Scientific Visualization & Data Analytics Showcase
TimeTuesday, November 14th5:15pm - 7pm
LocationMile High Prefunction
DescriptionConditioning large-scale simulation data for comprehensive visualizations to enhance intuitive understanding of complex physical phenomena is a challenging task. This is corroborated by the fact that the massive amount of data produced by such simulations exceeds the human horizon of perception. It is therefore essential to distill the key features of such data to derive at new knowledge on an abstract level.
Furthermore, presenting scientific data to a wide public audience, especially if the scientific content is of high societal interest, i.e., as it is the case for fine dust pollution, is not only difficult from a visualization but also from an information transfer point of view. Impressive visual and contextual presentation are hence key to an effective knowledge transfer of complicated scientific data and the involved methods to arrive at such data.
In this paper such an approach is presented for highly-dense simulation data stemming from HPC simulations of inspiratory flows in the human respiratory tract. The simulations are performed using a coupled lattice-Boltzmann/Lagrange method and aim at understanding the microscopic interactions of flow and particle dynamics in highly intricate anatomically correct geometries. As such, they deliver insights on the impact of particulate matter on the human body.