Lecture 1: Introduction Dr. Jafar Habibi. Outline Modeling and Simulation What? Why? Uses Taxonomy Model Development Life Cycle.

Lecture 1: Introduction

Dr. Jafar Habibi

OutlineModeling and Simulation

What?Why?UsesTaxonomy

Model Development Life Cycle

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]

System: set of objects, joined to accomplish some purpose

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

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

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?

Models in SimulationTypes of models

Physical modelsSimulation modelsAnalytical models

But why model?UnderstandingImprovementOptimizationDecision making

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

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

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)

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

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.

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

Types of Simulation ModelsSystem model

deterministic stochastic

static dynamic static dynamic

continuous discrete continuous discrete

Monte Carlosimulation

Discrete-eventsimulation

Continuoussimulation

Discrete-eventsimulation

Continuoussimulation

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

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

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

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

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

Define goals, objectives of study

Develop conceptual model

Develop specification of model

Develop computational model

Verify model

Validate model

Fundamentally an iterative

process

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

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

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

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

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)!

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?

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…

Courtesy of Professor Richard Fujimoto