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dc.contributor.authorBrownlee, C.
dc.contributor.authorPegoraro, V.
dc.contributor.authorShankar, S.
dc.contributor.authorMcCormick, Patrick S.
dc.contributor.authorHansen, C. D.
dc.date.accessioned2016-02-25T13:54:36Z
dc.date.available2016-02-25T13:54:36Z
dc.date.issued2011-11
dc.identifier.citationBrownlee C, Pegoraro V, Shankar S, McCormick PS, Hansen CD (2011) Physically-Based Interactive Flow Visualization Based on Schlieren and Interferometry Experimental Techniques. IEEE Transactions on Visualization and Computer Graphics 17: 1574–1586. Available: http://dx.doi.org/10.1109/TVCG.2010.255.
dc.identifier.issn1077-2626
dc.identifier.pmid21149891
dc.identifier.doi10.1109/TVCG.2010.255
dc.identifier.urihttp://hdl.handle.net/10754/599191
dc.description.abstractUnderstanding fluid flow is a difficult problem and of increasing importance as computational fluid dynamics (CFD) produces an abundance of simulation data. Experimental flow analysis has employed techniques such as shadowgraph, interferometry, and schlieren imaging for centuries, which allow empirical observation of inhomogeneous flows. Shadowgraphs provide an intuitive way of looking at small changes in flow dynamics through caustic effects while schlieren cutoffs introduce an intensity gradation for observing large scale directional changes in the flow. Interferometry tracks changes in phase-shift resulting in bands appearing. The combination of these shading effects provides an informative global analysis of overall fluid flow. Computational solutions for these methods have proven too complex until recently due to the fundamental physical interaction of light refracting through the flow field. In this paper, we introduce a novel method to simulate the refraction of light to generate synthetic shadowgraph, schlieren and interferometry images of time-varying scalar fields derived from computational fluid dynamics data. Our method computes physically accurate schlieren and shadowgraph images at interactive rates by utilizing a combination of GPGPU programming, acceleration methods, and data-dependent probabilistic schlieren cutoffs. Applications of our method to multifield data and custom application-dependent color filter creation are explored. Results comparing this method to previous schlieren approximations are finally presented. © 2011 IEEE.
dc.description.sponsorshipThe authors would like to thank Kelly Gaither for providing the x38 data and David Ebert for allowing them to reuse images from [25]. The authors would like to thank Gary Settles for images of shadowgraph and schlieren photographs. The authors would also like to thank Jeremy Thornock and Diem Nguyen from the Center for the Simulation of Accidental Fires and Explosions (C-SAFE) for providing the helium data. The authors would also like to thank Jamal Mohd-Yusof for his help and ideas for their paper. Additional thanks go to Tim McIntyre for the use of his interferometry example image and Mathias Schott for his assistance generating a volume rendering of the helium data set. This publication is based on work supported by: DOE: VACET, C-SAFE Alliance Center; KUS-C1-016-04 awarded by King Abdullah University of Science and Technology (KAUST); the US National Science Foundation (NSF): CNS-0615194, CNS-0551724, CCF-0541113, IIS-0513212; and the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under contract DE-AC52-06NA25396.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectflow visualization.
dc.subjectGPUs and multicore architectures
dc.subjectScalar field data
dc.titlePhysically-Based Interactive Flow Visualization Based on Schlieren and Interferometry Experimental Techniques
dc.typeArticle
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphics
dc.contributor.institutionUniversity of Utah, Salt Lake City, United States
dc.contributor.institutionUniversitat des Saarlandes, Saarbrucken, Germany
dc.contributor.institutionTerraSim, Inc., Pittsburgh, United States
dc.contributor.institutionLos Alamos National Laboratory, Los Alamos, United States
kaust.grant.numberKUS-C1-016-04


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