ParaView Images

This visualization was a finalist in the SC21 Visualization Showcase. Credits: Francesca Samel, Greg Abram, Stephanie Zeller at TACC. Mark Petersen, LeAnn Conlon, Prajvala Kurtakoti, Linnea Palstom, John Patchett, Andrew Roberts at Los Alamos National Lab. Learn more about this image.

This visualization was a finalist in the SC21 Visualization Showcase. Credits: Greg Foss and Dave Semeraro, TACC. Learn More about this image.

Credit: Baskar Ganapathysubramanian Research Group/Iowa State University; Greg Foss/Texas Advanced Computing Center (TACC). Learn more about this image.

Science: Fabrizio Bisetti, Tejas U. Kulkarni, The University of Texas at Austin Visualization: Greg Foss, Texas Advanced Computing Center. Learn more about this image.

Visualization: Greg Foss, Texas Advanced Computing Center Science: Nguyen, An T., Heimbach, P. and Vocaña, V., Institute for Computational Engineering and Sciences, The University of Texas at Austin. Learn more about this image.

Science: Abdul Malmi Kakkada (Dave Thirumalai’s group – Department of Chemistry at UT Austin) Visualization: Anne Bowen, Texas Advanced Computing Center. Learn more about this image.

Fluid flow in a mixing application showing two different mixing speeds. Color indicates velocity magnitude and surface LIC shows flow direction. Data generated by lattice-Boltzmann methods in M-Star CFD and visualized in M-Star Post, a ParaView-based application. Images courtesy M-Star CFD.

A cancer immunology hypothesis study (doi.org/10.1101/196709) ran and analyzed 231 simulations of T cells seeking and attacking a tumor. The simulations were created with PhysiCell (PhysiCell.MathCancer.org,doi.org/10.1101/088773), an open source physics-based cell simulator. They include approximately 125,000 cells in a 3.375 mm3 tissue, which contains the tumor. OSPRay capabilities in ParaView were used to render the final outcomes of the simulations (colored by both cell type and phenotype) to help researchers understand what T-cell design parameters may be considered most effective. An octant of the tumor was cut from the visualizations to better capture the 3D structure of the tumor. Acknowledgement: Ozik, Jonathan, Nicholson Collier, Justin Wozniak, Charles Macal, Chase Cockrell, Samuel Friedman, Ahmadreza Ghaffarizadeh, Randy Heiland, Gary An, and Paul Macklin. “High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow.” BioRxiv, September 30, 2017. doi.org/10.1101/196709.

This is from a DNS simulation of a planar jet, using a 512x512x128 grid. They are Vorticity norm isosurfaces colored by positive Q criterium.
Image courtesy of Ricardo Reis, LASEF/IST, Lisboa, Portugal www.lasef.ist.utl.pt

Image courtesy of Sandia National Laboratories.

ParaView was recently used in Russell M Taylor II’s Visualization in the Sciences class at the University of North Carolina at Chapel Hill.

Author: Joo Hwi Lee and Namdi Brandon

Copyright: Data courtesy of Laura Miller, UNC Applied Mathematics

ParaView was recently used in Russell M Taylor II’s Visualization in the Sciences class at the University of North Carolina at Chapel Hill.

Author: Michael Garrett Larson and Alexander D. Hill

Copyright: Data courtesy of Jonathan Lees and Keehoon Kim, UNC Geological Sciences

Blood flow simulation data courtesy of George Karniadakis and Leopold Grinberg of Brown University. It shows visualization of multiple data types, including unstructured tetrahedral mesh with fluid field and blood plasma data, particle data of particles advected by the flow, and triangle meshes showing healthy and diseased red blood cells. Data was generated with an integrated Nektar/LAMMPS simulation code. Data and ParaView tutorial from Argonne National Laboratory available at https://www.alcf.anl.gov/user-guides/vis-paraview-red-blood-cell-tutorial.

Courtesy of Jerry Clarke (US Army Research Laboratory)

Contributors from ICES, The University of Texas at Austin:
Carsten Burstedde, Omar Ghattas, James R. Martin, Georg Stadler, Lucas C. Wilcox
Visualization at the Texas Advanced Computing Center,
the University of Texas at Austin by Greg Abram

Simulation by Arno Candel, SLAC, in collaboration with CERN. Visualization by Greg Schussman, SLAC

Supercomptuing 2010, Video courtesy of Greg Schussman. Rendered in ParaView
Simulation: Cho-Kuen NG, Vineet Rawat

Supercomputing 2009, Video courtesy of Greg Schussman.
Simulation: Arno Candel

Image courtesy of Sandia National Laboratories

Image courtesy of Sandia National Laboratories.

Asteroid Golevka measures about 500 x 600 x 700 meters. In this CTH shock physics simulation, a 10 Megaton explosion was initiated at the center of mass. The simulation ran for about 15 hours on 7200 nodes of Red Storm and provided approximately 0.65 second of simulated time. The resolution was 1 meter, with a 1 cubic kilometer mesh that contained 1.1 billion cells. The remarkable resolution of this simulation provides
realism in crack formation and propagation not seen in lower-resolution models.

by Rolf Walder and Doris Folini. Visualization by Jean M. Favre

Visualization of Navy Coast Ocean Model data showing both internal and surface flow.

Visualization of MIT General Circulation Model data showing surface temperature, VHEr and bathymetry.

Temporal plane jet DNS simulation. Orange denotes the interface between the turbulent and non-turbulent flow. Yellow are the radius of identified Intense Vorticity Structures and the white mesh contours use a pressure value for identifying some Large Scale Vortex structures.
Ricardo Reis, LASEF at IST, Lisbon http://www.lasef.ist.utl.pt

Greg Schussman, SLAC National Accelerator Laboratory
Ken Moreland, Sandia National Laboratories
Lixin Ge, SLAC National Accelerator Laboratory
Zenghai Li, SLAC National Accelerator Laboratory
Cho-Kuen Ng, SLAC National Accelerator Laboratory
Liling Xiao, SLAC National Accelerator Laboratory
Kwok Ko, SLAC National Accelerator Laboratory

ParaView was recently used in Russell Taylor’s Comp 715: Visualization in the Sciences
class at the University of North Carolina at Chapel Hill.
Author: Joo Hwi Lee and Avery Ted Cashion
Copyright: Data courtesy of the 2011 IEEE Visualization Contest

Image courtesy of Sandia National Laboratories.

Visualization of Regional Arctic Simulation Model data showing flow magnitude and bathymetry.

The paint-like swirls of this visualization from Los Alamos National Laboratory depict global water-surface temperatures, with the surface texture driven by vorticity.
Image Courtesy of Los Alamos National Laboratory

Displacement in the x direction due to a unit point force
placed on top of a half space. The points on the surface move towards
the loading point, in the inside of the domain the points are pushed
away from it.

Data simulation by Rolf Walder and Doris Folini with the A-MAZE code. Data courtesy of the Swiss National Supercomputing Centre

Data courtesy of ChomboVis from Lawrence Berkeley Laboratory

ParaView was recently used in Russell Taylor’s Comp 715: Visualization in the Sciences class at the University of North Carolina at Chapel Hill.
Author: Joo Hwi Lee and Zhigang (Alfred) Zhong
Copyright: Data courtesy of Steffen Bass and Hannah Peterson, Duke Physics

ParaView was recently used in Russell Taylor’s Comp 715: Visualization in the Sciences class at the University of North Carolina at Chapel Hill.
Author: Jared Vicory and Zhigang (Alfred) Zhong
Copyright: Data courtesy of Jonathan Lees, UNC Geological Sciences

Image courtesy of Renato N. Elias, Associate Researcher at the CFD Group from NACAD/COPPE/UFRJ, Rio de Janeiro, Brazil

fan / nozzle and ground plane streamlines.
UFO-CFD: https://sites.google.com/site/ufocfdsolver/

characteristics of compression-induced skin micro-wrinkles as a function of relative humidity (left to right/back to front: decreasing relative humidity). Statum corneum (top skin layer) stiffness is a surrogate measure of relative humidity (stiffness increases with decreasing relative humidity). Courtesy of Georges Limbert, University of Southampton, UK and University of Cape Town, South Africa.

https://royalsocietypublishing.org/doi/full/10.1098/rspa.2017.0257

emergence of skin micro-wrinkles as a result of in-plane compressive stress in a bi-layer skin model. The white isosurface corresponds to 20% compressive strain while streamlines represent maximum principal strains in the substrate. Courtesy of Georges Limbert, University of Southampton, UK and University of Cape Town, South Africa.

https://pubs.rsc.org/-/content/articlelanding/2018/sm/c7sm01969f/unauth#!divAbstract

Raytracing helps us to better understand the 3D nature of the data, and by using depth of field, also to focus on the important parts pf the it, e.g. the Agulhas current at the southern tip of Africa. The current is visualized using streamlines and three iso surfaces (0.5, 1.0, 1.5 m/s) of the velocity magnitude, as well as through a thresholded surface that only shows the area in which ssh (sea surface height) > 0, i.e. the warm water transported by the current. Image courtesy of Niklas Röber, DRKZ and Helmuth Haak, MPI-M. This image was rendered with ParaView 5.7’s OptiX path tracing option.

The iconic OpenFoam Motorbike, rendered with the OptiX path tracer in ParaView. Realistic materials, shadows and depth of field help to guide the viewer to the interesting part of the visualization — the turbulence around the front wheel — while providing context to convey spatial relationships. Image courtesy NVIDIA.

This image demonstrates display of a vtkMolecule, in which the rendering uses OSPRay path tracing with depth of field enabled.

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