ase

Aerospace Systems Engineering

The Fuzzy Front End

Dr. Daniel P. Schrage

Professor and Director, CASA, CERT, & PLMC

Dr. Dan DeLaurentis

Asst. Professor, ASDL

School of Aerospace Engineering

Georgia Institute of Technology

Atlanta, GA 30332-0150







Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Presentation Outline

• Introduction to Systems Engineering, the Systems

Engineering Process and Systems Analysis

• Modern Systems Engineering and the Quality

Revolution

• The Five Lean Principles as Guiding Principles for

Modern Systems Engineering

• Integrated Product/Process Development (IPPD)

through Robust Design Simulation (RDS) for the Fuzzy

Front End to Identify Customer Value and the Value

Stream

• The Goal for Perfection through creation of a Virtual

Stochastic Life Cycle Design Environment (VSLCDE)

• A Robust Design Simulation (RDS) Example

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Systems Engineering

• Systems Engineering has been defined as an

interdisciplinary engineering management process to

evolve and verify an integrated, life cycle balanced set

of system solutions that satisfy customer needs (SE

Fundamentals, DSMC, 1999)

• Systems Engineering methods and tools were

developed in the early 1960s to decompose and

breakdown complex Aerospace Systems, e.g. Ballistic

Missile, Launch Vehicles, Aircraft

• These methods and tools contributed greatly to winning

the ―Space Race‖ and the ―Cold War‖

• However, a Modern Approach to Systems Engineering

must reflect the Quality Revolution which has driven

industry for the past 20 years

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Systems Engineering Process

• As developed for the Department of Defense (DoD) the

Systems Engineering Process includes three major

elements:

– Requirements Analysis

– Functional Analysis and Allocation

– Synthesis

• Systems Analysis and Control include the techniques

and tools to analyze and control the Systems

Engineering Process

• The Systems Engineering Process is applied to each

stage of life cycle development, one level at a time

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The DoD Systems Engineering Process

(SE Fundamentals, DSMC, 1999)

P

R

O

C

System Analysis

E

Requirements and Control

S

Analysis (Balance)

S

Requirements

I Loop

N

Functional Analysis

P

Allocation

U

T Design

Loop

Verification

Synthesis









PROCESS OUTPUT

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Systems Engineering Process

Process Input

• Customer Needs/Objectives/

Requirements

- Missions

- Measures of Effectiveness

- Environments System Analysis

- Constraints Requirements Analysis

• Technology Base • Analyze Missions & Environments & Control

• Output Requirements from Prior • Identify Functional Requirements (Balance)

Development Effort • Define/Refine Performance & Design

• Program Decision Requirements Constraint Requirement

• Requirements Applied Through • Trade-Off Studies

Specifications and Standards Requirement Loop • Effectiveness Analysis

• Risk Management

Functional Analysis/Allocation • Configuration Management

• Decompose to Lower-Level Functions • Interface Management

• Allocate Performance & Other Limiting Requirements to • Performance Measurement

All Functional Levels - SEMS

• Define/Refine Functional Interfaces (Internal/External) - TPM

• Define/Refine/Integrate Functional Architecture - Technical Reviews

Design Loop

Synthesis

• Transform Architectures (Functional to Physical)

• Define Alternative System Concepts, Configuration

Verification Items & System Elements

• Select Preferred Product & Process Solutions

• Define/Refine Physical Interfaces (Internal/External)

Related Terms:

Customer = Organization responsible for Primary Functions Process Output

Primary Functions = Development, Production/Construction, Verification, • Development Level Dependant

Deployment, Operations, Support Training, Disposal - Decision Data Base

Systems Elements = Hardware, Software, Personnel, Facilities, Data, Material, - System/Configuration Item

Services, Techniques Architecture

- Specification & Baseline

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Why Systems Analysis?

• Systems Analysis is a scientific process, or methodology,

which can best be described in terms of its salient problem-

related elements. The process involves:

– Systematic examination and comparison of those

alternative actions which are related to the

accomplishment of desired objectives

– Comparison of alternatives on the basis of the costs

and the benefits associated with each alternative

– Explicit consideration of risk

• NASA, DoD, and Industry are realizing that more

emphasis must be placing on enhancing systems analysis

at the front end of the life cycle using modern systems

engineering approaches



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Lean Principles for Modern Systems Engineering

• Systems Engineering developed in the early 1960s for a

top down hardware decomposition approach

• Systems Analysis and Control used to track and evaluate

implementation

• Work Breakdown Structure (WBS) identified work

packages for pulling in the complete supply chain

• Software Engineering was developed in 1980s along a

parallel path

• Quality Revolution of the 1980’s revealed the need for a

quality emphasis, e.g. Concurrent Engineering, IPPD, JIT

Six Sigma and Lean Manufacturing

• Quality Emphasis has cumulated into a set of Lean

Principles , as first identified in the Womack and Jones

Book on Lean Thinking

• Modern Systems Engineering should start with the Lean

Principles as Guiding Principles

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Evolution of Systems Engineering and Software

Engineering Standards

2000+



ISO

15288

Systems 1994 1998 2000+

Engineering

EIA/IS ANSI/EIA ANSI/EIA

1994 632 632 632

1974 Mil-Std-

1969 1998

499B

Mil-Std-

499A 1994 EIA/IS 731 SE

Mil-Std- .

CapabModel

499 IEEE

1220 1998

Others ...

(Trial Use) IEEE

1220

IEEE

1220

Updates



1995 1998?

Software 1988 ISO/IESC US

Engineering 12207 12207

Dod-Std-

2167A

1997

1987 1994 1996

-Std-

Dod Mil-Std- IEEE 1498 J-Std-

1703 498 /EIA 640 016

1988 (Draft)

Dod-Std-

7935A



Application of standards is the realm of Systems Engineers

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

Source: INCOSE Systems

www.asdl.gatech.edu Engineering Handbook

Quality Revolution - Where Competition is Today

Cost Advantage

Cheap Labor

Hi Volume, Lo Mix Production

Quality

Statistical Process Control

Variability reduction

Customer Satisfaction Time-to-Market

Cycle time Comparison (JIT)

Integrated Product/Process Development

Product/Process Simulation

Hi Skill adaptable Workforce

Manufacturing Product Variety

Enterprise

Cost Independent of Volume

Flexibility

Agile and Lean

Commercial/Military Integration

Virtual Companies

Company Goodness

Environment

1960 1970 1980 1990 2000

NCAT Report, 1994

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Japanese Auto Industry Used Concurrent Engineering To

Make Design Changes Earlier Than U.S. Auto Industry with Reduced Cycle Time



Japanese/U.S. Engineering Change Comparison

Number of Engineering Product









U.S. Company

Changes Processed









Japanese

Company









90%

Total Japanese

Changes Complete



Months









Months

Months









Months









Job #1

20-24









14-17









1-3









+3

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Quality Engineering Process

provides Recomposition







Knowledge Feedback







Seven Quality Robust Statistical

Customer Management Function Design Methods Process

and Planing Deployment (Taguchi, Six - Control

Tools Sigma, DOE)

Off-Line Off-Line Off-Line On-Line



• Identify •Variation •Hold Gains

•Needs Important Experiments

•Continuous

Items

•Make Improvement

Improvements







Having heard the “voice of the customer”, QFD prioritizes where improvements

are needed; Taguchi provides the mechanism for identifying these

improvements

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Lean Principles as Guiding Principles for

Modern Systems Engineering

• Establish and Specify value: Value is defined by

customer in terms of specific products & services,

preferably as a Benefits to Cost Ratio (BCR)

• Identify the value stream: Map out all end-to-end linked

actions, processes and functions necessary for

transforming inputs to outputs to identify and eliminate

waste (Value Stream Map or VSM)

• Make value flow continuously: Having eliminated

waste, make remaining value-creating steps ―flow‖

• Let customers pull value: Customer‘s ―pull‖ cascades all

the way back to the lowest level supplier, enabling just-

in-time production

• Pursue perfection: Pursue continuous process of

improvement striving for perfection

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu Womack and Daniel T. Jones, Lean Thinking (New York: Simon & Schuster, 1996).

Source: James

Relationship of Lean Principles to Systems and

Quality Engineering Activities

• Value is established based on Systems Engineering activities, such as

requirements definition and functional analysis and allocation, and

Quality Engineering activities, such as use of the seven management &

planning tools and Quality Function Deployment (QFD) to Define the

Problem and Establish Value, through the identification of an Overall

Evaluation Criterion (OEC)

• The Value Stream is next determined through system synthesis &

analysis for Generating and Evaluating Alternatives for establishing

customer focused life cycle activities along a timeline, often domain or

agency specific

• Make Value Flow through decision-making to track, system analyze

and control the OEC periodically along the life cycle process, e.g.

earned Value,

• Let Customers Pull Value to apply the Lean Principles throughout the

System Work Breakdown Structure (WBS) to sub-contractors, vendors

and suppliers, e.g. the Supply Chain Integration

• Pursue Perfection is to apply robust design and 0ptimization

approaches for Process Improvement toward attaining Six Sigma,

which is accomplished by shifting the mean to the target and

variability reduction , e.g. Statistical Process Control techniques

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Relationships between Requirements, DoD

Acquisition, RDTE, and Industry Design Processes

Operational MNS Phase 0 Phase I ORD Phase II Phase III

Req’ts. Documents

Mission Area Concept Program Definition Engineering and Production, Demilitarization

Analyses (MAAs) } Exploration &

Risk Reduction

Manufacturing

Development

Deployment and

Operational Support

&

Disposal

DoD Acquisition

Process Phases M.S. 0 M.S. I M.S. II M.S. III









Advanced Concept Technology

Demonstrations (ACTDs)



Advanced Technology Manufacturing

Demonstrations (ATDs) (7.8)

Technology



S&T Categories

and RDT&E

6.1 6.2 6.3 6.4



Basic Exploratory Advanced Engineering

Research Development Development Development







Industry Design Phases



Product Conceptual Preliminary Detailed

Design Phases Design Design Design



Increasing Fidelity of Analysis and Test





Process System Parameter Tolerance Statistical Process

Design Stages Design Design Design Control



Off Line On Line

Quality Quality



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Interaction Between the Defense Acquisition System, the

Requirements Generation System, and the PPBS

(Latest DoD 5000.2)





FUNDING & REQUIREMENTS

 Full funding commitment occurs when systems-

Technology

Opportunities & level work commences

User Challenges  Maintain requirements flexibility early to facilitate

cost-performance trades







X D

Advanced

D

System

D

System

C Production

IOC

Concept Development Integration Demo Readiness &

Production & Support

Exploration

IPR IPR LRIP Deployment

IPR

Risk Reduction & Demonstration

Funding BA 2 or 3 BA 3 BA 4 BA 5 BA 5/Prod Production/O&M

Research Category 6.2/6.3a 6.3a 6.3b 6.4 6.4



Exploratory Advanced Engineering Production Production

Development Development Development Readiness/

IOT&E, LRIP



Requirements MNS Initial ORD ORD





BA: Budget Activity

MNS: Mission Need Statement

ORD: Operational Requirements

Document Full Program Funding

LRIP: Low Rate Initial Production

IOT&E: Initial Operational Test and through outyears

Evaluation







Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

NASA’s Life Cycle Process Model

(2nd Generation RLV Risk Reduction Solicitation)



Phase 1 Phase 2 Production Operations



Effectiveness Issues

Requirement Issues Design Issues Verification Issues



Design Accepted

Selected

Reqmts Product

NASA System Detailed Design

Reqmt Verification Operations

Commercial Analyses Design Design

DOD

Customer Baseline

Reqmts Approved

System Technology Design

System

Model Analyses Performance

Measures Production









Safety

Reliability Define Scale, Enable Full

Define Priority

Refine for Fidelity, Test Scale

Affordability Commercial

Risk

Environment Development

Reduction

Mission Convergence for Tech With 20%

Needs

Demos Margin

Performance





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

A Value Example: Military Transport Aircraft

Overall Evaluation Criterion (OEC)







 ( Ai )   ( MCI )   ( Si )

OEC 

 ( LCC )



Ai 

MTBF

MCI 



Payload  Dis tan ce

Time



MTBF  MTTR WEmpty  WFuel







Si = 1 - PDPHPK LCC = RDTE + PC + O&S +DC





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Coninuous RDS along the System Life Cycle to link the

“fuzzy front end” to the “process capability approaches”

Continuous Product Improvement / Innovation

Uncertainty Risk Management/Reduction



Overall Fuzzy Front End



Evaluation

Criterion Upper Specification

(OEC)

Response



OEC Target



Lower Specification



Bring the Development Process Approach Six-Sigma,

Define Distributions Under Control, C p =1 1






System Definition

System Design System Integration Manufacturing

&

(Preliminary/Parameter) (Detail/Tolerance) (On-Line Quality)

Tech. Development

(Conceptual/System)









Traditional C p and C pk Approach for Continuous, On-line Process Improvement

Overall Upper Specification



Evaluation

Criterion

(OEC)

Response OEC Target

Lower Specification



Six-Sigma Achieved,

Cp = 2





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

Initial Distribution Reduced Variability and Improved Mean Response

CASA/CERT/PLMC

www.asdl.gatech.edu Time

The VSLCDE- Key Characteristics

The purpose of VSLCDE is to facilitate design decision-

making over time (at any level of the organization) in the

presence of uncertainty, allowing affordable solutions to be

reached with adequate confidence. It is a research testbed.



 Virtual . . . Simulation-based system life-cycle prediction

 Stochastic . . . Time-varying uncertainty is modeled; temporal decision-making

 Life-Cycle . . . the design, engineering development, test, manufacture, flight test,

operational simulation, sustainment, and retirement of a system. The operational

simulation includes virtual testing, evaluation, certification, and fielding of a vehicle in the

existing infrastructure, and tracking of its impact on the economy, market demands,

environment.

 Design . . . Implies that the environment’s main role is to provide knowledge for use by

decision-makers, especially for finding robust solutions

 Environment . . . Implies the support of geographically distributed analyses and people

through collaboration tools and data management techniques

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Concept Exploration

Alternative Concepts

Analysis of Alternatives

M System Level Requirements O

N R

S D

System Analysis

Requirements and Control

Analysis (Balance)

Requirements

Loop

Functional Analysis

Allocation

Design

Loop

Verification

Synthesis









Tech

MS 0 Review MS 1



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

What is IPPD?

• Integrated Product/Process Development (IPPD) is a

management methodology that incorporates a systematic

approach to the early integration and concurrent application of

all the disciplines that play a part throughout a system‘s life cycle

(Technology for Affordability: A Report on the Activities of the Working Groups to

the Industry Affordability Executive Committee, The National Center for Advanced

Technologies (NCAT), January 1994)

• IPPD evolved out of the commercial sector‘s assessment of what

it took to be world class competitive in the 1980s

• The DoD has required IPPD and the use of IPTs where practical

throughout the DoD Acquisition Process for Major Systems

(DoD 5000.2R)

• Conduct of IPPD requires Product/Process Simulation using

Probabilistic Approaches



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Traditional Design & Development Using only a Top Down

Decomposition Systems Engineering Process









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Life Cycle Cost Gets Locked In Early for Complex

Systems using only Systems Engineering Decomposition









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Concurrent vs Serial Approach









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

IPPD Requires the Computer Integration of Product and Process Models and

Tools for System Level Design Trades and Cycle Time Reduction



CONCEPTUAL

DESIGN

(SYSTEM)

SYSTEM SYSTEM

PROCESS FUNCTIONAL

RECOMPOSITION DECOMPOSITION

Process Product

Trades Trades

PRELIMINARY PRELIMINARY

DESIGN DESIGN

(PARAMETER) (PARAMETER)









INTEGRATED

COMPONENT Process PRODUCT COMPONENT

Product

PROCESS Trades PROCESS FUNCTIONAL

Trades

RECOMPOSITION DEVELOPMENT DECOMPOSITION









DETAIL DETAIL

DESIGN DESIGN

(TOLERANCE) (TOLERANCE)

Process Product

Trades Trades

PART PART

PROCESS FUNCTIONAL

RECOMPOSITION DECOMPOSITION

MANUFACTURING

PROCESSES

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Georgia Tech Generic IPPD Methodology

COMPUTER-INTEGRATED ENVIRONMENT

QUALITY TOP-DOWN DESIGN SYSTEMS

ENGINEERING M ETHODS DECISION SUPPORT PROCESS ENGINEERING M ETHODS





7 M&P TOOLS AND REQUIREMENTS

ESTABLISH

QUALITY FUNCTION & FUNCTIONAL

THE NEED

DEPLOYMENT (QFD) ANALYSIS









PRODUCT DESIGN DRIVEN

PROCESS DESIGN DRIVEN









DEFINE THE PROBLEM SYSTEM DECOMPOSITION

&

FUNCTIONAL ALLOCATION

ESTABLISH

VALUE





ROBUST DESIGN GENERATE FEASIBLE

ASSESSMENT & SYSTEM SYNTHESIS

ALTERNA TIVES THROUGH MDO

OPTIMIZATION



EVALUATE

ALTERNA TIVE



ON-LINE QUALITY

SYSTEM ANALYSIS

ENGINEERING & MAKE DECISION &

STATISTICAL

PROCESS CONTROL







Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Georgia Tech Generic IPPD Methodology

• Methodology provides a procedural design (trade-off iteration)

approach based on four key elements:

– Systems Engineering Methods and Tools (Product design

driven, deterministic, decomposition approaches; MDO is usually based

on analytic design approach)

– Quality Engineering Methods and Tools (Process design driven,

nondeterministic, recomposition approaches; MDO is usually based on

experimental design approach)

– Top Down Design Decision Process Flow (Provides the design

trade-off process required for Complex Systems)

– Computer Integrated Design Environment(Information

Technology driven to provide a collaborative interactive environment)

• Methodology has been implemented through Robust

Design Simulation (RDS) for a number of applications

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Georgia Tech Graduate Program in Aerospace

Systems Design & Analysis

• Initiative kicked of in the early 1990s based on IPPD Approach

• Education executed in the School of Aerospace Engineering and

research program executed through the Center for Aerospace

Systems Analysis (CASA) and its two major Laboratories, The

Aerospace Systems Design Laboratory (ASDL) and The Space

Systems Design Laboratory (SSDL)

• Currently has approximately 140 graduate students with over 80

% U.S. citizens – top students from top universities

• Research Program currently at approximately $6M per year

including four faculty and 15 research engineers, plus 100 GRAs

• Program is built on probabilistic approaches for implementing

IPPD through Robust Design Simulation

• Goal is to develop, verify and validate, in collaboration with

industry and government, a Virtual Stochastic Life Cycle Design

Environment (VSLCDE)

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

A System Integration and Practice-Oriented M.S.

Program in Aerospace Systems Design & Analysis



Semester I Semester II Summer



Design Methods/Techniques ISE/PLMC

Aerospace Disciplinary Propulsion Development

Systems Electives Systems

Engineering Design

Special

Project

Applied

Systems Applied

Systems

Design II

Design Design II

Design II

Safety

By Design

Advanced Advanced Product

Design Design Life Cycle

Methods I Methods II Management

Internship

Design Tools/Infrastructure

Mathematics (2 Required) Other Electives



Dr. Daniel P. Schrage Legend: Core Classes Elective Classes

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Aerospace Systems Design Integrated

Education & Research Philosophy

Industry

Government

Partners: • Methods Formulation Relevant Partners:

ONR • Supports Basic Research Problems GEAE

NASA • Implementation of Methods RRA

Data & Tools LMTAS

AFRL

NRTC Funding Boeing

Funding Sikorsky





Aerospace Systems

Design Laboratory









Methods Students

Classroom Implementation









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Complex System Formulation initially taught In Aerospace

Systems Engineering using an Integrated Set of Simple Tools



Tech. Alternative

QFD Identification

HOWs alt. concepts Engine Type

Baseline

MFTF

1 st Option

Mid-Tandem

2 nd Option

Turbine Bypass

Fan

Fan 3 Stage 2 Stage No Fan









criteria

Combustor Conventional RQL LPP

Nozzle Conventional Conventional + Mixer Ejector

Acoustic Liner Nozzle

Aircraft None Circulation Hybrid Laminar

Technologies Control Flow Control

Weights Morphological Matrix



Pugh Evaluation Matrix



MADM Best

Alternative

Subjective Evaluation

(through expert opinion,

surveys, etc.)

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Current Complex System Formulation Projects in

Aerospace Systems Engineering Course (Fall 2003)

1. AIAA Graduate Student Missile Design Competition “Multi

Mission Cruise Missile Design”

2. AHS Student Design Competition for “Design Certification

Mountain Rescue Helicopter”

3. NASA Identified Complex System of Systems Problem: “Future

Air Transportation Architecture - A System of Systems

Problem‖

4. NASA Specific Complex System Problem: ―Space Shuttle

Derivative: What it takes to make it Safe and Flyable‖

5. NASA Identified Complex System Problem: “Two Stage

Turbine Based Combined Cycle (TBCC) Space Access

Launch Vehicle”

6. NASA Aerospace Vehicle Systems Technology Office Student

Design: “Quiet Supersonic Business Jet and Transport”



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Current Complex System Formulation Projects in

Aerospace Systems Engineering Course (Fall 2003)

7. Homeland Security and Coast Guard Initiative: “Feasibility of

Accelerating the Integrated Deepwater System (IDS): A

Network Centric Complex System”

8. Missile System Technical Committee: “Long Range Liquid

Target Vehicle (LRLTV)”

9. University Student Design Competition: “International Micro

Aerial Vehicle (MAV) Competition”

10. Complex System Formulation for: “Boeing 7E7

“Dreamliner” Commercial Transport”

11. Complex System Formulation for : “Morphing UCAV

Aircraft”

12. NASA Aerospace Vehicle Systems Technology Office Student

Design Competition for: “Unmanned Air Vehicle Systems

and Technologies: Replacement for Helios”



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Roadmap to Affordability Through

Robust Design Simulation



Robust Design Simulation

Subject to



Design & Environmental

Robust Solutions

Technology Constraints

Infusion

Objectives:

Physics- Schedule

Based Economic Budget

Synthesis Operational

Modeling Simulation Life-Cycle Reduce LCC

& Sizing Environment Analysis

Increase Affordability

Activity and Increase Reliability

Process- .....

Based

Modeling Economic & Impact of New

Discipline Technologies-

Uncertainties Performance &

Schedule Risk Customer

Satisfaction

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Synthesis & Sizing

is the key for translating Mission into Geometry

Safety



Safety

Aerodynamics Economics

Aerodynamics Economics

Geometry



Synthesis & Sizing

S&C Mission Manufacturing

S&C Manufacturing





Integrated Routines

Table Lookup Increasing

Sophistication and

Structures Performance Complexity

Conceptual Design Tools

(First-Order Methods)

Approximating Functions

Direct Coupling of Analyses Propulsion

Structures Performance









Preliminary Design Tools

(Higher-Order Methods)



Propulsion

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Aircraft Life Cycle Cost Analysis (ALCCA) -

including Economic Cash Flow Analysis

AIRCRAFT

WEIGHTS

ENGINE RDT & E

THRUST & WGHT. COSTS

LABOR MANUFACTURER

RATES

AIRCRAFT UNIT

CALCULATE YES MANUFACTURER ROI VS PRICE

MANUFACTURING MANUFACTURER CASH-FLOW

COSTS

PRODUCTION

COSTS CASH-FLOW

QUANTITY ROI

LEARNING AVERAGE

CURVES COST



NO





AIRCRAFT MISSION

PERFORMANCE A i r l in e P ro d u c ti o n

FUEL, INSURANCE

Y i el d Q u a n t it y

DEPRECIATION RATES AIRLINE ROI

OPERATING

LABOR & BURDEN

RATES

COST



P R IC E

INDIRECT









DIRECT

COSTS









COSTS









AIRLINE AIRLINE

CALCULATE YES ROI VS PRICE

AIRLINE ROI RETURN ON

INVESTMENT

NO





TOTAL

Dr. Daniel P. Schrage OPERATING

Georgia Institute of Technology

Atlanta, GA 30332-0150

COST

CASA/CERT/PLMC

www.asdl.gatech.edu

Risk & Uncertainty are Greatest at the Front

Known Unknowns correspond to Risk and Known Probability Distribution









KNOWNS





KNOWN-UNKNOWNS









UNKNOWN-UNKNOWNS





CONCEPT VALIDATION FULL PRODUCTION DEVELOPMENT

SCALE

DEVELOPMENT









UnKnown Unknowns correspond to Uncertainty and Unknown Probability Distribution

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Five Step RDS Process

2

Determine System Feasibility 1

Problem Definition









Design Space Model

Constraint Fault Tree Identify objectives, constraints,

x

P(feas)

1 design variables (and associated

Xi = Design Variable side constraints), analyses,

Ci = Constraint FPI(AIS) or Monte Carlo x

2

uncertainty models, and metrics

AND

x

3

C1 C2 C3 C4



3

Examine Feasible Space Constraint

Cumulative Distribution

Functions (CDFs)

N P(feas) Y

P









Design Space Model

< esmall

x

1

FPI(AMV) C1

5 P

Decision Making or

x Monte Carlo

2

• MADM Techniques C2

• Robust Design Simulation P

• Incorporate Uncertainty Models x Relax

3 Y

Constraints?

C3



4

Technology Identification/Evaluation/Selection (TIES)

• Technology Selection • Identify Technology Alternatives Relax Active Y

• Resource Allocation Obtain New CDFs Constraints

• Collect Technology Attributes

• Robust Design Solution ?

• Form Metamodels for Attribute Metrics P N

through Modeling & Simulation

• Incorporate Tech. Confidence Shape Fcns. Ci

• Probabilistic Analysis to obtain CDFs for the Old Tech.

Alternatives New Tech.



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Interactive RDS Environment

FPI / MC

FPI CDF









or Requirement 2

Criterion 2

e

ac

100%

n Sp

DISCIPLINARY RSEs tio









Probability

pira

As

Aero

0%

Structures Objective JPDM



Weights Criterion 1

or Requirement 1

Etc. Concept Space









Metrics/Objectives

 Responses

Technology

Space Requirements









Metrics/Objectives

 Responses

Space

SYNTHESIS & SIZING









Metrics/Objectives

 Responses

Constraints









Constraints









Constraints

1









TW R

Dynamic

Contour ² %$/RP M









Dr. Daniel P. Schrage RSEs Plots

Georgia Institute of Technology

Atlanta, GA 30332-0150

-1



-1 SW

TO F Lm od

SL N m od

1

CASA/CERT/PLMC

www.asdl.gatech.edu

Two Examples on Application

of IPPD Through RDS

System of Systems: CDSE Process for FCS

FST Team in Phase I

Derivative Program: F-18C Conversion to

F-18E/G





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

FST Process Methodology

for FCS Phase I

Concept Development

& Systems Engineering

Methodology Incorporates IPPD Principles, QFD,

Analysis, Engineering Simulation, Systems Engineering

Tools, and Force-on-Force Simulation

(This presentation does not reflect

the current thinking of the FCS LSI)

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Full Spectrum Team(FST): 2408W-C07





One of Four Teams in FCS Phase I







SBA/SMART

Soldier C4ISR

Systems





Weapons/ FCS Robotics

Platforms



Deployment /

O&O Sustainment









Dr. Daniel P. Schrage Working Closely With Government Labs

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC A0042

www.asdl.gatech.edu

Network Centric System Design of a Lethal Brigade



The FST Brigade is designed to fight with precision fires and high lethality

• Diverse, overlapping

fires and sensor coverage

Command

Vehicle Sensor systems

at all echelons Tethered UAV Command

Vehicle HQ External

• Near and far fires with RSTA

augmentation



area and precision effects Vehicle Command Brigade

Vehicle



• Multiple layered sensor echelon

MM Radar

NLOS

coverage

Typical Vehicle Sensors Tethered UAV Command MM Radar BDE Air Defense

EO/IR sensor/sight Vehicle Scout

Vehicle ARV Marsupial

laser detectors NLOS BG

glint detectors UGV

NetFires

Medium UAV



RSTA Battle Group

Marsupial RSTA Small UAV NLOS

Vehicle 9 ton variants

UGV Mortar

Command

Marsupial Vehicle Scout NetFires

UGV Vehicle ARV Battle Group

Infantry echelon

Carrier Command

NLOS RSTA Battle Unit

Mortar

Soldier Systems Vehicle ARV



Stinger BlkIIE

NLOS Vehicles

HUMMWV SUV Soldier

Systems

Mortar LOS/BLOS Assault Battle Unit Objective Crew Served Weapon

command vehicles

omitted echelon ARV

Dr. Daniel P. Schrage

Weapon systems

Georgia Institute of Technology

Common Missile

CASA/CERT/PLMC

Objective Crew Served Weapon

Atlanta, GA 30332-0150 External

www.asdl.gatech.edu augmentation

Concept Development & Systems

Engineering (CDSE) Process

• Incorporates key aspects of modern systems engineering

approaches, and lends itself to iteration

– Requirements Flowdown

– Engineering Trades / Analysis

– Force Effectiveness Modeling and Simulation

– Risk Mitigation

• Allows full exploration of need identification and problem

definition, concept development, and concept selection—prior

to system definition and design

• Facilitates group work and utilizes modern software based

tools

• Allows full incorporation of increasingly detailed simulation-

based analyses and designs

• Smoothly extends throughout engineering and manufacturing

phases

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Integration of QFD, Morph and Pugh Products

Into CDSE Process

HOWs

QFD 1-4 Tech. Alternative

Identification

HOWs alt. concepts Baseline 1st Option 2nd Option

Engine Type MFTF Mid-Tandem Turbine Bypass

Fan

Fan 3 Stage 2 Stage No Fan

Pugh



criteria

Combustor Conventional RQL LPP



Evaluation Nozzle



Aircraft

Conventional



None

Conventional +

Acoustic Liner

Circulation

Mixer Ejector

Nozzle

Hybrid Laminar



Matrix Technologies Control Flow Control





Morphological Matrices

Weights



Multi Attribute

Decision

QFD Methodology Best

“Context” Alternative

Rationale

Subjective Evaluation

(through expert opinion,

Preliminary

surveys, etc.)

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Full Spectrum Team FCS Concept

Development & Systems Engineering (CDSE) Process



Selected Force End Start 1 2

Iteration Guidance - QFD 1 Guidance - QFD (2-4)

Pugh Force Concepts

 AUTL  Missions

Concepts  TRADOC Docs  Tasks

Selection Matrix MOEs  MNS  Functions



 Capabilities







7

Force 3

Design Guidance Systems

Effectiveness

Alternative 1  Organizational Capabilities

Simulations

O&O  Operational

Recomposition









Decomposition

 Engineering



Morph Matrices -

6 Selection of

Force Concepts Pugh Force Consistent Sets

Alternatives 1 Concepts of Systems and

Selection Matrix Technologies

5

4

Systems Concept 2 Technology /

Set 1 Characteristics 1

Concept Subsystems

Characteristics Options



Legend Process is Parallel Technology

Missions /

Products Decision Systems Concept IPT

and Iterative Scenarios Trees

Technology IPT

Dr. Daniel P. Schrage Requirements IPT All IPTs

CASA/CERT/PLMC

A0031t

Georgia Institute of Technology

Atlanta, GA 30332-0150

www.asdl.gatech.edu

Focus on Requirements

Flowdown through QFD

AUTL QFD4

Commander

National Imperatives /









Missions AUTL Combat

Centric Co-Owned

Tasks

with Concepts IPT

Army Vision









Warfare

HOWs (Title) Customer

Customer Importance









Assessment







HOQ





How

Direction of Improvement

HOWs (Title) Customer

Functions System





Customer Importance

Assessment









How

What









HOQ

Missions

Capabilities Technologies

WHATs (Title)









AUTL



Direction of Improvement

HOWs (Title) Customer









What









Customer Importance

Assessment









AUTL Combat

How

HOQ

WHATs (Title)









Centric Functions

HOWs (Title) Customer

How Much









Customer Importance

Assessment











Direction of Improvement









How

Organizational Difficulty HOQ









Tasks



What









System Capabilities

Assessment









Commander

Technical









WHATs (Title)

How Much Direction of Improvement











Weighted Im portance









What

Organizational Difficulty

Relative Im portance

Assessment









WHATs (Title)

Technical











How Much









Morpho-

Weighted Im portance



Relative Im portance

Organizational Difficulty









Assessment

Technical



How Much









Weighted Im portance

Organizational Difficulty



logical









Assessment

Relative Im portance









Technical

Weighted Im portance



Relative Im portance Matrix

Requirements CDSE ITERATION 2

IPT Incorporate Four Detailed Determine Force

Characteristics to Select

Mission Scenarios Perform Functions Technologies

and Combine

into Candidate

Candidate Concepts

Criteria / MOEs









Concepts









Best Concepts Determined in Process That Trades

Dr. Daniel P. Schrage Technologies, Concepts and Requirements

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Representative FCS Trade Studies

• Communications Trades • Weapons Trades

– Peer-to-Peer and Client-Server – Guns and Missiles

– Islands and Backbones – 105 and 120 BLOS/LOS

– Frequency Bands and Bandwidth – Precision and Area Fires

– QoS (Latency, Availability, Bandwidth)

• Sensor Trades

• C2 Trades – Radar, EO/IR, and Ladar

– Legacy, New, Organic, Joint – Systems (UAV, UGV, Mast, Tethered)

– Initiative and Control – On Board and Off Board Fusion

• Platform Trades – ATR and Clutter (False Alarms)

– Tracks and Wheels • Survivability Trades

– Weight Studies (16t, 9t, 6t)

– Active and Passive

– Modularity and Commonality Studies

– Collective and Individual

– Hybrid and Conventional Propulsion

– Links and Nodes

– Turbine and Diesel

– Wheel and Body Motors • HMI Trades

– Active and Passive Suspension – Autonomy, Responsibility, Workload

– Commonality, Simplicity

• Force Trades

– Motion and Maneuver

– UAVs and UGVs

– Manned and Unmanned • Logistics

– Mounted snd Dismounted – OPTEMPO and Sustainment

– BLOS / NLOS / LOS Mix – Deployment (Weights, Times, Pulses)

–

Dr. Daniel P. Schrage BU Size and Composition – Prognostics, Distribution, Log Support

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

The Trade Space is Defined

by a Full Spectrum of Scenarios

Sensitivity Environment

analysis will Scenario 1

help drive to Solution

“The FCS

Solution”

“The Potential

FCS FCS

Solution” Solution

Potential

Scenario 2

FCS

Solution

Solution

Scenario 3

Solution

Threat



Mission

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Scenario Driven OMS/MP Development

Bosnia FST concept robustness is tested against a

FST Scenarios: Scenario broad range of missions & scenarios which

Forcing Function Korean Desert Storm gain validity from extensive quantitative &

for Full Spectrum Scenario Scenario

Chad qualitative wargaming & analysis

Force Development

Scenario

Tabletop

MAPEX

JANUS Operational

Initial Force Four Wargaming Examination

Four

Scenario

Concepts Initial CASTFOREM

Specific

1&2 OMSMPs Wargaming

OMS/MP

Inputs Scenario Weighting





Full Spectrum

Force Concepts Force

Concept Composite OMS/MP

(Version 4.1) (Scenario Specific Consolidation)

Wargaming Feedback



Force Concept Was Developed Iteratively & in Parallel With OMS/MP Refinements

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Evolution of FCS Force Concepts

Concept 1 Concept 2

 Heavy reliance on robotic ground vehicles  Heavy reliance on NLOS range engmts / UAVs

 More RSTA and assault  Significantly more payload volume

 Deployable by current rotary wing aircraft 2012 FUE  Mostly 18-ton vehicles



 Max vehicle weight 10 tons  Peer-to-peer information architecture

 Client-server information architecture  Based on Small Unit Operations comms tech

 Evolutionary comms and software systems  New software services

Baseline

 Blending of Concept 1 and 2--Robotics and NLOS engmts

 6-ton ARVs, 9-ton CVs, 16-ton max vehicle weight

 Helo vertical envelopment with smaller vehicles

2012 FUE  16 ton limit for C-130 deployment on unimproved runways

 Hybrid information architecture

 Peer-to Peer with communication islands



 Based on Small Unit Operations comms technology

 New software services





BLK I / BLK II

 BLK I - Employ available weapons systems

- Most vehicles manned

2008 FUE BLK I - 9-ton vehicles become 16 ton

2013 FUE BLK II  BLK II – Employ advanced weapons systems

- Many advanced robotic systems

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

 Same information and comms arch. as Baseline CASA/CERT/PLMC

www.asdl.gatech.edu

Concept Baseline Alternatives Summary



Concept 1 Concept 2

Sensors • Smaller Number of UAVs • Larger Number of UAVs

• Large Number of Modestly Capable • Small Number of Highly Capable

Ground Sensors (ARVs) Ground Sensors (RSTA Vehicle)

Information • Client Server • Peer-to-Peer

• Evolutionary Software • Distributed, Dynamic

Deciders • Highly Automated • Highly Automated

Commander-Centric Commander-Centric

(Unit/Group) (Group/Brigade)



Actors • Full Spectrum, but Weighted for the • Full Spectrum, but Weighted for

Red Zone Beyond the Red Zone

• Robotic ARV, DF, Netfires, NLOS • Robotic Netfires, Small UGV

• Manned C2, CV, and Infantry • Manned C2, Infantry Carrier, Direct

Carrier Fire, RSTA and Short Range NLOS

• CH-47 Transportable (<10 tons) • C-130 Transportable (<18 tons)

• Many Dispersed 4, 6, and 9 Ton • Common Chassis with

Vehicles (~650) Modules (~340)

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Baseline 2012 Concept

Network Centric Functional Overview

Soldier

Sensors Organic UAVs, UGVs (EO/IR, FoPen, SAR, MTI)

Organic Manned R&S with Tethered Sensors

Aided Target Recognition

Augmenting UAVs and Satellites

Service Layer View Distributed Command and Control Services Real Time and Hard Real Time Processors





Actors Deciders



Services Layer



Information Layer

Sensors





S

e

c

Planning

Planning



2

Air Space CC2

Air Space

Execution

Execution





Intelligence

Intelligence

Information

Information

Operations

Operations

Soldier System Interface



Warfighter C2 Services

Fires &&

Fires

Effects



Combat Service

Effects



Combat Service

Support

Support

Situation

Situation Reconnaissance && C4ISR Systems

Awareness

Awareness





&&Simulation

Simulation

Reconnaissance

Surveillance



Training, Rehearsal,

Surveillance



Training, Rehearsal, Nontactical

Nontactical

Support

Support

C4ISR Systems

Management

Management

Standard

Standard

Office Tools

Office Tools

Vehicle Mgmt

and Control

Organic Multi-Layer Peer-to-Peer Coms

u Common C2 Services

Synchronization & Navigation & Distributed

r Scheduling

Terrain Reasoning MCG&I

Positioning Collaborative Ops

Personalization



i

Communications Layer







Opportunistic Ground, UAV, and Satellite Links

Information Management (IM) Services

t









Info System

Common Object

Knowledge Base DataStore Geospatial Data Plan Force Structure Profile

y Store IM(

b)

Radios and networking Multicast Proxy

QoS &

Policy

Dissemination Interest Mgmt Component Federation Interoperability IM(a



- kernels Computing Platform Services

)





- controllers Network / Communications Services Sensor Arrays and Fusion

- processors







State- of- the-

Industry Standard and State-of-the-Art Techniques for Heterogeneous Computing

App1

f1

App3

App3

App1

f2f1

f1

App2

f1

App2

f2





“Middle Ware”/Operating System Services:

App3

f1 App4

f1

App4

f2

App5

TOOLS

AND STANDARDS

Integrated Software/Hardware Open Architecture

f1

Implementation of the REAL TIME COMPUTING Meta-H environment









and Procedures

OPERATING ENVIRONMENT







SEI and Emerging Processes

Industry Standards and

SAGE

UML

Autocoders

M&S simulations

System Integ Lab

Distributed Warfighter Services

established protocol









NLOS LS

Deciders Brigade, Battle Group, Battle Unit

BDE

HQ

RSTA

Group

Battle

Group Group Group Common Relevant Operating Picture

Decision and Planning Aids

Precision and Area NLOS Fires

Actors Precision BLOS / LOS Weapons

Net Fires

Soldier

System

Armed Reconnaissance Vehicles OCSW on all Combat Vehicles

105 NLOS

Mix of Manned and Unmanned Platforms

Infantry / Soldier Systems

120mm Mortar NLOS Infantry Carrier /

Dr. Daniel P. Schrage C2 / CV Large Number of Stowed Kills

CASA/CERT/PLMC

Systems

105 LOS / BLOS

Georgia Institute of Technology

Atlanta, GA 30332-0150

www.asdl.gatech.edu

FCS Phase I Results

• Talented team pulled together through a generic

IPPD methodology extended to address the

‗system of systems‘ problem for FCS CDSE

• An integrated set of simple tools proved useful for

the first iteration through the process

• Success of the Concept Development and Systems

Engineering (CDSE) Process developed and

utilized is summarized by the rankings by the

government‘s independent assessment team

• Elements of the process are being continued by the

Boeing-SAIC Lead System Integrator (LSI)

through the current FCS Phase

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Future Combat System (FCS)

Government Independent Assessment Team’s

Phase 1 competitive scores (awarded Nov 2001)









Full Gladiator FocusVision

CONCEPT CATEGORY Spectrum Boeing (Lockheed/ (GDLS/

Team TRW) Raytheon)

Deployment 1st 2nd (tie) 2nd (tie) 2nd (tie)

Situational 1st 4th 3rd (tie) 3rd (tie)

Understanding

Lethal Effects 1st 2nd (tie) 2nd (tie) 2nd (tie)

Protection 1st (tie) 4th 1st (tie) 2nd

Sustainability 1st 2nd (tie) 2nd (tie) 4th

Transition to New 1st 4th 2nd 3rd

Mission

OVERALL 1st 4th 2nd 3rd









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

F/A-18 Example

Example Application

• Link the appropriate aircraft sizing/synthesis and economic tools plus probabilistic methods

to create testbed environment; model the F/A-18C (using substantiation data for validation)



• With F/A-18E/F requirements (Ref. AIAA Paper 98-4701) as drivers, look at relation of

technology metrics on requirements mathematically









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Expanding Missions: The F/A-18E/F

Air

Maritime Air Close Day/ All

Fighter Defense

Air Combat Recce Air Night Weather

Escort Suppres-

Superiority Fighter Support Attack Attack

sion





F-14D F/A-18 A-6F

NATF A/B/C/D







F/A-18 E/F





Ref. Young, et.al. AIAA-98-4701, 1998.





How can such multi-role vehicles be

examined as potential solutions for the

war-fighter with respect to technologies,

requirements, and design constraints ?

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Process

The traditional process of identification of an overall objective to be

optimized is replaced by the following process:



1) Using Response Surface Method to mathematically represent combined

requirements-technology-configuration space



2) search for alternatives (configuration changes plus technology infusion) that

satisfy requirements and constraints (TIES method)



3) simultaneously, optimize on desirements within this feasible space

(continuous) or set (discrete) then, perform sensitivity studies to show the

perturbation of the solution due to possible changes in requirements and design

variables.



Thus, the customer/decision maker has information with regards to the choice between

tolerating a relaxation in requirements or accepting achievable performance levels





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

F/A-18C Basic Geometry









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Primary Mission- Fighter Escort

Actual

Modeled ( )

Intermediate Thrust Climb

Cruise at Optimum Mach and Altitude

42,550 ft

(39,910 ft)

(40,000 ft) 41,300 ft

(37,796 ft) 39,300 ft

(38,904 ft)

38,100 ft









Reserves:

20 minutes Loiter at S.L.

plus 5% of T/O Fuel Combat at 10,000 ft

Start & Taxi, Accelerate to Climb Speed 2 minutes at Maximum Thrust

4.6 minutes at Intermediate Thrust, SLS Mach 1.0 (missiles retained)









Combat Radius =311 nmi

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Alternate Missions- Addressing Multi-role

Capability

• Requirements can include performance against a wide variety of missions



• Vehicle sizing proceeds based on a primary mission and then fallout performance of the sized vehicle on

alternate missions is computed and tracked

Metrics/Objectives

 Responses









Primary

Constraints









Mission

Responses





Alternative

Mission

Responses

Example: Given a vehicle sized

for Air Superiority (A-S) mission, Constraints

compute the performance for

Interdiction mission as A-S

requirements change Requirements, Vehicle Chars., or Technologies





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Alternate Mission: Hi Hi Hi

Actual

Modeled ( )

Intermediate Thrust Climb Cruise at Optimum Mach and Altitude

43,200 ft

(40,000 ft)

38,550 ft

40,600 ft

(37.911 ft) (40,000 ft)









Reserves:

20 minutes Loiter at S.L.

plus 5% of T/O Fuel Combat at Best Altitude

Start & Taxi, Accelerate to Climb Speed 5 minutes at Maximum Speed

4.6 minutes at Intermediate Thrust, SLS Mach 1.0 (missiles retained)









Combat Radius =505 nmi

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Correlation of Drag Polars for Varying Mach Numbers

Altitude = 36,089 ft









Cl









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Propulsion Modeling

F404-GE-402 Augmented Turbofan Engine



• The F404-GE-402 is an increased General Specifications:

performance derivative of the F404 • Thrust: 17,700 lb

and is used in the F/A-18C • SFC (max A/B): 1.74 lbm/lbf-hr

• Features a dual-spool mixed flow • SFC (IRP): 0.81 lbm/lbf-hr

turbofan architecture, 3X7X1X1

turbomachinery configuration • Airflow (SLS): 146 pps

• F404 Engine performance deck • Weight: 2,282 lb

based on installed engine data for • Length: 159 in

the F/A-18C • Diameter: 35 in

• Engine performance data source:

“F/A-18C Substantiating

Performance Data with F404-GE-

402 Engines” Report

MDC91B0290

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Weight Breakdown- Validation

F/A18C Weight Breakdown Comparison

Group F/A18C Baseline Model

• Sizing/Synthesis Code Used: Wing 3,919 3,918

FLight OPtimization System Tail Group 1,005 1,006

Body 5,009 5,009

(FLOPS) Alighting Gear 2,229 2,228

Propulsion Group

Engines 4,420 4,417

• F/A-18C Baseline Modeled in Engine Section

Gear Box 921 922

FLOPS calibrated against actual Controls

Starting System

substantiation data from Fuel System 1,078 1,078

manufacturer Flight Controls 1,061 1,062

Auxiliary Power Plant 206 206

Instruments 84 84

• Highly accurate model (errors Hydraulics 351 352

Electrical 592 592

in weights less than 1%) Avionics 1,864 1,864

Armament, Gun, Launchers, Ejectors 948 948

Furnishings, Load/Handling, Contingency 631 631

Air Conditioning 641 642

Crew 180 180

Unusable Fuel 207 207

Engine Fluids 114 115

Chaff, Ammunition 252 252

Miscellaneous 58 58

Operating Weight Empty 25,770 25,771

Missiles 1,410

(2) AIM-7F 1,020

(2) AIM-9L 390

Mission Fuel 10,860 10,857

Takeoff Gross Weight 38,040 38,038

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Economic Assumptions

• MALCCA (Military Aircraft Life Cycle Cost Analysis) in-house

code used to determine notional aircraft economics



• Baseline File created starting with defaults based on the military

aircraft assumptions (primarily sourced from F-15 and F-16 data)



Inflation Factor 3.3%

Dollar Year 1994

Year of Program Initiation 2000

Final Year of Production 2023

# Operational Vehicles 2530 units

System Economic Life 20 years

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Wind Over Deck

Recovery Wind Over Deck Launch Wind Over Deck



Wind Over Deck Wind Over Deck









Aircraft Weight

Aircraft Weight









Aircraft Arresting Gear Cat Plus

Airspeed

Touchdown Speed Performance A/C Thrust

Required









Speed

Speed







•Aircraft Touchdown Speed = 1.05 * Vapp

•Airspeed Required = Calculated Liftoff Speed

•Arresting Gear Performance Calculated at Limit Capacity



Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Responses/Desirements Responses and Top Level Requirements

Example Responses:

R1

Metrics/Objectives









Gross Weight

R2

Probability of Survival

R3 Lethality

O+S

R4 Acquisition Cost



R5 Approach Speed (constraint)

TOFL (constraint)

Constraints









R6



R7

T/W and W/S may belong in

either the requirements or the

Req.1 Req.2 Req.3 Req.4 Req.5 Req. 6 Req.7 Req.8

responses section - depending

Range Payload PS tloiter turn rate fW wtW Mach on how the programs are set up

Top Level Requirements

This approach de-emphasizes the geometry of an aircraft, and instead focuses on the mission requirements.

However, it does require a baseline aircraft configuration. Geometry and Technologies are fixed, while

Requirements vary. Each vector of top level requirements maps to a specific mission.





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Requirement RSEs for Notional F/A-18C

Performance Capabilities Weights Economics

Takeoff Landing

Alternate Combat Specific Combat Combat Approach Landing Field Takeoff Field Wind Over Wind Over Takeoff Gross Operation & First Unit RDT&E

Wing Loading Thrust/Weight Wing Span Mission Range Life Cycle Cost

Excess Power Turn Rate Turn Radius Speed Length Length Deck Deck Empty Weight Weight Support Cost Cost Cost

(lb/ft2) (ft) cents

(nm) (ft/s) (deg/s) (ft) (ft/s) (ft) (ft) (ft/s) (ft/s) (lbs) (lbs) (mil. $/yr) (mil. $) (mil. $)

#AC * hr * lb





95.89077









37.03584









740.7152

0.841592









1364.336









11844.52

3.746093









4088.045

152.8745









4184.198









17.36432









30061.56









42958.04

-0.99871



42.98329









5330.668









62.87922









0.792245









4380.113

-7.06871









4841.269





5885.437









3777.587

-39.8082









0.866574

0.749297









4897.278

45.0536









36214.7

23727.3









31626.8







55799.5

1014.9

1062.1





1787.6









53.187

16241

137.2









72.927

0.666









33.67









2.732

1.088









510.8









128.9

4.891









182.8

9072









2708

3137





5776









9345

68.1









40.3

-5%

 Radius

+40% -10%

Ult. Load Fact. Combat Mach #

+10% -10% +10% 0 500 1000 -5%

 Payload

(lbs)

Thrust

+40% -5%

Area

+30% 0

stealth

(lbs)



500 1000 0

# Aux. Tanks

(lbs)





2244 4488

Fuel Consumption





-15%

Specific

0%









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Requirements Exploration: F/A-18C Design Contours

Horiz Vert Factor Current X Grid Density Update Mode

Radius -0.8888 20 x 20 Immediate

ULF 0

CmbMach 0

DPayld

Thrust

-1

-0.8888

Slide bars control variable values

Area -0.857

DStealth -1

Response Auxtnk -1

SFC 1

contours may Response Contour Current Y Lo Limit Hi Limit

O&S 5500 5031.4066 ? 5500

be set here TOGW 40000 37137.19 ? 40000

LDWOD 15 4.8129793 ? 15 Constraints are set here

TOWOD 0 -20.93512 ? 0

Vapp 153 152.42056 ? 153

Ps 695 700.21755 695 ?

AltRng 920 1238.6386 920 ?



21420 Vapp







Takeoff

Wind Over Deck





Thrust Ps

(lbs.)

Landing

Wind Over Deck





TOGW

14535

360 Area (ft^2) 520

White area indicates available design space, while filled areas

Dr. Daniel P. Schrage

Georgia Institute of Technology

indicate areas which violate set constraints CASA/CERT/PLMC

Atlanta, GA 30332-0150

www.asdl.gatech.edu

Effects of Increase in Combat Radius Req.

21,420 Vapp







TOWOD



Thrust LDWOD



Ps



TOGW



14,535

Decreasing 380 Area 520 Increasing

Feasible 21,420 Vapp





Space

Combat

Thrust

TOWOD Radius

LDWOD



Ps

Reqmt.

TOGW

14,535

380 Area 520

21,420

Vapp









Thrust TOWOD

LDWOD



Ps



TOGW

14,535

380 Area 520

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Horiz Vert Factor Current X Grid Density Update Mode

Radius 0.964 20 x 20 Immediate

ULF 0.71

CmbMach 0

DPayld -1

Thrust 0.88888

Area 0.857

DStealth -1





Exploring the Space:

Auxtnk -1

SFC 0.3333

Response Contour Current Y Lo Limit Hi Limit





Capturing the F/A-18E/F !

O&S 5130 5475.7833 ? ?

TOGW 45000 47224.344 ? ?

LDWOD 30 26.563468 ? 30

TOWOD 15 13.613377 ? 15

Vapp 151 150.2058 ? 151

t_Radius 12656.5 10828.741 ? ?

t_Rate 3.8115 4.0987387 ? ?

Ps 780 807.60615 780 ?

AltRng 1540 1545.1224 1540 ?





21420 TOWOD





Vapp Alternate

Ps Range

Turn Radius

Turn Rate

TOGW



Thrust

(lbs.)









O&S

14535

380 Area (ft^2) 520

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

RDS Example: TOGW Req. for Notional F/A-18 (1)

Scenario 1: Conservative tech. improvements gives low confidence of meeting requirement

k_CDo

k CDo

k_CDi

k CDi

k_WingWt

k W in g W t.









- 0 .0 6 - 0 .0 5 - 0 .0 3 - 0 .0 2 0 .0 0 - 0 .0 6 - 0 .0 5 - 0 .0 3 - 0 .0 2 0 .0 0 - 0 .1 3 - 0 .1 0 - 0 .0 7 - 0 .0 4 - 0 .0 1









k_FusWt

k F u sW t.

k_HTWt

k HT W t.

k_VTWt

k VtW t.









- 0 .1 2 - 0 .0 9 - 0 .0 6 - 0 .0 3 0 .0 0 - 0 .1 1 - 0 .0 8 - 0 .0 5 - 0 .0 2 0 .0 1 0 .0 0 0 .2 5 0 .5 0 0 .7 5 1 .0 0









Ov erlay Chart Forecast: Ov erlap



Frequency Comparison 10,000 Trials Cumulativ e Chart 1 Outlier

.025 1.000 10000





Achieved

TOGW

.019

TOGW

.750 Probability of Satisfying

TOGW Req. =~1%

.013 .500







.006 Anticipated .250

Anticipated Req.

TOGW Req.

.000 .000 0



31,500.00 32,375.00 33,250.00 34,125.00 35,000.00 -1,000.00 0.00 1,000.00 2,000.00 3,000.00



Certainty is 99.99% from -1,000.00 to +Infinity





Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

RDS Example: TOGW Req. for Notional F/A-18 (2)

Scenario 2: Aggressive tech. improvements gives higher confidence of meeting requirement

k_CDo

k CDo

k_CDi

k CDi

k_WingWt

k W in g W t.









- 0 .1 0 - 0 .0 9 - 0 .0 7 - 0 .0 6 - 0 .0 4 - 0 .1 0 - 0 .0 9 - 0 .0 7 - 0 .0 6 - 0 .0 4 - 0 .1 7 - 0 .1 4 - 0 .1 1 - 0 .0 8 - 0 .0 5









k_FusWt

k F u sW t.

k_HTWt

k HT W t.

k_VTWt

k VtW t.









- 0 .1 7 - 0 .1 4 - 0 .1 1 - 0 .0 8 - 0 .0 5 - 0 .1 6 - 0 .1 3 - 0 .1 0 - 0 .0 7 - 0 .0 4 0 .0 0 0 .2 5 0 .5 0 0 .7 5 1 .0 0









Ov erlay Chart Forecast: Ov erlap



Frequency Comparison 10,000 Trials Cumulativ e Chart 19 Outliers

.023 1.000 10000





Achieved Probability of Satisfying

.017 TOGW .750

TOGW TOGW Req. =~45%



.011 .500







.006 Anticipated .250

Anticipated Req.

TOGW Req.

.000 .000 0



31,500.00 32,250.00 33,000.00 33,750.00 34,500.00 -1,500.00 -625.00

0 250.00 1,125.00 2,000.00









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

RDS Example:O&S $ as an Anticipated Requirement (1)

Scenario 1: Conservative tech. improvements gives low confidence of meeting requirement

k_CDo

k CDo

k_CDi

k CDi

k_WingWt

k W in g W t.









- 0 .0 6 - 0 .0 5 - 0 .0 3 - 0 .0 2 0 .0 0 - 0 .0 6 - 0 .0 5 - 0 .0 3 - 0 .0 2 0 .0 0 - 0 .1 3 - 0 .1 0 - 0 .0 7 - 0 .0 4 - 0 .0 1









k_FusWt k_HTWt k_$O&S

k F u sW t. k HT W t. k $O &S









- 0 .0 7 - 0 .0 6 - 0 .0 4 - 0 .0 3 - 0 .0 1

- 0 .1 2 - 0 .0 9 - 0 .0 6 - 0 .0 3 0 .0 0 - 0 .1 1 - 0 .0 8 - 0 .0 5 - 0 .0 2 0 .0 1









Forecast: Overlap

P(Achieved-Anticipated)

Overlay Chart

5,000 Trials Cumulative Chart 189 Outliers

Frequency Comparison

1 .0 0 0 5 00 0

.0 2 2

Probability of Satisfying

Achieved .7 5 0 $O&S Req. =~2%

.0 1 7 $ O& S/ fl th r

$O&S

.5 0 0

.0 1 1





.2 5 0

.0 0 6 Anticipated

A n tic ip a te d R e q.

$O&S Req.

.0 0 0 0

.0 0 0

-1 0 0 .0 0 0 1 00 . 00 3 00 . 00 5 00 . 00 7 00 . 00

6 ,5 0 0. 00 6 ,7 7 5. 00 7 ,0 5 0. 00 7 ,3 2 5. 00 7 ,6 0 0. 00









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Example: $O&S as an Anticipated Requirement (2)

Scenario 2: Aggressive tech. improvements gives higher confidence of meeting requirement

k_CDo

k CDo

k_CDi

k CDi

k_WingWt

k W in g W t.









- 0 .1 0 - 0 .0 9 - 0 .0 7 - 0 .0 6 - 0 .0 4 - 0 .1 0 - 0 .0 9 - 0 .0 7 - 0 .0 6 - 0 .0 4 - 0 .1 7 - 0 .1 4 - 0 .1 1 - 0 .0 8 - 0 .0 5









k_FusWt

k F u sW t.

k_HTWt

k HT W t.

k_$O&S

k $O &S









- 0 .1 7 - 0 .1 4 - 0 .1 1 - 0 .0 8 - 0 .0 5 - 0 .1 6 - 0 .1 3 - 0 .1 0 - 0 .0 7 - 0 .0 4 - 0 .1 1 - 0 .0 9 - 0 .0 8 - 0 .0 6 - 0 .0 5









Overlay Chart Forecast: Ov erlap

P(Achieved-Anticipated)

Frequency Comparison 5,000 Trials Cumulativ e Chart 9 Outliers

.0 2 0 1.000 5000



Probability of Satisfying

Achieved .750

$O&S Req. =~50%

.0 1 5 $ O& S/ fl th r

$O&S

.0 1 0 .500









.0 0 5 Anticipated .250

A n tic ip a te d R e q.

$O&S Req.

.0 0 0 .000 0



6 ,5 0 0. 00 6 ,7 5 0. 00 7 ,0 0 0. 00 7 ,2 5 0. 00 7 ,5 0 0. 00 -600.00 -300.00 0.00 300.00 600.00









Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu

Summary and Conclusions

• Systems Engineering has its roots in Aerospace

• Modern Systems Engineering must reflect the Quality Revolution

• The Five Lean Principles should serve as guiding principles for

a Modern Systems Engineering Approach

• Integrated Product and Process Development (IPPD) required in

the Fuzzy Front End to Establish Value for subsequent principle

implementation: Value Stream, Process Flow, Customer Pull,

and Perfection

• Georgia Tech has developed an IPPD through Robust Design

Simulation (RDS) environment that allows a Probabilistic

Approach for the Fuzzy Front End

• The transition of the IPPD through RDS to a Virtual Stochastic

Life Cycle Design Environment is necessary for full

implementation of the Five Lean Guiding Principles

Dr. Daniel P. Schrage

Georgia Institute of Technology

Atlanta, GA 30332-0150

CASA/CERT/PLMC

www.asdl.gatech.edu