Modeling and Simulation: Fundamentals and Implementation

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Modeling and Simulation Fundamentals and
Implementation

• Introduction

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Outline

• Modeling and Simulation
• What?
• Why?
• Uses
• Taxonomy
• Course Overview
• Model Development Life Cycle

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Modeling and Simulation

• Definitions
• Model
• A (usually miniature) representation of
  something an example for imitation or emulation
  Mirriam-Webster dictionary
• A description of observed behavior, simplified by
  ignoring certain details. Models allow complex
  systems to be understood and their behavior
  predicted within the scope of the model, but may
  give incorrect descriptions and predictions for
  situations outside the realm of their intended
  use. www.learnthat.com
• Simulation
• The imitative representation of the functioning
  of one system or process by means of the
  functioning of another Mirriam-Webster
  dictionary
• A sham object counterfeit !!!

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Why Simulate?

• It may be too difficult, hazardous, or expensive
  to observe a real, operational system
• Parts of the system may not be observable (e.g.,
  internals of a silicon chip or biological system)
• Uses of simulations
• Analyze systems before they are built
• Reduce number of design mistakes
• Optimize design
• Analyze operational systems
• Create virtual environments for training,
  entertainment

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Applications System Analysis

• Classical application of simulation
• Telecommunication networks
• Transportation systems
• Electronic systems (e.g., microelectronics,
  computer systems)
• Battlefield simulations (blue army vs. red army)
• Ecological systems
• Manufacturing systems
• Logistics
• Focus typically on planning, system design

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Applications On-Line Decision Aids
interactive simulation environment
analysts and decision makers
live data feeds
forecasting tool (fast simulation)
situation database

• Simulation tool is used for fast analysis of
  alternate courses of action in time critical
  situations
• Initialize simulation from situation database
• Faster-than-real-time execution to evaluate
  effect of decisions
• Applications air traffic control, battle
  management
• Simulation results may be needed in only seconds

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Applications Virtual Environments

• Uses training (e.g., military, medicine,
  emergency planning), entertainment
• Simulations are often used in virtual
  environments to create dynamic computer generated
  entities
• Adversaries and helpers in video games
• Defense Computer generated forces (CGF)
• Automated forces
• Semi-automated forces
• Physical phenomena
• Trajectory of projectiles
• Buildings blowing up
• Environmental effects on environment (e.g., rain
  washing out terrain)

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A Few Example Applications
Earth magnetosphere understand space weather
Wargaming test strategies training
Transportation systems improved operations
urban planning
Computer communication network protocol design
Parallel computer systems developing scalable
software
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Simulation Fundamentals

• A computer simulation is a computer program that
  models the behavior of a physical system over
  time.
• Program variables (state variables) represent the
  current state of the physical system
• Simulation program modifies state variables to
  model the evolution of the physical system over
  time.

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Defense Simulations

• Types of simulation
• Constructive simulated people operating
  simulated equipment
• Virtual real people operating simulated
  equipment,
• Live real people operating real equipment
• Major application areas
• Analysis
• Wargaming, logistics
• Training
• Platform level, Command level
• Test and evaluation
• Hardware-in-the-loop

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Types of Simulation Models
System model
stochastic
deterministic
static
dynamic
static
dynamic
Monte Carlo simulation
continuous
continuous
discrete
discrete
Continuous simulation
Continuous simulation
Discrete-event simulation
Discrete-event simulation
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Stochastic vs. Deterministic

• Stochastic simulation a simulation that contains
  random (probabilistic) elements, e.g.,
• Examples
• Inter-arrival time or service time of customers
  at a restaurant or store
• Amount of time required to service a customer
• Output is a random quantity (multiple runs
  required analyze output)
• Deterministic simulation a simulation containing
  no random elements
• Examples
• Simulation of a digital circuit
• Simulation of a chemical reaction based on
  differential equations
• Output is deterministic for a given set of inputs

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Static vs. Dynamic Models

• Static models
• Model where time is not a significant variable
• Examples
• Determine the probability of a winning solitaire
  hand
• Static stochastic Monte Carlo simulation
• Statistical sampling to develop approximate
  solutions to numerical problems
• Dynamic models
• Model focusing on the evolution of the system
  under investigation over time
• Main focus of this course

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Continuous vs. Discrete

• Discrete
• State of the system is viewed as changing at
  discrete points in time
• An event is associated with each state transition
• Events contain time stamp
• Continuous
• State of the system is viewed as changing
  continuously across time
• System typically described by a set of
  differential equations

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Course Overview

• This course is basically about going from

to
A useful simulation model of that system
An actual or envisioned system

• Discrete event simulation
• Continuous simulation
• Monte Carlo simulation
• Simulation software

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Model Development Life Cycle
Define goals, objectives of study
Develop conceptual model
Develop specification of model
Fundamentally an iterative process
Develop computational model
Verify model
Validate model
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Determine Goals and Objectives

• What does you (or the customer) hope to
  accomplish with the model
• May be an end in itself
• Predict the weather
• Train personnel to develop certain skills (e.g.,
  driving)
• More often a means to an end
• Optimize a manufacturing process or develop the
  most cost effective means to reduce traffic
  congestion in some part of a city
• Often requires developing a business case to
  justify the cost
• Improved efficiency will save the company
• Example electronics
• Even so, may be hard to justify in lean times
• Goals may not be known when you start the
  project!
• One often learns things along the way

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Develop Conceptual Model

• An abstract (i.e., not directly executable)
  representation of the system
• What should be included in model? What can be
  left out?
• What abstractions should be used
• Level of detail
• Often a variation on standard abstractions
• Example transportation
• Fluid flow?
• Queueing network?
• Cellular automata?
• What metrics will be produced by the model?
• Appropriate choice depends on the purpose of the
  model

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Develop Specification Model

• A more detailed specification of the model
  including more specifics
• Collect data to populate model
• Traffic example Road geometry, signal timing,
  expected traffic demand, driver behavior
• Empirical data or probability distributions often
  used
• Development of algorithms necessary to include in
  the model
• Example Path planning for vehicles

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Develop Computational Model

• Executable simulation model
• Software approach
• General purpose programming language
• Special purpose simulation language
• Simulation package
• Approach often depends on need for customization
  and economics
• Where do you make your money?
• Defense vs. commercial industry
• Other (non-functional) requirements
• Performance
• Interoperability with other models/tools/data

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Verification

• Did I build the model right?
• Does the computational model match the
  specification model?
• Largely a software engineering activity
  (debugging)
• Not to be confused with correctness (see model
  validation)!

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Validation

• Did I build the right model?
• Does the computational model match the actual (or
  envisioned) system?
• Typically, compare against
• Measurements of actual system
• An analytic (mathematical) model of the system
• Another simulation model
• By necessity, always an incomplete activity!
• Often can only validate portions of the model
• If you can validate the simulation with 100
  certainty, why build the simulation?

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Summary

• Modeling and simulation is an important, widely
  used technique with a wide range of applications
• Computation power increases (Moores law) have
  made it more pervasive
• In some cases, it has become essential (e.g., to
  be economically competitive)
• Rich variety of types of models, applications,
  uses
• As easy (actually, easier!) to get wrong or
  misleading answers as it is to get useful results
• Appropriate methodologies required to protect
  against major mistakes. Even so