jun ni, ph.d. m.e research services, its

Distributed Physically-based

Art and Live Animation on the

GRID

Presented at Prof. Joe Kearney’s lectureJun Ni, Ph.D. M.EJun Ni, Ph.D. M.E

Research Services, ITSResearch Services, ITS

Interactive Kites Flying

Shalini Venkataraman, Dept. of CS

EVL, University of ChicagoEVL, University of Chicago

NCSA, University of IllinoisNCSA, University of Illinois

OutlineOutline

Introduction to Grid ComputingIntroduction to Grid Computing State of the art of high performance State of the art of high performance

computing tele-immersive VR computing tele-immersive VR applicationapplication

Motivation and backgroundMotivation and background Physically based modelPhysically based model Implementation with VR and no-grid Implementation with VR and no-grid

simulationsimulation Grid computing based simulationGrid computing based simulation

Introduction to Grid Introduction to Grid ComputingComputing

Geologically distributed “virtual Geologically distributed “virtual supercomputer” in virtual organizationsupercomputer” in virtual organization NSF MiddlewareNSF Middleware NSF and DOE supported globus project, NSF and DOE supported globus project,

TeriGrid (CalTech, NPACI, ANL, NCSA) TeriGrid (CalTech, NPACI, ANL, NCSA) (ongoing $54 millions)(ongoing $54 millions)

NSF ITR projects NSF ITR projects Grids everywhere (next generation of Grids everywhere (next generation of

computing)computing) Combination of grid computing together Combination of grid computing together

with tele-immersive VR applicationwith tele-immersive VR application

State of the art of high State of the art of high performance computing performance computing

tele-immersive VR tele-immersive VR application on internetapplication on internet

Globally network-basedGlobally network-based Physical model based scientific Physical model based scientific

animationanimation Tele-immersive VR applicationTele-immersive VR application Art designArt design

Motivation and background Motivation and background

French sculpter and light artist, Jackie Matisse creates teflon or crepe kites, with artistic tails as long as 15 feet, that can soar through the air, ripple through water, or undulate with the air currents in a room.

Randomly influenced by natural forces, the kitetails move, and metamorphose in faint air currents and dramatically changing natural light

Motivation and background Motivation and background

The VR piece was inspired by the three-screen collaborative video Sea Tails created in 1983 by Matisse with filmmaker Molly Davies. The film follows ten kitetails on their dancing flight through the air and into the water.

Physically Based Model Physically Based Model

To ensure stability, the simulation has to be performed in very small time steps making them very computationally intensive.

Implicit approaches to mass-spring systems in the context of VR environments

using a grid computing system with its geographically dispersed processors linked by high-speed networks


Each kite is modeled as a cloth object treated as a cluster of masses and springs

Using fundamental laws of dynamics to calculate various forces acting on these masses and springs in order to account for the movement of each kite


Mesh Model is introduced to each grid point P(i,j) and each point has its mass and linked to neighboring points

Position x(i,j) obeys dynamic laws

Discretize dynamic law

dx(i,j)/dt = F(i,j)/m(i,j) Newton’s second law

x (i,j) t+dt = x(i,j) t + t v(I,j) t+dt


Internal and external forces acting on each point of grids Internal forces:

structural, shearing and bending forces

Fin(i,j) = k (Lt – Lo)[ P(i,j)-P(k,l) ]

Elasticity


Internal and external forces acting on each point of grids External gravitational force

Fg(i,j) = m(i,j) g

Gravitational acceleration


Internal and external forces acting on each point of grids Wind forces

Fw(i,j) = n(i,j) [ w – v(i,j) ] n (i,j)

Air or fluid viscosity


Internal and external forces acting on each point of grids viscous forces

Fw(i,j) = - n(i,j) v(i,j)

Damping coefficient

Implementation with VRImplementation with VR

CAVE VR environment EVL’s CAVE CAVE Library


Standalone kite Texture mapped onto

the kitetail mesh User can use wand to

grab on the kite head and move or change its imagery

Wind direction is controlled by wand orientation (constant wind speed)

Head controlled by wand in CAVE system


Standalone kite Other properties

such as stiffness, length, width and visual attributes like texture maps can be specified at the rum-time by user

Each kite dimension is 2 ft by 30 ft in virtual space modled by 250 mass-points



Standalone kite (no grid) Simulation rate for on kite

takes 125 iteration per second.

Each iteration takes 8 ms. In 3-kite simulation, each

kite has 41 ms/s. Small time step makes

more stable but more computer intensive

SGI ONYX Inifite Reality with 8 198 MHz MPIS R10000 processors and 2G memory.


Grid computing based Grid computing based simulationsimulation

Distributed simulation Small time steps Grid enhanced High-speed

network based Architecture of gird

enhanced application to kite simulation

Grid Computing Based Grid Computing Based SimulationSimulation

Distributed simulation Configure several

simulation nodes globally distributed

QUANTA middleware (collection of network programming tools for optimizing data sharing over high-speed networks)


Distributed simulation kiteServer (database server for wind direction

as a 3-float array; any user interaction results will be received and broadcast to other nodes)

kiteSim (simulation server for computing each kite’s position and directly transmitted through UDP socket to dispply client running in CAVE system)

kiteDisplay (client) Implementation (displays the kitetails and user-

interaction. The kite positions will read from kiteServer and display texture mapped with images


Results Distributed simulation rate

(1000iterantions/s) is significant higher than standalone simulation (125 iterations/s)

Simulation rate is dependent of the number of kites due to network bandwidth. With increasing number of kites, simulation rate approaches to constant.


Discussion Network latency Interactions among kites Fluid models Communication between kites Virtual space for flying aircrafts (Jun Ni’s

proposal) using physically based mathematical models in CFD fro fluid flow along each craft and deformable body model for each object of craft

Interactive sound tracks What about your suggestions?

ReferenceReference

http://www.evl.uic.edu/research/http://www.evl.uic.edu/research/template_res_project.php3?indi=231template_res_project.php3?indi=231

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