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A framework is proposed for extracting features in 2D transient flows, based on the acceleration field to ensure Galilean invariance. The minima of the acceleration magnitude, i.e. a superset of the acceleration zeros, are extracted and discriminated into vortices and saddle points --- based on the spectral properties of the velocity Jacobian. The extraction of topological features is performed with purely combinatorial algorithms from discrete computational topology. The feature points are prioritized with persistence, as a physically meaningful importance measure. These features are tracked in time with a robust algorithm for tracking features. Thus a space-time hierarchy of the minima is built and vortex merging events are detected. The acceleration feature extraction strategy is applied to three two-dimensional shear flows:
(1) an incompressible periodic cylinder wake,
(2) an incompressible planar mixing layer and
(3) a weakly compressible planar jet.
The vortex-like acceleration feature points are shown to be well aligned with acceleration zeros, maxima of the vorticity magnitude, minima of pressure field and minima of λ2.
To improve existing weather prediction and reanalysis capabilities, high-resolution and multi-modal climate data becomes an increasingly important topic. The advent of increasingly dense numerical simulation of atmospheric phenomena, provides new means to better understand dynamic processes and to visualize structural flow patterns that remain hidden otherwise. In the presented illustrations we demonstrate an advanced technique to visualize multiple scales of dense flow fields and Lagrangian patterns therein, simulated by state-of-the-art simulation models for each scale. They provide a deeper insight into the structural differences and patterns that occur on each scale and highlight the complexity of flow phenomena in our atmosphere.
When physical unfolding/unrolling of papyri is not possible or too dangerous for preserving the precious object, tomographic approaches may be the ap- propriate alternative. Requirements are the resolution and the contrast to distinguish writing and substrate. The steps to be performed are the following: (1) Select the object of interest (archaeological arguments, cultural back- ground of the object, etc.). (2) Find the proper physical procedure, especially with respect to contrast, take the tomographic data, e.g. by absorption x-ray tomography. (3) Apply mathematical unfolding transformations to the tomographic data, in order to obtain a 2d-planar reconstruction of text.
Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three-dimensional, complex, large and time-varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The survey concludes with an outlook on promising and important research topics to foster further success in the development of tools that help to reveal molecular secrets.
In this report we review and structure the branch of molecular visualization that is concerned with the visual analysis of cavities in macromolecular protein structures. First the necessary background, the domain terminology, and the goals of analytical reasoning are introduced. Based on a comprehensive collection of relevant research works, we present a novel classification for cavity detection approaches and structure them into four distinct classes: grid-based, Voronoi-based, surface-based, and probe-based methods. The subclasses are then formed by their combinations. We match these approaches with corresponding visualization technologies starting with direct 3D visualization, followed with non-spatial visualization techniques that for example abstract the interactions between structures into a relational graph, straighten the cavity of interest to see its profile in one view, or aggregate the time sequence into a single contour plot. We also discuss the current state of methods for the visual analysis of cavities in dynamic data such as molecular dynamics simulations. Finally, we give an overview of the most common tools that are actively developed and used in the structural biology and biochemistry research. Our report is concluded by an outlook on future challenges in the field.
ORBKIT is a toolbox for postprocessing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wave function, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemistry programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other derived quantity. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT offers routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at https://github.com/orbkit/orbkit/.
This article provides an overview of ORBKIT with particular focus
on its capabilities and applicability, and includes several example
calculations.
Purpose: To account for the impact of turbulence in blood damage modeling, a novel approach based on the generation of instantaneous flow fields from RANS simulations is proposed.
Methods: Turbulent flow in a bileaflet mechanical heart valve was simulated using RANS-based (SST k-ω) flow solver using FLUENT 14.5. The calculated Reynolds shear stress (RSS) field is transformed into a set of divergence-free random vector fields representing turbulent velocity fluctuations using procedural noise functions. To consider the random path of the blood cells, instantaneous flow fields were computed for each time step by summation of RSS-based divergence-free random and mean velocity fields. Using those instantaneous flow fields, instantaneous pathlines and corresponding point-wise instantaneous shear stresses were calculated. For a comparison, averaged pathlines based on mean velocity field and respective viscous shear stresses together with
RSS values were calculated. Finally, the blood damage index (hemolysis) was integrated along the averaged and instantaneous pathlines using a power law approach and then compared.
Results: Using RSS in blood damage modeling without a correction factor overestimates damaging stress and thus the blood damage (hemolysis). Blood damage histograms based on both presented approaches differ.
Conclusions: A novel approach to calculate blood damage without using RSS as a damaging parameter is established. The results of our numerical experiment support the hypothesis that the use of RSS as a damaging parameter should be avoided.