it Interactive Exploration of Large 3-D Unstructured-Grid Data
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NASAContractorReport201618
ICASEReportNo.96-63it•/
ICA
INTERACTIVEEXPLORATIONOFLARGE
3-DUNSTRUCTURED-GRIDDATA
Kwan-LiuMa
ScottLeutenegger
DimitriMavriplis
NASAContractNo.NAS1-19480October1996
InstituteforComputerApplicationsinScienceandEngineeringNASALangleyResearchCenterHampton,VA23681-0001
OperatedbyUniversitiesSpaceResearchAssociation
NationalAeronauticsandSpaceAdministration
LangleyResearchCenterHampton,Virginia
23681-0001InteractiveExplorationof
Large3-DUnstructured-GridData
Kwan-LiuMat
InstituteforComputerApplicationsinScienceandEngineering
ScottLeuteneggertMathematicsandComputerScienceDepartmentUniversityofDenver
DimitriMavriplistInstituteforComputerApplicationsinScienceandEngineering
Abstract
Visualizingunstructured-griddatafromaerodynamicscalculationsischallengingbecauseoftheassociatedmeshesaretypicallylargeinsizeandirregularinbothshapeandresolu-tion.Thisresearchinvestigatesappropriatedatastructuresandrenderingmethodstoallowinteractiveexplorationofthedata.Inconjunctionwithfastsplattingrendering,amultiresolutiondatarepresentationbasedonagglomerationisusedtomakepossibleinteractivevisualizationonaworkstation.Thatis,dataarerenderedataparticularresolutionaccordingtovisualizationparamentersaswellasthespeedandmemorycapacityoftheworkstation.Interactivevisualizationallowstheusertoquicklydetermineregionsofinterestandimportantvisualizationparameterssuchasviewingdirectionandtransferfunctions.Wethenapplyamoreaccurate,expensiverenderingmethodtotheorignaldataontheregionsofinterest.Theoriginaldataarestoredondisk.WeshowwithbothanalysisandexperimentalresultsthatR-treeisabetterdatastructureforfastretrievalofsuchdisk-residentdata.
tThisresearchwassupportedbytheNationalAeronauticsandSpace'AdministrationunderNASAcon-tractNAS1-19480whiletheauthorwasinresidenceattheInstituteforComputerApplicationsinScienceandEngineering(ICASE)_NASALangleyResearchCenter,Hampton,
VA23681-0001.1Introduction
Inaerodynamicscalculations,unstructuredgridsareusedtomodelobjectswithcomplex
geometry.Becausethegridsaretypicallylargeinsizeandirregularinshapeandresolu-
tion,oftenspecialdataprocessingandrenderingalgorithmsareneededtomakepossiblevisualizationofthesimulationresults.Thisresearchstudiestheneededsoftwaresupport
forconductingthedesirableiterative,near-interactivevisualizationprocesstoanalyzevery
largeunstructured-griddataonanaverageworkstation.TheproposedvisualizationprocessincludesmainlytwostepsasshowninFigure1.The
firststepattemptstoderivedesirableviewingandrenderingparametersandtolocatere-gionsofinterest,asub-volume.Thismaybeperformedonaworkstationusingafastbut
lessaccuraterenderingalgorithmonacoarserepresentation,thusamuchsmallerversion,
oftheoriginaldatastoredinthemainmemoryoftheworkstation.Consequently,weneed
amulti-resolutionrepresentationoftheoriginaldata(mrrd).Themrrdallowsinteractive
explorationofthedata.Onceahotspotisidentifiedataparticularresolution,theuser
mayswitchtoviewingatahigher-resolutionforfurtherexploration.Thisexplorationpro-cesscontinuesuntiltheregionofinterestandviewingaswellasrenderingparametersare
completelydetermined.Weuseafast,approximatedsplattingalgorithmforrenderingdata
atresolutionsaccordingtovisualizationrequirements,aswellasthespeedandmemory
capacityoftheworkstation.Thesecondsteptakestheparametersderived,extractstheselectedsub-volumeoutofthe
originaldata,andinvokesamoreaccuraterenderingprogramtoproducehighqualityvisu-alizationresults.Thisstepmaybeperformedoneitheraworkstationorahigh-performance
computer.Thesub-volumerepresentsaspatialregionofinterestusuallymuchsmallerthan
theoverallgriddomainandthusmayberenderedmoreefficientlyonaworkstation.Notethatbecauseofitssize,theoriginaldatasetmustbestoredondisk.Itisthenessentialto
haveadequatedatabasesupportsuchthatasub-volumecanbequicklyretrievedfromdisk.Therefore,werepresentthedataasanR-tree[13],anefficienthierarchicaldatastructure
thathasbeenwidelyadoptedbymanydatabaseapplications.
Dataexplorationisinherentlyiterative.Thevisualizationprocessandsystemarchitec-
turedevelopedinthisresearchallowcomputationalscientiststostudytheirdataatthe
highestpossibleresolutioninamoreefficientmanner,ratherthanreducingthedataorop-
eratingataveryinefficientbatch-mode.Inthispaper,wedescribetheconstructionofthe
rnrrd,demonstratetheeffectivenessofthefastsplattingrenderingmethod,andshowthatR-treesworkbetterthanoctreeswhichhavebeenwidelyusedbythevisualizationcommunity.
Testresultswereobtainedbyusingafour-milliontetrahedra-celldataseton
workstations.Fastdataexplorationatmultipleresolutionstodetermineregionsofinterest,renderingandviewingparameters.
)
Figure1:Theproposedvisualizationprocessandsystemarchitecture.
2Multi-resolutionRendering
Multiresolutiondatavisualizationhasbeenanactiveareaofresearchbutmostprevious