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

Distributed visualization of gridded geophysical data: the Carbon Data Explorer, version 0.2.3

Due to the proliferation of geophysical models, particularly climate models, the increasing resolution of their spatiotemporal estimates of Earth system processes, and the desire to easily share results with collaborators, there is a genuine need for tools to manage, aggregate, visualize, and share data sets. We present a new, web-based software tool – the Carbon Data Explorer – that provides these capabilities for gridded geophysical data sets. While originally developed for visualizing carbon flux, this tool can accommodate any time-varying, spatially explicit scientific data set, particularly NASA Earth system science level III products. In addition, the tool\u27s open-source licensing and web presence facilitate distributed scientific visualization, comparison with other data sets and uncertainty estimates, and data publishing and distribution

Mapping the results of local statistics

The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.geographically weighted regression, local statistics, mapping, nonstationarity

Visualizing the motion of graphene nanodrums

Membranes of suspended two-dimensional materials show a large variability in mechanical properties, in part due to static and dynamic wrinkles. As a consequence, experiments typically show a multitude of nanomechanical resonance peaks, which makes an unambiguous identification of the vibrational modes difficult. Here, we probe the motion of graphene nanodrum resonators with spatial resolution using a phase-sensitive interferometer. By simultaneously visualizing the local phase and amplitude of the driven motion, we show that unexplained spectral features represent split degenerate modes. When taking these into account, the resonance frequencies up to the eighth vibrational mode agree with theory. The corresponding displacement profiles however, are remarkably different from theory, as small imperfections increasingly deform the nodal lines for the higher modes. The Brownian motion, which is used to calibrate the local displacement, exhibits a similar mode pattern. The experiments clarify the complicated dynamic behaviour of suspended two-dimensional materials, which is crucial for reproducible fabrication and applications