lecture 1: introduction dr. jafar habibi. outline modeling and simulation what? why? uses taxonomy...
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Lecture 1: Introduction
Dr. Jafar Habibi
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OutlineModeling and Simulation
What?Why?UsesTaxonomy
Model Development Life Cycle
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Modeling and SimulationDefinitionsModel
A (usually miniature) representation of something; an example for imitation or emulation [Merriam-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]
SimulationThe imitative representation of the functioning of one
system or process by means of the functioning of another [Merriam-Webster dictionary]
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System: set of objects, joined to accomplish some purpose
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Why Simulate?It may be too difficult, hazardous, or expensive to
observe a real, operational systemParts of the system may not be observable (e.g.,
internals of a silicon chip or biological system)
Uses of simulationsAnalyze systems before they are built
Reduce number of design mistakesOptimize design
Analyze operational systemsCreate virtual environments for training,
entertainment
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When is simulation appropriate?Allows access to system internals that may otherwise not be
observable. Informational, organizational, and environmental changes can
be simulated, and the effect of these changes on the model’s
behavior can be observed.Observations based on simulations give great insight into the
system behavior, and it can be determined which variables are most important and how they interact.
Analytic solutions can be verified.Simulation allows to experiment with new designs or policies
prior to implementation.Can be used for training without the cost and disruption of on-
the-job learning.The simulated system is so complex, that its interactions can
be treated only through simulation
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When simulation is not appropriate?Would common sense suffice?Is there an analytical solution?Is it easier to perform direct measurements on a physical
system?Is there a shortage of resources for implementing the
simulation?Is there a shortage of time for getting the desired results?Is data lacking for modeling the system and beginning a
simulation study?Is there enough time and personnel to verify and validate
the model?Are the managers’ expectations unrealistic?Is the system too complex to be modeled?
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Models in SimulationTypes of models
Physical modelsSimulation modelsAnalytical models
But why model?UnderstandingImprovementOptimizationDecision making
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Applications: System Analysis“Classical” application of simulationTelecommunication networksTransportation systemsElectronic systems (e.g., microelectronics,
computer systems)Battlefield simulations (blue army vs. red
army)Ecological systemsManufacturing systemsLogisticsFocus typically on planning, system design
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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
Applications: On-Line Decision Aids
livedatafeeds
analysts anddecision makers
forecasting tool(fast simulation)
situationdatabase
interactive simulation
environment
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Applications: Virtual EnvironmentsUses: training (e.g., military, medicine,
emergency planning), entertainmentSimulations are often used in virtual
environments to create dynamic computer generated entities
Adversaries and helpers in video gamesDefense: Computer generated forces (CGF)
Automated forcesSemi-automated forces
Physical phenomenaTrajectory of projectilesBuildings “blowing up”Environmental effects on environment (e.g., rain
washing out terrain)
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QuickTime™ and a Graphics decompressor are needed to see this picture.
Wargaming: test strategies; training
Earth magnetosphere: understand space weather
Transportation systems: improved operations; urban planning
Computer communicationnetwork: protocol design
Parallel computer systems: developing scalable software
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Simulation FundamentalsA 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 SimulationsTypes of simulation
Constructive: simulated people operating simulated equipment
Virtual: real people operating simulated equipment,
Live: real people operating real equipmentMajor application areas
Analysis Wargaming, logistics
Training Platform level, Command level
Test and evaluation Hardware-in-the-loop
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Types of Simulation ModelsSystem model
deterministic stochastic
static dynamic static dynamic
continuous discrete continuous discrete
Monte Carlosimulation
Discrete-eventsimulation
Continuoussimulation
Discrete-eventsimulation
Continuoussimulation
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Stochastic vs. DeterministicStochastic 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 customerOutput is a random quantity (multiple runs
required analyze output)Deterministic simulation: a simulation
containing no random elementsExamples
Simulation of a digital circuit Simulation of a chemical reaction based on differential
equationsOutput is deterministic for a given set of inputs
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Static vs. Dynamic ModelsStatic models
Model where time is not a significant variableExamples
Determine the probability of a winning solitaire handStatic + stochastic = Monte Carlo simulation
Statistical sampling to develop approximate solutions to numerical problems
Dynamic modelsModel focusing on the evolution of the system
under investigation over timeMain focus of this course
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Continuous vs. DiscreteDiscrete
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
ContinuousState of the system is viewed as changing
continuously across timeSystem typically described by a set of
differential equations
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Course OverviewThis course is basically about going from
to
An actual or envisioned system A useful simulation model of that system
• Discrete event simulation• Continuous simulation• Monte Carlo simulation• Simulation software
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Course OutcomesRecognize mathematical parameters as if they were physical
variables and vice-versaBe able to follow general mathematical concepts of
derivation of engineering or scientific result and possess the mathematical skill to link those concepts
Be able to understand the relevance of the mathematical results to physical applications
Have the ability to use computational tools for finding graphical, numerical, statistical and analytic solutions to problems
Have the ability to use systems simulations appropriate to engineering practice
Be able to identify input, output, and operating variables as appropriate in various units
Be able to identify technical relationships between the input, output and variables and use the relationships to predict mutualchanges
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Define goals, objectives of study
Develop conceptual model
Develop specification of model
Develop computational model
Verify model
Validate model
Fundamentally an iterative
process
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Determine Goals and ObjectivesWhat does you (or the customer) hope to
accomplish with the modelMay 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 costImproved efficiency will save the company $$$
Example: electronicsEven 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 ModelAn abstract (i.e., not directly executable)
representation of the systemWhat should be included in model? What can be
left out?What abstractions should be used
Level of detailOften a variation on standard abstractionsExample: 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 ModelA more detailed specification of the model
including more specificsCollect 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 modelExample: Path planning for vehicles
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Develop Computational ModelExecutable simulation modelSoftware approach
General purpose programming languageSpecial purpose simulation languageSimulation packageApproach often depends on need for
customization and economics Where do you make your money? Defense vs. commercial industry
Other (non-functional) requirementsPerformanceInteroperability with other models/tools/data
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VerificationDid 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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ValidationDid I build the right model?Does the computational model match the
actual (or envisioned) system?Typically, compare against
Measurements of actual systemAn analytic (mathematical) model of the systemAnother simulation model
By necessity, always an incomplete activity!Often can only validate portions of the modelIf you can validate the simulation with 100%
certainty, why build the simulation?
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SummaryModeling and simulation is an important,
widely used technique with a wide range of applicationsComputation power increases (Moore’s law) have
made it more pervasiveIn 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…
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Courtesy of Professor Richard Fujimoto