Alexander Straub

Abstract

In multiphase flows, the evolution of fluid-fluid interfaces is of interest in many applications. In addition to fluid dynamic forces governing the flow in the entire volume, surface tension determines droplet interfaces. Here, the analysis of interface kinematics can help in the investigation of interface deformation and the identification of potential breakups. To this end, we developed a visualization technique using metric and shape tensors to analyze interface stretching and bending. For interface stretching, we employ the eigenpairs of the metric tensor defined for the deformation rate of the fluid surface. For interface bending, we present a technique that locally captures the interface curvature change in terms of a shape tensor, extracting its principal directions and curvatures. We then visualize interface deformation by combining both representations into a novel glyph design. We apply our method to study multiphase flow simulations with particular emphasis on interface effects. These include the interplay between fluid dynamics and surface tension forces leading to breakup processes following droplet collisions, as well as droplet-droplet interactions of different fluids where Marangoni convection along the surface is explicitly taken into account.

Abstract

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading fluid, we first condense an input image series to a single time map. From this map, we generate a spatio-temporal flow graph covering the whole process. This graph is further simplified to only reflect topological changes in the movement of the invading fluid. Our interactive tools allow the visual analysis of these processes by visualizing the graph structure and the context of the experimental setup, as well as by providing charts for multiple metrics. We apply our approach to analyze and compare ensemble datasets jointly with domain experts, where we vary either fluid properties or the solid structure of the porous medium. We finally report the generated insights from the domain experts and discuss our contribution's advantages, generality, and limitations.

Abstract

Line integration of stream-, streak-, and pathlines is widely used and popular for visualizing single-phase flow. In multiphase flow, i.e., where the fluid consists, e.g., of a liquid and a gaseous phase, these techniques could also provide valuable insights into the internal flow of droplets and ligaments and thus into their dynamics. However, since such structures tend to act as entities, high translational and rotational velocities often obfuscate their detail. As a remedy, we present a method for deriving a droplet-local velocity field, using a decomposition of the original velocity field removing translational and rotational velocity parts, and adapt path- and streaklines. Generally, the resulting integral lines are thus shorter and less tangled, which simplifies their analysis. We demonstrate and discuss the utility of our approach on droplets in two-phase flow data and visualize the removed velocity parts employing glyphs for context.

Abstract

We present a data-driven visual analysis approach for the in-depth exploration of large numbers of droplets. Understanding droplet dynamics in sprays is of interest across many scientific fields for both simulation scientists and engineers. In this paper, we analyze large-scale direct numerical simulation datasets of the two-phase flow of non-Newtonian jets. Our interactive visual analysis approach comprises various dedicated exploration modalities that are supplemented by directly linking to ParaView. This hybrid setup supports a detailed investigation of droplets, both in the spatial domain and in terms of physical quantities . Considering a large variety of extracted physical quantities for each droplet enables investigating different aspects of interest in our data. To get an overview of different types of characteristic behaviors, we cluster massive numbers of droplets to analyze different types of occurring behaviors via domain-specific pre-aggregation, as well as different methods and parameters. Extraordinary temporal patterns are of high interest, especially to investigate edge cases and detect potential simulation issues. For this, we use a neural network-based approach to predict the development of these physical quantities and identify irregularly advected droplets.

Abstract

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a pre-processing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

Abstract

We present implicit visualization of 2D vector field topology, and show its utility for validating and guiding approaches for periodic orbit extraction. Instead of following the traditional approach by explicit extraction of the topological skeleton, we investigate its implicit visualization by approaches that label the regions that are separated by the skeleton. While such approaches perform well for gradient fields, they fail, in particular, to visualize periodic orbits. This motivates us to complement the label-based approach with a closely related distance-based metric. We show that our approach is able to reveal periodic orbits, also in configurations in which the state-of-the-art techniques for periodic orbit extraction fail, and demonstrate their utility for interactive extraction of all periodic orbits of a 2D vector field.

Abstract

The analysis of large multiphase flow simulation data poses an interesting and complex research question, which can be addressed with interactive visualization techniques, as well as semi-automated analysis processes. In this project, the focus lies on the investigation of forces governing droplet evolution. Therefore, our proposed methods visualize and allow the analysis of droplet deformation and breakup, droplet behavior and evolution, and droplet-internal flow. By deriving quantities for interface stretching and bending, we visualize and analyze the influence of surface tension force on breakup dynamics, and forces induced by Marangoni convection. Using machine learning to train a simple model for the prediction of physical droplet properties, we provide a visual analysis framework that can be used to analyze large simulation data. Computing droplet-local velocity fields where every droplet is observed separately in its own frame of reference, we create local, interpretable visualizations of flow within droplets, allowing for the investigation of the influence of flow dynamics on droplet evolution.

Abstract

When investigating multiphase flow, important aspects to consider are free surfaces, their influence on coherence and breakup of droplets, and the influence of droplet-local flow on droplet shape and topology. To this end, we want to provide visualization techniques and the means for visual analysis of complex multiphase flow data. This data is obtained from direct numerical simulations (DNS), in this case from the solver Free Surface 3D (FS3D), which solves the Navier-Stokes equations for incompressible multiphase flow. The data consists of a velocity field and a volume of fluid (VOF) field, whereas the latter can be used to reconstruct the fluid interface. In the following, we will present three projects, which all aim at using visualization for gaining understanding of phenomena related to flow in droplets and forces acting on the free surfaces.

Abstract

The IEEE SciVis 2019 Contest targets the visual analysis of structure formation in the cosmic evolution of the universe from when the universe was five million years old up to now. In our submission, we analyze high-dimensional data to get an overview, then investigate the impact of Active Galactic Nuclei (AGNs) using various visualization techniques, for instance, an adapted filament filtering method for detailed analysis and particle flow in the vicinity of filaments. Based on feedback from domain scientists on these initial visualizations, we also analyzed X-ray emissions and star formation areas. The conversion of star-forming gas to stars and the resulting increasing molecular weight of the particles could be observed.

Abstract

This project aims at investigating droplet-related phenomena, especially the influence of forces acting on the surface. To this end, we visualize interface deformation using two quantities: interface stretching and interface bending. This allows the visual analysis of droplet behavior and interface-related forces, such as the surface tension force and forces induced by Marangoni convection. For the latter, we show an application case for debugging and aiding the implementation of the Marangoni term in the solver Free Surface 3D (FS3D). Furthermore, we demonstrate the usefulness of our approach for prediction of droplet breakup.

Abstract

Vector field topology is traditionally visualized by explicit extraction of graphical objects that represent the topological skeleton, including critical points, periodic orbits, and the invariant manifolds converging to those in forward or reverse time. In this work, we present implicit visualization of vector field topology by means of derived scalar fields. We obtain these fields by seeding a forward and a reverse streamline at each of their samples, and determining which critical point, periodic orbit, or solid boundary they converge to. This provides a segmentation of the domain with respect to regions of qualitatively similar streamline behavior, and thus its topological structure.We present an adaptive sampling strategy of the fields, together with an approach to determine their convergence with respect to streamline integration. Using 2D and 3D fields, we exemplify the utility and interpretation of our approach.