VMV18

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https://diglib.eg.org/handle/10.2312/2632666

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Browsing VMV18 by Subject "I.3.3 [Computer Graphics]"

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    Parameter Space Comparison of Inertial Particle Models
    (The Eurographics Association, 2018) Holbein, Jérôme; Günther, Tobias; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    In many meteorological and engineering problems, the motion of finite-sized objects of non-zero mass plays a crucial role, such as in air pollution, desertification, stirring of dust during helicopter navigation, or droplets in clouds or hurricanes. The motion of these so-called inertial particles can be modeled by equations of motion that place certain application-specific assumptions. These models are determined by parameters, such as the particle size, the Stokes number or the density ratio between particle and fluid. To describe the motion of finite-sized particles in an accurate and feasible way, one has to choose the most suitable particle model and its model parameters very carefully. In this paper, we present multiple interactive visualizations that allow us to compare different inertial particle models for a range of model parameters. To assess the similarities and disparities in the inertial pathline geometries in space-time, we first trace multiple inertial particles with varying model parameters from the same seed point and visualize their motion in space-time for different inertial particle models. Further, we find for a given inertial trajectory in one model, the parameters of the other model that fit this trajectory best. Finally, we offer a quantitative view of the pair-wise inertial trajectory distance for each possible parameter combination of two inertial particle models for a given seed point. By visually exploring this parameter space, we can find similarities and dissimilarities between parameter configurations, which guides the selection of the parameter model. Since all these visualizations only consider one single seed point, we extend the methods by displaying the results for multiple seed points in the same domain or by using stacked visualizations. We apply our method to multiple analytic and numerical vector fields for two inertial particle models.