Visualization of Time-Varying Data from Atomistic Simulations and Computational Fluid Dynamics

Time-varying data from simulations of dynamical systems are rich in spatio-temporal information. A key challenge is how to analyze such data for extracting useful information from the data and displaying spatially evolving features in the space-time domain of interest. We develop/implement multiple approaches toward visualization-based analysis of time-varying data obtained from two common types of dynamical simulations: molecular dynamics (MD) and computational fluid dynamics (CFD). We also make application case studies. Parallel first-principles molecular dynamics simulations produce massive amounts of time-varying three-dimensional scattered data representing atomic (molecular) configurations for material system being simulated. Rendering the atomic position-time series along with the extracted additional information helps us understand the microscopic processes in complex material system at atomic length and time scales. Radial distribution functions, coordination environments, and clusters are computed and rendered for visualizing structural behavior of the simulated material systems. Atom (particle) trajectories and displacement data are extracted and rendered for visualizing dynamical behavior of the system. While improving our atomistic visualization system to make it versatile, stable and scalable, we focus mainly on atomic trajectories. Trajectory rendering can represent complete simulation information in a single display; however, trajectories get crowded and the associated clutter/occlusion problem becomes serious for even moderate data size. We present and assess various approaches for clutter reduction including constrained rendering, basic and adaptive position merging, and information encoding. Data model with HDF5 and partial I/O, and GLSL shading are adopted to enhance the rendering speed and quality of the trajectories. For applications, a detailed visualization-based analysis is carried out for simulated silicate melts such as model basalt systems. On the other hand, CFD produces temporally and spatially resolved numerical data for fluid systems consisting of a million to tens of millions of cells (mesh points). We implement time surfaces (in particular, evolving surfaces of spheres) for visualizing the vector (flow) field to study the simulated mixing of fluids in the stirred tank

Clutter reduction in rendering of particle (Atom) trajectories with adaptive position merging

Visualization of position-time series data from molecular dynamics simulations of a material has to render atomic trajectories, and relevant structural and dynamical information. Clutter/occlusion associated with overlapping trajectories becomes serious even for moderate data sizes. We present an adaptive hierarchical scheme for merging multiple positions along trajectories to significantly reduce the number of points/line segments used for rendering. Our approach finds positions lying within a space window (cut-off distance) from a reference position and merges them into a single position. The window is then moved in time order with merging performed at each successive location. All original positions are thus processed to a reduced number of new (merged) positions, which are further merged with the same or a different cut-off to obtain even fewer positions. This hierarchical merging may continue several levels deep. Moreover, merging can be performed subject to constraint of information, which is displayed (color-coded) along individual trajectories. Both the trajectory geometry and underlying atomic structure become increasingly visible after merging so the nature and extent of atomic arrangements and movements can be better assessed

Rendering particle trajectories with color-coded information for atomistic visualization

Particle (atomic) trajectories from molecular dynamics simulations allow a complete representation of spatiotemporal information of real material systems at the atomic level. However, cluttering/occlusion of trajectories with increasing data size is a major issue. To address this challenge, we explore various ways primarily based on the idea of constraining the rendering of trajectories in time and space. Approaches such as selective trajectory rendering, region based rendering and merging of multiple positions are proposed to reduce the number of rendered trajectories (or line segments of the trajectories). Additional information about the simulated atomic systems can be extracted in real time and color coded along the trajectories. We also propose a 2D representation of coded information as a function of time

Visualizing microscopic structure of simulated model basalt melt

We perform a detailed visualization-based analysis of atomic-position series data for model basalt melt obtained from first-principles (quantum mechanical) molecular dynamics simulations. To gain insight into the short- and mid-range order of the melt structure, we extract and visualize the details of radial distribution function (RDF) and coordination environment. The first peaks of all partial RDFs lie in the distance range of 1.6-4. Å and the corresponding mean coordination numbers vary from less than 1 to more than 9. The coordination environments involving cations and anions differ substantially from each other, each consisting of a rich set of coordination states. These states vary both spatially and temporally: The per-atom coordination information extracted on the fly is rendered instantaneously as the spheres and polyhedra as well as along the corresponding trajectories using a color-coding scheme. The information is also visualized as clusters formed by atoms that are coordinated at different time intervals during the entire simulation. The Si-O coordination is comprised of almost all tetrahedra (4-fold) whereas the Al-O coordination includes both tetrahedra (4-fold) and pentahedra (5-fold). The animated visualization suggests that the melt structure can be viewed as a dynamic (partial) network of Al/Si-O coordination polyhedra connected via bridging oxygen in an inhomogeneous distribution of mobile magnesium and calcium atoms. © 2013 Elsevier Ltd

First-principles study of diffusion and viscosity of anorthite (CaAl \u3csub\u3e2\u3c/sub\u3eSi\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e8\u3c/sub\u3e) liquid at high pressure

We have carried out equilibrium molecular dynamics simulations of CaAl Si O (anorthite) liquid as a function of pressure (up to 160 GPa) and temperature (2500 to 6000 K) within density functional theory. Along the 3000 K isotherm, the Ca self-diffusivity varies most (decreasing by two orders of magnitude between 0 and 50 GPa), whereas the self-diffusion coefficients of Al, Si, and O vary anomalously-they initially increase with pressure, reach the broad maxima (around 5 GPa), and then decrease upon further compression. The calculated melt viscosity also shows a weak anomalous behavior with a local minimum around a similar pressure. Temperature suppresses the dynamical anomalies as well as the overall pressure variations. Therefore, the curvatures of the diffusivity (viscosity) isotherms change from a concave (convex) shape at 3000 K to a convex (concave) shape at 6000 K. We find that anorthite liquid is much more mobile than silica liquid because of its high content of non-bridging oxygen atoms (NBO) and oxygen triclusters (O3). The predicted pressure variations can be associated with structural changes consisting of the pressure-induced maximum in the abundance of pentahedral states (fivefold Al/Si-O coordination) and rapid increase in the O3 abundance. Finally, our predicted first-principles results compare favorably with the available experimental data. 2 2

Time-curvature and time-torsion of virtual bubbles as fluid mixing indicators

Massive data sets describing vector fields from computational fluid dynamic simulations on high-performance computers can no longer be visualized directly by displaying the data values at each point in time and space, but require data reduction to analyze the essential properties. We describe early results of work in progress to visualize a dataset of 500GB of raw data, consisting out of multiple time steps from a CFD simulation describing the mixing of fluids within a Stirred tank. The objective is to assess the quality of the mixing of the fluids after some time. We compute pathlines with out-of-core memory management to handle the massive dataset on a single desktop machine and use these to trace the evolution of user-specified initial geometric structures. On top of these structures we compute quantities indicating the mixture, such as curvature and torsion of the pathlines as information complementary to the intrinsic curvature of the evolving time surfaces. © 2010 IADIS