Intro to Simulation and Virtual Reality CE001661

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Intro to Simulation and Virtual RealityCE00166-1

• Animation v Simulation
• Week 3

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What is simulation?

• Simulation is Designing, Implementing, Running,
  and Using either Models or Software to represent
  systems
• Simulation is Used
• To gain insights into the system
• To predict what a system will do
• For Education and Training
• When it is better than using the system
• Some Examples
• Flight and Traffic simulators
• Simulation of Slow Processes or New Designs
• Simulations to drive Animations
• Monte Carlo Simulation

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Monte Carlo simulation

• How did Monte Carlo simulation get its name?
  Monte Carlo simulation was named for Monte
  Carlo, Monaco, where the primary attractions are
  casinos containing games of chance. Games of
  chance such as roulette wheels, dice, and slot
  machines, exhibit random behaviour.
• The random behaviour in games of chance is
  similar to how Monte Carlo simulation selects
  variable values at random to simulate a model.
  When you roll a die, you know that either a 1, 2,
  3, 4, 5, or 6 will come up, but you don't know
  which for any particular roll. It's the same with
  the variables that have a known range of values
  but an uncertain value for any particular time or
  event (e.g. interest rates, staffing needs, stock
  prices, inventory, phone calls per minute).

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Technical Attractions of Simulation

• Ability to compress time, expand time
• Ability to control sources of variation
• Avoids errors in measurement
• Ability to stop and review
• Ability to restore system state
• Facilitates replication
• Modeler can control level of detail

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Ways To Study A System
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Introduction

• What is discrete-event simulation?
• Modeling, simulating, and analyzing systems
• Computational and mathematical techniques
• Model construct a conceptual framework that
  describes a system
• Simulate perform experiments using computer
  implementation of the model
• Analyze draw conclusions from output that assist
  in decision making process

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Characterizing a Model

• Deterministic or Stochastic
• Does the model contain stochastic components?
• Randomness is easy to add to a DES
• Static or Dynamic
• Is time a significant variable?
• Continuous or Discrete
• Does the system state evolve continuously or only
  at discrete points in time?
• Continuous classical mechanics
• Discrete queuing, inventory, machine shop models

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Definitions

• Discrete-Event Simulation Model
• Stochastic some state variables are random
• Dynamic time evolution is important
• Discrete-Event significant changes occur at
  discrete time instances
• Monte Carlo Simulation Model
• Stochastic
• Static time evolution is not important

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Model Taxonomy
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DES Model Development

• Algorithm 1.1 How to develop a model
• Determine the goals and objectives
• Build a conceptual model
• Convert into a specification model
• Convert into a computational model
• Verify
• Validate
• Typically an iterative process

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Three Model Levels

• Conceptual
• Very high level
• How comprehensive should the model be?
• What are the state variables, which are dynamic,
  and which are important?
• Specification
• On paper
• May involve equations, pseudocode, etc.
• How will the model receive input?
• Computational
• A computer program
• General-purpose PL or simulation language?

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Verification vs. Validation

• Verification
• Computational model should be consistent with
  specification model
• Did we build the model right?
• Validation
• Computational model should be consistent with the
  system being analyzed
• Did we build the right model?
• Can an expert distinguish simulation output from
  system output?
• Interactive graphics can prove valuable

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A Machine Shop Model

• 150 identical machines
• Operate continuously, 8 hr/day, 250 days/yr
• Operate independently
• Repaired in the order of failure
• Income 20/hr of operation
• Service technician(s)
• 2-year contract at 52,000/yr
• Each works 230 8-hr days/yr
• How many service technicians should be hired?

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System Diagram
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Algorithm Applied

• Goals and Objectives
• Find number of technicians for max profit
• Extremes one techie, one techie per machine
• Conceptual Model
• State of each machine (failed, operational)
• State of each techie (busy, idle)
• Provides a high-level description of the system
  at any time
• Specification Model
• What is known about time between failures?
• What is the distribution of the repair times?
• How will time evolution be simulated?

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Algorithm Applied

• Computational Model
• Simulation clock data structure
• Queue of failed machines
• Queue of available techies
• Verify
• Software engineering activity
• Usually done via extensive testing
• Validate
• Is the computational model a good approximation
  of the actual machine shop?
• If operational, compare against the real thing
• Otherwise, use consistency checks

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Graphics algorithms for visual simulation
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What would we have to model/simulate ?

• models of shape -- need to be deformable
  skeleton, skin
• lighting (shadows)
• physical motion
• light bending by lenses (refraction)
• surface texture
• controlling/specifying motion
• perspective
• depth of field
• camera model / camera control
• sound effects music
• story

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Physics Simulation

• Particles
• Rigid bodies
• Collisions, contact, stacking, rolling, sliding
• Articulated bodies
• Hinges, constraints
• Deformable bodies (solid mechanics)
• Elasticity, plasticity, viscosity
• Fracture
• Cloth

• Fluid dynamics
• Fluid flow (liquids gasses)
• Combustion (fire, smoke, explosions)
• Phase changes (melting, freezing, boiling)
• Vehicle dynamics
• Cars, boats, airplanes, helicopters, motorcycles
• Character dynamics
• Body motion, skin muscle, hair, clothing

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Computer Animation

• What is Animation?
• Make objects change over time according to
  scripted actions
• What is Simulation?
• Predict how objects change over time according to
  physical laws or behavioral analysis

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Principle of Traditional Animation Disney

• Squash and Stretch
• Slow In and Out
• Anticipation
• Exaggeration
• Follow Through and Overlapping Action
• Timing
• Staging
• Straight Ahead Action and Pose-to-Pose Action
• Arcs
• Secondary Action
• Appeal

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Squash and Stretch
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Slow In and Out
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Anticipation
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Principle of Traditional Animation Disney

• Squash and Stretch
• Slow In and Out
• Anticipation
• Exaggeration
• Follow Through and Overlapping Action
• Timing
• Staging
• Straight Ahead Action and Pose-to-Pose Action
• Arcs
• Secondary Action
• Appeal

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Computer Animation

• Animation Pipeline
• 3D modeling
• Motion specification
• Motion simulation
• Shading, lighting, rendering
• Postprocessing

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Keyframe Animation

• Define Character Poses at Specific Time Steps
  Called Keyframes

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Keyframe Animation

• Interpolate Variables Describing Keyframes to
  Determine Poses for Character in between

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Inbetweening

• Linear Interpolation
• Usually not enough continuity

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Inbetweening

• Spline Interpolation
• Maybe good enough

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Inbetweening

• Cubic Spline Interpolation
• Maybe good enough
• May not follow physical laws !!

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Summary

• Simulation is
• Animation is
• Graphics is a simulation
• Animation Requires ...
• Modeling
• Scripting
• Inbetweening
• Lighting, shading
• Rendering
• Image processing