systems realization laboratory the role and limitations of modeling and simulation in systems design...

Systems Realization Laboratory

The Role and Limitations of Modeling and Simulation in Systems Design

Jason Aughenbaugh & Chris ParedisThe Systems Realization Laboratory

The George W. Woodruff School of Mechanical Engineering

The Georgia Institute of Technology

November 19, 2004, Anaheim, CAIMECE2004-5981


Uncertainty: The Challenge of Design, Modeling, and Simulation

Evaluate Alternatives

GenerateAlternatives

Select Alternative

KnowledgeInformation

GenericDecisionProcess

Predictions of Consequences of DecisionsAre Always Uncertain

Analyze the results

Model the alternatives


Uncertainty: The Challenge of Design, Modeling, and Simulation



Select Alternative



Designers currently lack appropriate methods for representing and computing with

the various types of uncertainty faced in designespecially lack of knowledge


Motivation: Complexity Increasing

Increasingly complex

Increasingly multidisciplinary

Need more knowledge

Need more collaboration

HumanCommunication

HumanCommunication

Product SystemInteractions



Systems Engineering: A Decomposition Approach

Understand User Requirements, Develop

System Concept and Validation Plan

Develop System Performance Specification and System Validation Plan

Expand Performance Specifications into CI

“Design-to” Specs and CI Verification Plan

Fab, Assemble and Code to “Build-to” Documentation

Evolve “Design-tp” Specifications into “Build-

to” Documentation and Inspection Plan

Inspect to “Build-to”

Documentation

Assemble CIs and Perform CI Verification

to CI “Design-to” Specifications

Integrate System and Perform System Verification to

Performance Specifications

Demonstrate and Validate System to

User Validation Plan

Inte

grat

ion

and

Qua

lific

atio

nD

ecomposition

and Definition

Time

Systems Engineers

Discipline Engineers

Understand User Requirements, Develop

System Concept and Validation Plan

Develop System Performance Specification and System Validation Plan

Expand Performance Specifications into CI

“Design-to” Specs and CI Verification Plan

Fab, Assemble and Code to “Build-to” Documentation

Evolve “Design-tp” Specifications into “Build-

to” Documentation and Inspection Plan

Inspect to “Build-to”

Documentation

Assemble CIs and Perform CI Verification

to CI “Design-to” Specifications

Integrate System and Perform System Verification to

Performance Specifications

Demonstrate and Validate System to

User Validation Plan

Inte

grat

ion

and

Qua

lific

atio

n

Inte

grat

ion

and

Qua

lific

atio

nD

ecomposition

and Definition

Decom

position

and Definition

TimeTime

Systems Engineers


Systems Engineers


Forsberg, K., and Mooz, H., 1992, "The Relationship of Systems Engineering to the Project Cycle," Engineering Management Journal, 4(3), pp. 36-43.Forsberg, K., Mooz, H., and Cotterman, H., 2000, Visualizing Project Management: A Model for Business and Technical

The Vee Model


System Decomposition:Relating Requirements and Attributes

Requirements AttributesRequirements Attributessubsystems

system


Relating Requirements and Attributes

Engineers Decide on

Engineers Design and Build

Requirements AttributesRequirements Attributessubsystems

system

Have resultant

Must match customer

requirements


Making “good” decisions

Engineering Decisions

Engineers Build

Requirements Attributessubsystems

system

Have resultant

Must match customer

requirements

???


The Role of Modeling and Simulation

Engineers Decide on

Engineers Build

Requirements Attributessubsystems

system

Have resultant

Must match Customer

requirements

Modeling and Simulation:estimates


Decomposition is Hierarchical

Requirements AttributesAttributessubsystems

system

A


systemA


Decomposition is Hierarchical


system

A


systemAHow do subsystem decisions affect the system attributes?


Aggregation of Subsystem Attributes

Depending on system compositione.g. mass

Depending on system structuree.g. cost

Depending on system operatione.g. reliability

Resulting from complex emergent behaviore.g. queue wait times

Increasingly complex

Increasing value of simulation



HumanCommunication

HumanCommunication


Specific Uses of Modeling and Simulation


system

HumanCommunication

HumanCommunicationProduct System

InteractionsProduct System

Interactions

Models improve communication

Simulations reveal emergent behaviors

Ref. AND

1

Start System

LP

2

Run A to BLP

LP

3

Run B to ALP

AND Ref.

SystemReady at A

InPeopleA

OutPeopleB

SystemReady a...

InPeopleB

OutPeopleA

Date:Saturday, July 12, 2003

Author:Trial User

Number:0

Name:(Trial) OperateTransportation System

Models clarify requirements

I didn’t think it would do that! I wanted it to behave

more like…

Now I understand!

Models help explore robustness


Limitations of Modeling and Simulation


system

Representation and propagation of uncertainty

Limitations of knowledge: uncertainty Integration of multiple models

PID withRedundancy

SatelliteStructure

PayloadReaction Wheels

AttitudeEstimator

Signal

Mechanical

PID withRedundancy

SatelliteStructure

PayloadReaction Wheels

AttitudeEstimator

Signal

Mechanical

x1

x2

q1

q2

u1

u2

u3

u4

PDF/PMF

value[ ]u5

This is what I know:

Expressing model validity

So how accurate are these numbers?

Is he even using the right model?


How do we deal with uncertainty?

We need formalisms for• Representing uncertainty accurately

• Computing with such formalisms

• Making decisions based on these formalisms

We need to accurately express what is known• Capture as much of what is known as necessary

• Not imply information that we don’t have

Reflect different types of uncertainty


Different Types of Uncertainty

Aleatory uncertainty• Inherently random – irreducible

• Best represented as probabilitydistribution

• Examples:• Manufacturing variability

Epistemic uncertainty• Due to a lack of knowledge

• Not accurately represented as

• probability distributions

• Examples:• Error due to model approximation• Future design decisions

x1

x2

q1

q2

u1

u2

u3

u4

PDF/PMF

value[ ]u5


Possible Handling Mixed Aleatory and Epistemic Uncertainty:Probability Bounds Analysis

A p-box expresses the range of all possible CDFs that are still deemed possible based on existing knowledge.

• Example: An enveloping of all possible CDFs for normal distributions with variance of 1 and means in the interval [0,1]

• It represents aleatory uncertainty (variability) via the normal distributions

• It represents epistemic uncertainty (incertitude) via the interval on the parameters

-3 -2 -1 0 1 2 3 40

0.5

1

g3

g4

g5

g6 g1

A “P-box”

N(0,1)

N(1,1)


P-boxes: two dimensions of uncertainty

Ignorance (Epistemic) Precise

Variability (Aleatory)

-3 -2 -1 0 1 2 3 40

0.5

1

pbox

(a)

Ignorance about the variability

-3 -2 -1 0 1 2 30

0.5

1 cdf

(b)

Precise knowledge of the variability

Deterministic

-1 0 1 20

0.5

1

interval

(c)

Ignorance about the true deterministic value

0.4 0.5 0.60

0.5

1

deterministic_value

(d)

Precise knowledge of the deterministic value

Variable

Deterministic

Epistemic Precise


Summary: we need more appropriate representations of uncertainty



Select Alternative




Analyze the results



Summary: we need more appropriate representations of uncertainty



Select Alternative




Analyze the results


Better Representations

Better Selection

Better Design


Acknowledgements

Thank you for attending! This material is based upon work supported under a National

Science Foundation Graduate Research Fellowship. • Any opinions, findings, conclusions or recommendations expressed in this

presentation are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Additional support is provided by the G.W. Woodruff School of Mechanical Engineering at Georgia Tech.


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