based on the velocity field in the plane perpendicular to the vortex core. The present work describes these methods along with the results obtained.

No / We present an automated technique for comparison of magnetic field inversion-line maps from SOHO/MDI magnetograms with solar filament data from the Solar Feature Catalogue created as part of the European Grid of Solar Observations project. The Euclidean distance transform and connected component labelling are used to identify nearest inversion lines to filament skeletons. Several filament inversion-line characteristics are defined and used to automate the decision whether a particular filament/inversion-line pair is suitable for quantitative comparison of orientation and separation. The technique is tested on 551 filaments from 14 H¿ images at various dates, and the distributions of angles and distances between filament skeletons and line-of-sight (LOS) magnetic inversion lines are presented for six levels of magnetic field smoothing. The results showed the robustness of the developed technique which can be applied for a statistical analysis of magnetic field in the vicinity of filaments. The method accuracy is limited by the static filament detection which does not distinguish between filaments, fibrils, pre-condensations and filament barbs and this may increase the asymmetries in magnetic distributions and broadening in angular distributions that requires the incorporation of a feature tracking technique.

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