A user-friendly interactive framework for unsteady fluid flow segmentation and visualization

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Motta, Danilo
Casaca, Wallace [UNESP]
Pagliosa, Paulo
Paiva, Afonso
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Abstract: While vector fields are essential to simulate a large amount of natural phenomena, the difficulty to identify patterns and predict behaviors makes the visual segmentation in simulations an attractive and powerful tool. In this paper, we present a novel user-steered segmentation framework to cope with steady as well as unsteady vector fields on fluid flow simulations. Given a discrete vector field, our approach extracts multi-valued features from the field by exploiting its streamline structures so that these features are mapped to a visual space through a multidimensional projection technique. From an easy-to-handle interface, the user can interact with the projected data so as to partition and explore the most relevant vector features in a guidance frame of the simulation. Besides navigating and visually mining structures of interest, the interactivity with the projected data also allows the user to progressively enhance the segmentation result according to his insights. Finally, to successfully deal with unsteady simulations, the segments previously annotated by the user are used as a training set for a Support Vector Machine approach that classifies the remaining frames in the flow. We attest the effectiveness and versatility of our methodology throughout a set of classical physical-inspired applications on fluid flow simulations as depicted in the experiment results section. Graphical Abstract: [Figure not available: see fulltext.].
Flow segmentation, Interactive tools, Machine learning, Time-varying visualization, Vector field
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Journal of Visualization, v. 21, n. 4, p. 625-636, 2018.