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.

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Figure1:Theproposedvisualizationprocessandsystemarchitecture.

2Multi-resolutionRendering

Multiresolutiondatavisualizationhasbeenanactiveareaofresearchbutmostprevious