Copyright © 2007, SAS Institute Inc. All rights reserved.
Dimitri MavrisAerospace Systems Design Laboratory (ASDL)
Georgia Institute of Technology
22Prof. Dimitri Mavris
Dr. Dimitri Mavris
Boeing Endowed Professor for Advanced Aerospace Systems AnalysisDirector, Aerospace Systems Design Laboratory (ASDL)
Guggenheim School of Aerospace Engineering Georgia Institute of Technology
Atlanta, GA, 30332-0150
A Visual Analytics Approach for the Design and Technology Assessment
of Innovative Aerospace Concepts
333Prof. Dimitri Mavris
Presentation Outline
• Vantage point• Innovation and Critical Thinking• Critical elements needed for Innovation• Barriers and enablers for Innovation to occur• What is Visual Analytics?• Means to enable technology and new methods
transition• A sample example of the current state of the art in the
aerospace community• Closing remarks
444Prof. Dimitri Mavris
ASDL: A Unique Education and Research Mission
• Produce well-trained System Analysts, System Engineers and Technologists for immediate deployment in academia, industry, and government
• Develop strong strategic partnerships with counterparts in Industry and Government
• Use these relationships to study problems of fundamental interest to both
• Promote student participation in IPT and IPPD teams, internships with industry/government, and national design competitions
• Provide leadership and innovation in the field of Advanced Design Methods (probabilistic and robust design simulations)
555Prof. Dimitri Mavris
ASDL Research Program
Formulation, development, and implementation ofcomprehensive approaches to the design of affordableand high quality complex systems, emphasizing:
• Disciplinary breadth and depth while accounting for uncertainty and risk
• Multi-disciplinary analysis, optimization and design• Reduction of analysis, design process cycle time• Physics-based analysis and design of complex systems• Systems-of-systems, architecture-based systems engineering• Interdisciplinary research, both within the schools at Georgia
Tech and through the formation of alliances with other universities, industry, and government
666Prof. Dimitri Mavris
The System Design Academy• The ASDL at Georgia Tech provides a unique opportunity for graduate
students to be developed. Emphasis is given on innovation and creativity skills to be developed.
• Well-rounded, multidisciplinary education provides breadth and depth• A wealth of activities are available to cultivate the variety of interests in our
diverse student body• ASDL education revolves/centers on the development of its students, not
overloading them with information• Technology-focused education allows students to succeed in any industry• Incorporation of policy, economics and philosophy courses into the
curriculum gives the students a different perspective and makes them better rounded
• Undergraduate and Graduate students work together in a team-centric environment
• Weaknesses are exposed and turned into strengths– Peers work to develop each other– Opportunities are provided for all students to excel
• Immense freedom contributes to unprecedented student satisfaction
777Prof. Dimitri Mavris
Motivating Factor for our ResearchN
umbe
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ngin
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hang
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20-2
4M
onth
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14-1
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onth
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1-3
Mon
ths
Job
#1
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U.S. CompanyJapanese Company
90%Total Japanese
Changes Complete
Be Creative. Innovate or Else.
888Prof. Dimitri Mavris
So What is Creativity? Where can I get it?
• Define creative.• Consider these questions:
– How do you know if something is creative?– How do you measure it?– Why do we even need it?
999Prof. Dimitri Mavris
Some words for creative?
• Original• Unique• Unusual• Different• Deep meaning• Imaginative• Practical/Impractical• Smart
101010Prof. Dimitri Mavris
A Definition of “Creative”
• According to Merriam-Webster’s Collegiate Dictionary, 10th ed.– When used as an adjective . . .
• 1. marked by the ability or power to create : given to creating
• 2. having the quality of something created rather than imitated : IMAGINATIVE
• 3. managed so as to get around legal or conventional limits : deceptively arranged so as to conceal or defraud
111111Prof. Dimitri Mavris
Creative Thinking Defined
• “Creative thinking is generally thought of as putting together information to come up with a whole new understanding, concept, or idea.”
• Kenneth D. Moore (2005)
• Phase I: Preparation• Phase II: Incubation• Phase III: Illumination• Phase IV: Verification
• Kenneth D. Moore (2005)
151515Prof. Dimitri Mavris
So why is it important?
• The thinking process is what counts. A solid process will produce a solid product.
• They have the “information,” now let them apply it in a unique and possibly unconventional way.
• In the context of educating and empowering the next generation of scientists and engineers one has to ponder. If this group of people will be the future leaders of the world? Do you want them to effectively solve the problems of the world or keep trying what’s been done in the past and failed?
161616Prof. Dimitri Mavris
Why don’t we use creative thinking more?
• Ambiguity scares both students and teachers• Cannot be measured by numbers
– How can standardized tests measure creative thinking?
• We have not built up the critical thinking capacity in our classrooms
• Many of us honestly think our students can’t handle it
• End effect: Engineers enter the workforce ill- prepared, without the necessary skill set
171717Prof. Dimitri Mavris
Some Thoughts on Innovation
• "All innovation begins with creative ideas . . . We define innovation as the successful implementation of creative ideas within an organization. In this view, creativity by individuals and teams is a starting point for innovation; the first is a necessary but not sufficient condition for the second" (Amabile et al, 1996)
• "Often, in common parlance, the words creativity and innovation are used interchangeably. They shouldn't be, because while creativity implies coming up with ideas, it's the "bringing ideas to life" . . . that makes innovation the distinct undertaking it is.“ (Davila et al, 2006)
• “Generally, the true disruptive power of an innovation lies not in the technology itself but in the business model that surrounds that technology.” (Clayton Christensen, Forbes.com, 2007)
181818Prof. Dimitri Mavris
Why Innovate?• “According to one study, 75 % of CEOs of the fastest growing
companies claim their strongest competitive advantage is unique products and services and the distinct business processes that power them to market” (Howard Smith, CTO of CSC)
• According to a study conducted by Accenture (1996-2007), leading suppliers “drive their consistent revenue increases through investing in research and development and capital expenditures at a faster rate than peers, leveraging these “growth investments” to develop more and better products continually.”
• A study published in Outlook Journal (2004), suggests that there is a small subset of automakers who are significantly better at growing revenues than their competitors. The study identifies four key drivers for their success, including product innovation.
191919Prof. Dimitri Mavris
Disruptive Innovation
• A disruptive innovation replaces the current state of the art and creates room for further improvement than was previously possible
• According to Forbes.com, the top 10 disruptors in the last 10 years are:– NetFlix– Google– Blackberry– Minute Clinic– Apple iPod– Roomba– Skype– HeartStart– YouTube
Source: Forbes.com, Clayton Christensen and Innosight
Graphic Adapted From: The Virtual Innovator by D. Arnold
202020Prof. Dimitri Mavris
The Evolution of the Automobile
www.co.rice.mn.us www.southlakes-uk.co.ukwww.cars-pictures-index.comwww.autosellers.org
1900 1910 1920 1930 1940 1950
1960 1970 1980 1990 2000 2007 +
212121Prof. Dimitri Mavris
The Evolution of the Aircraft
1900 1910 1920 1930 1940 1950
1960 1970 1980 1990 2000 2007 +
1928
1916
1903
1957
1981 2007
1947
First Jet-
Powered Aircraft
1939
www.meteorflight.com
1967
19692005
www.iiptc.comwww.wikipedia.comwww.fotoimages.comwww.aerospace.org
1994
222222Prof. Dimitri Mavris
Next Generation Innovative Concepts - Disruptors
Subsonic Transports
Supersonic Aircraft
Personal Air Vehicles
Uninhabited Air Vehicles
Rotorcraft
New Generation of Innovative Vehicles can not be modeled accurately in the absence of historical data
Extreme STOL
Motivation for Physics-based Conceptual Design
232323Prof. Dimitri Mavris
Next Generation Concepts – From Innovation to Implementation
Some Personal Observations:
– Usually there is no shortage of new ideas, creative or not.
– When you think that you have come up with something original think again. One can say that almost everything has been attempted before.
– Cross-fertilization of ideas, solutions from different fields could provide great insights and benefits.
– New ideas have a hard time overcoming organizational barriers.
242424Prof. Dimitri Mavris
Next Generation Concepts – From Innovation to Implementation
Elements needed to overcome the organizational and technical barriers so as to ensure market penetration and Successful Transition to every day use:
– Advocacy for the proposed concepts and their associated design approach. Support to overcome organizational barriers
– Fundamentally sound systems engineering processes tailored to new systems and system of systems formulations
– Practical methods and processes that can handle “Extrapolations” beyond the realm of the historical databases. A physics based approach to design
– Exploitation of technological breakthroughs
252525Prof. Dimitri Mavris
Next Generation Concepts – From Innovation to Implementation
– System level modeling and simulation to assess the technological gaps and to provide guidance, goals, impact assessments
– Means to support electronic design reviews(A parametric, multi-variate, dynamic trade-off environment)
– Methods to account for uncertainty and risk throughout the process
– Means to support strategic decision making. Visualization of the data to enable the right level of depiction for the right audience(Multi-attribute, scenario based decision making)
– Training for the new generation of designers and system engineers
262626Prof. Dimitri Mavris
Enablers Needed to Overcome Barriers
• New methods almost by definition go against the grain of established paradigms that are well defined and accepted by the practicing community and thus are always viewed with skepticism, criticism, or in some cases even cynicism
• To facilitate the introduction of new methods the following criteria must pre- exist before they can be adapted for use:– The underlying theories, methods, mathematics,
logic algorithms etc upon which the new
272727Prof. Dimitri Mavris
Enablers Needed to Overcome Barriers– cont’d
– Availability of training utilizing material written on the overarching method, tutorials, etc. with relevant examples.
– Proposed methods which are grounded on or which are complimentary to established practices have a better chance of succeeding.
– Tools automating the proposed method and making it practical for every day use. Without them the method resembles a topic of academic curiosity
– Relevant examples and applications within a given field of study.
282828Prof. Dimitri Mavris
Advanced Methods DevelopmentIn a breakdown analogous to NASA/DoD’s Technology Readiness Levels (TRLs), the
following Advanced Methods Readiness Levels (AMRLs) hierarchical approach to research has emerged as a useful construct at ASDL:
ONR/NASABasic Research
MethodsDevelopment andProof-of-Concept
(NASA, NAVAIR, AFRL, NSWC)
IndustryApplications
• Formulation Phase• Basic Principles, Fundamentals• Analytical Formulation• Cross-Fertilization of Elements• Method Initial Formation• Initial Testing
• Method Development • Application Formulation• Proof-of-Concept Implementation• Unification of Theory• Automation, Tool Development• Creation of Tutorial Material
• Method Implementation• Full System Prototype• Actual System in Application Domain• Real World, Industrial Applications• Stakeholder Involvement• Utilization, Integration and
Automation of Actual Tools and Processes
292929Prof. Dimitri Mavris
Enabling Models and Techniques
Established TechniquesResponse Surface Methodology (Biology, Ops Research)Neural Networks (Artificial Intelligence, Image Processing)Design of Experiments (Agriculture, Manufacturing)Design for Computer Simulation (Geostatistics, Physics, Nuclear)Quality Function Deployment, Pugh Diagram (Automotive)Morphological Matrix or Matrix of Alternatives (Forecasting)Multi-attribute decision making (MADM) techniques (U.S Army, DoD)Uncertainty/Risk Analysis (Control Theory, Finance, Mathematics)Technology Readiness Levels (NASA, DoD)
Customized Methods Synthesized from Established TechniquesFeasibility/Viability IdentificationRobust Design Simulation (RDS)Technology Identification, Evaluation, Selection (TIES)Joint Probabilistic Decision Making (JPDM)Unified Trade-off Environment (UTE)Inverse Design using Filtered Monte Carlo Simulation
Mathematical techniques have emerged that are suitable to enable the average engineer to speed up the analysis process, do requirements flow-down, and conduct multivariate trade-off analyses.
303030Prof. Dimitri Mavris
Motivation for Physics-based Conceptual Design
Subsonic Transports
Supersonic Aircraft
Personal Air Vehicles
Uninhabited Air Vehicles
Rotorcraft
New Generation of Vehicles can not be modeled accurately in the absence of historical data
Extreme STOL
313131Prof. Dimitri Mavris
Structures
Performance
Propulsion
Aerodynamics
Weights & Sizing
Stability &Control
Manufacturing
Cost& Economics
Safety
Operations
Avionics
Trajectory/Mission
Physics-based Multidisciplinary Design
323232Prof. Dimitri Mavris
Cross Fertilization Example
Number of Measurements
13
125
15
a) One at a time
b) Matrix
c) Central Composite
Experimental Design in Biotechnology
333333Prof. Dimitri Mavris
What is needed for the Paradigm Shift to occur?
• Transition from single-discipline to multi-disciplinary analysis, design and optimization
• Easy to use integrative environments• Automation of the resultant integrated design process• Transition from a reliance on historical data to physics-based formulations,
especially true for unconventional concepts• Means to perform requirements exploration, technology infusion trade-offs
and concept down selections during the early design phases (conceptual design) using physics-based methods
• Methods which will allow us to move from deterministic, serial, single-point designs to dynamic parametric trade environments
• Incorporation of probabilistic methods to quantify, assess risk • Transition from single-objective to multi-objective optimization• Need to speed up computation to allow for the inclusion of variable fidelity
tools so as to improve accuracy• Means to facilitate data and knowledge creation, storage, versioning,
retrieval and mining• An integrated knowledge based engineering and management framework
343434Prof. Dimitri Mavris
Data Integration and Visualization is Needed
• “An ideal environment for analysis would have a seamless integration of computational and visual techniques*”– Maturation of statistical methods and data mining
techniques– Development of visualization that uses these techniques– Synthesis of enabling techniques into a suite of application-
based methods• Extending the reach of analytics requires these three
foundational concepts
*From Illuminating the Path: the R&D Agenda for Visual Analytics, National Visualization
353535Prof. Dimitri Mavris
Key Enablers
• An integrated knowledge based engineering and management framework based on the concept of meta-models or surrogate models
• A multi-attribute Decision Making and Support Environment based on the concept of Visual Analytics that finally allows the Decision Makers to Visualize, understand/comprehend and query their data in real time
• These two enablers combined will provide a means to :- speed up processes, - protect proprietary nature of codes used, overcome organizational barriers (protectionism of tools and data), - allow for the framework to be tool independent (no need for direct integrations of codes), this also enables our desire for variable tool fidelity formulations, - enable the designer to perform requirements exploration, technology infusion trade-offs and concept down selections during the early design phases (conceptual design) using physics-based methods- finally move from a deterministic, serial, single-point design mind frame to a dynamic parametric and interactive trade-off environment
363636Prof. Dimitri Mavris
Introducing the Concept of Visual Analytics• Grand Challenge: How to analyze overwhelming, disparate, dynamic
information• Analytics is the “science of analysis” to discover and understand patterns
– Uses statistical tools and methods– Primary goal is to understand the past to predict the future
• Visual Analytics is “the science of analytical reasoning facilitated by interactive visual interfaces”
– Provides a mechanism for a user to see and understand large volumes of information at once
– The brain can best process information received through visual channels– Facilitates discovery of unexpected trends and highlights transparency of
underlying physical phenomena• Applications include Homeland Security, marketing, design and
optimization, disaster management, and othersVisualization aids decision making on otherwise insurmountable problems
373737Prof. Dimitri Mavris
Creation of Modeling and Simulation Environment
WATEWeight Analysis of
Turbine Engines Code
FLOPSFlight Optimization
Code
NPSSNumerical Propulsion
Simulation
ALCCAAircraft Life CycleCost Analysis Code
Multi-Disciplinary DOEMission
Requirements
MarketRequirements
TechnologySetting
FidelityMultipliersEconomic
Assumptions
Vehicle Size
VehiclePerformance
VehicleEconomics
NOx CO2 NOISE
EmissionsModules
Airframe Fixed Given Engine Architecture
ThrustRequired
ThrustAvailable
A B
Engine Engine ArchitecturesArchitectures
Aircraft NeedsAircraft Needs
404040Prof. Dimitri Mavris
Parametric Interactive Technology Assessments
-40
-10
20
1.7e
-14
-100
0100
-30.
6373
-20
-100
-5.9
e-14
0
100
-6e-
14
-4-202
1.42
e-14
-202
1.42
e-14
-30-1010
9.49
e-14
-101030
8.7e
-13
-400
50100
-9.6
e-13
-0.0
1.0
3
0
Fan_
eff
1.5
1.56
1.5
FPR
-0.2 0
0
Fan_
BD
-0.2 0
0
Fan_
SD
-0.0
1.0
2
0
LPC
_eff
1.3
1.5
1.3
LPC
PR
-0.2 0
0
LPC
_BD
-0.2 0
0
LPC
_SD
1.1
1.3
1.0629
HP
C_T
S
-0.0
1.0
3
0
HP
C_e
ff
20 23
20
HP
CP
R
1.6
1.9
1.6
HP
C_F
SP
R
-0.7
-0.2
0
Com
b_D
3300
3600
3285
T4m
ax
-0.0
1.0
3
0
HP
T_ef
f
0 .2
0
HP
T_Lo
ad
060
0
0
HPT_
1VT
030
00
HPT_
2VT
030
0
0
HPT_
BT
-0.2 0
0
HPT_
BD
-0.7
-0.2
0
HPT_
SD
030
0
0
HPT_
Max
MT
-0.0
1.0
3
0
LPT_
eff
0 .2
0
LPT_
load
030
0
0
LPT_
VT
030
0
0
LPT_
BT
-0.2 0
0
LPT_
BD
-0.2 0
0
LPT_
SD
030
0
0
LPT_
Max
MT
.85
.94
1
FCD
SU
B
120
140
125.2
WSR
.27
.32
0.2703
TWR
.8 1
0.75
Com
b_co
ol
10 40
40.12
UTE
P
-0.1 .1
0
Util
35 Input Variables
Emissions
Engine Performance
NoiseCost
(More) Emissions
NOx
CO2
SFC
T/W
FON
SLN
DOC
HC
Soot
414141Prof. Dimitri Mavris
• A Gap Analysis can be used at the conceptual design stage to:– Determine the minimum technology attributes that
are necessary in order to meet the desired objective– Identify where investment in technology
development will yield the best payoffs– Assess where design tradeoffs need to be made
• By obtaining this information earlier in the design process, Gap Analysis can reduce design costs and time
The Gap Analysis answers the question: What do I need to do today to get to where I
want to be in the future?
Gap Analysis
424242Prof. Dimitri Mavris
Probabilistic Requirements Analysis
• Using surrogate models, the constellations of points can be viewed in multiple dimensions
• The green area indicates the desirable region• Using probabilistic techniques and surrogate
models, 10,000 discrete designs were created and graphically displayed
Zoomed In Version
% NOx Reduction
% C
O2
Red
uctio
n
% Engine Thrust/Weight
% F
uel C
onsu
mpt
ion
Red
uctio
n
Flyo
ver N
oise
Red
uctio
n (d
b)
Sideline Noise Reduction (db)
434343Prof. Dimitri Mavris
Technology Space Constellations
% NOx Reduction % NOx Reduction
% C
O2
Red
uctio
n
% C
O2
Red
uctio
n
% Engine Thrust/Weight
% F
uel C
onsu
mpt
ion
Red
uctio
n
Flyo
ver N
oise
Red
uctio
n (d
b)
Sideline Noise Reduction (db)
Zoomed In Version
• Clicking on any point of interest brings up the associated flowpath diagram
Case 1156
Research Lead: Dr. Michelle Kirby
444444Prof. Dimitri Mavris
Multivariate Analysis: Joint Probability Contours
• Using the joint probability technique, the trends in each dimension can be viewed
• For positive correlations, both variables can be changed in the same direction
• For negative correlations, a compromised design will result
• The blue highlighted point of interest is also shown in each dimension
• The highlighted point can be compared to the pareto optimum
• Where is the “Needle in the haystack?”
Research Lead: Dr. Michelle Kirby
CO2 Reduction
NOx Reduction
% Thrust/ Weight
% TSFC
FlyoverNoise
SidelineNoise
454545Prof. Dimitri Mavris
Three-Dimensional Pareto Frontier (Propulsion)
• Red = Architecture A• Green = Architect. B• Blue = Architecture C• Surrogate model
allows rapid evaluation of SFC and Specific Thrust as a function of several input parameters
• Clicking on any point brings up a flowpath of that engine and links to other information
Notional
464646Prof. Dimitri Mavris
High Dimensionality Analysis Using Multivariate Plot
• Multivariate = Simultaneous view of all x vs. y plots
– “Slices” of the 3-D profiler
• All points linked across hierarchy
• Validation: Multiple trends can be easily confirmed
• Error checking: Failed cases (negative TSFC) can be highlighted and the reason for failure can be graphically identified very quickly
• Traceability: Engine architectures for specific design regimes can be justified using physics- based modeling
1
32
1 23
TSFC
SpecificThrust
Mach
Altitude
TurbineTemp.
EngineType
Turbojet
Turbofan
Ramjet
1
32
1 23
11
3322
11 2233
TSFC
SpecificThrust
Mach
Altitude
TurbineTemp.
EngineType
Turbojet
Turbofan
Ramjet3
1
2
474747Prof. Dimitri Mavris
Filtered Monte Carlo Technique0 20 40 60 80
PlatformsLost
100 400 700
Enemy Range
0.1 0.6 1 1.4Enemy
Dwell Time
0 200 500FriendlyRange
500 1100 1600FriendlySpeed
10 20 30 40Missile-
Wing Area
3 4 5 6Missile-
Aspect Ratio
0.25 0.34 0.42Missile-
Fuel Volume
0.2 0.3 0.4Engine-
Inlet
4 5 6 7Engine-
Pressure Ratio
1000 3000Engine-
Turbine Temp
03060
Targ
ets
Kill
ed
03060
Pla
tform
sLo
st
100400
700
Ene
my
Ran
ge
0.10.71.3
Ene
my
Dw
ell T
ime
0300600
Frie
ndly
Ran
ge
50010001400
Frie
ndly
Spe
ed
10
30
Mis
sile
-W
ing
Are
a
3
5
Mis
sile
-A
spec
t Rat
io
0.250.320.4
Mis
sile
-Fu
el V
olum
e
0.20.30.4
Eng
ine-
Inle
t
4
6
Eng
ine-
Pre
ssur
e R
atio
9746 matching rows
Select Show Include
0 ≤Targets Killed≤100
0 ≤Platforms Lost ≤100
100.28 ≤Enemy Range≤899.97
0.10019 ≤Enemy Dwell Time≤2
-58.257036 ≤Friendly Range≤904.230311
532.322798 ≤Friendly Speed≤1978.78995
Data Filter
484848Prof. Dimitri Mavris
Filtered Monte Carlo Technique
0 20 40 60 80Platforms
Lost
100 400 700
Enemy Range
0.1 0.6 1 1.4Enemy
Dwell Time
0 200 500FriendlyRange
500 1100 1600FriendlySpeed
10 20 30 40Missile-
Wing Area
3 4 5 6Missile-
Aspect Ratio
0.25 0.34 0.42Missile-
Fuel Volume
0.2 0.3 0.4Engine-
Inlet
4 5 6 7Engine-
Pressure Ratio
1000 3000Engine-
Turbine Temp
03060
Targ
ets
Kill
ed
03060
Pla
tform
sLo
st
100400
700
Ene
my
Ran
ge
0.10.71.3
Ene
my
Dw
ell T
ime
0300600
Frie
ndly
Ran
ge
50010001400
Frie
ndly
Spe
ed
10
30
Mis
sile
-W
ing
Are
a
3
5
Mis
sile
-A
spec
t Rat
io
0.250.320.4
Mis
sile
-Fu
el V
olum
e
0.20.30.4
Eng
ine-
Inle
t
4
6
Eng
ine-
Pre
ssur
e R
atio
804 matching rows
Select Show Include
0 =Targets Killed=100
0 =Platforms Lost =10
100.28 =Enemy Range=899.97
0.10019 =Enemy Dwell Time=0.30017
-58.257036=Friendly Range=904.230311
532.322798=Friendly Speed=1978.78995
Data Filter
575757Prof. Dimitri Mavris
T3
off
on
off
T7
off
on
off
T5
off on
off
T8
off
on
off
T10
off
on
off
T4
off
on
off
T11a
off
on
off
T11b
off
on
off
T12
off
on
off
T13
off
on
off
T14
off
on
off
T15a
off
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off
T16
off
on
off
T9
off
on
off
% CO2 /ASM
% Below LTO NOx
% DOC+I
+26.70%
-88.18%-31.50%
-17.35%
0%+5.85%
0%-23.40%
+3.49%%
Cha
nge
From
Bas
elin
e
This is the baseline configuration.
Dynamic Technology Environment
Baseline +T3 +T5 +T7 +T8 +T9 +T10 +T11a/b+T13 +T14 +T15a +T16
0.00%
-31.50%
-30%
-24%
-18%
-12%
-6%
0%
% R
educ
tion
in C
O2
-100%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%
% R
educ
tion
in N
OX
585858Prof. Dimitri Mavris
T3
off
on
on
T7
off
on
off
T5
off on
off
T8
off
on
off
T10
off
on
off
T4
off
on
off
T11a
off
on
off
T11b
off
on
off
T12
off
on
off
T13
off
on
off
T14
off
on
off
T15a
off
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off
T16
off
on
off
T9
off
on
off
% CO2 /ASM
% Below LTO NOx
% DOC+I
+26.70%
-88.18%-82.76%
-17.35%
0%+5.85%
0%-23.40%
+3.49%%
Cha
nge
From
Bas
elin
e
Addition of the TAPS advanced combustor (T3) does not change the flowpath, but results in a significant reduction in the LTO NOx characteristics.
Dynamic Technology Environment
Baseline +T3 +T5 +T7 +T8 +T9 +T10 +T11a/b +T13 +T14 +T15a +T16
0.00%
-82.76%
-30%
-24%
-18%
-12%
-6%
0%
% R
educ
tion
in C
O2
-100%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%
% R
educ
tion
in N
OX
595959Prof. Dimitri Mavris
T3
off
on
on
T7
off
on
off
T5
off on
on
T8
off
on
off
T10
off
on
off
T4
off
on
off
T11a
off
on
off
T11b
off
on
off
T12
off
on
off
T13
off
on
off
T14
off
on
off
T15a
off
on
off
T16
off
on
off
T9
off
on
off
% CO2 /ASM
% Below LTO NOx
% DOC+I
+26.70%
-88.18%-83.54%
-17.35%
-2.99%+5.85%
-3.91%-23.40%
+3.49%%
Cha
nge
From
Bas
elin
e
The highly loaded compressor system (T5) improves the efficiency of the HPC, thus reducing vehicle fuel burn and CO2 production.
Dynamic Technology Environment
Baseline +T3 +T5 +T7 +T8 +T9 +T10 +T11a/b+T13 +T14 +T15a +T16
-3.91%
-83.54%
-30%
-24%
-18%
-12%
-6%
0%%
Red
uctio
n in
CO
2
-100%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%
% R
educ
tion
in N
OX
606060Prof. Dimitri Mavris
T3
off
on
on
T7
off
on
on
T5
off on
on
T8
off
on
on
T10
off
on
on
T4
off
on
off
T11a
off
on
on
T11b
off
on
on
T12
off
on
off
T13
off
on
on
T14
off
on
on
T15a
off
on
off
T16
off
on
off
T9
off
on
on
% CO2 /ASM
% Below LTO NOx
% DOC+I
+26.70%
-88.18%-78.73%
-17.35%
-15.35%+5.85%
-21.56%-23.40%
+3.49%%
Cha
nge
From
Bas
elin
e
The turbine tip clearance control (T14) influences the adiabatic efficiency, which improves fuel burn and reduces the CO2 /ASM.
Dynamic Technology Environment
Baseline +T3 +T5 +T7 +T8 +T9 +T10 +T11a/b+T13 +T14 +T15a +T16
-21.55%-78.73%
-30%
-24%
-18%
-12%
-6%
0%%
Red
uctio
n in
CO
2
-100%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%
% R
educ
tion
in N
OX
616161Prof. Dimitri Mavris
T3
off
on
on
T7
off
on
on
T5
off on
on
T8
off
on
on
T10
off
on
on
T4
off
on
off
T11a
off
on
on
T11b
off
on
on
T12
off
on
off
T13
off
on
on
T14
off
on
on
T15a
off
on
on
T16
off
on
on
T9
off
on
on
% CO2 /ASM
% Below LTO NOx
% DOC+I
+26.70%
-88.18%-78.91%
-17.35%
-16.45%+5.85%
-22.27%-23.40%
+3.49%%
Cha
nge
From
Bas
elin
e
The aspirated seal for the turbine (T16) influences the adiabatic efficiency, which reduces the fuel burn, decreasing the amount of CO2 /ASM produced.
Dynamic Technology Environment
Baseline +T3 +T5 +T7 +T8 +T9 +T10 +T11a/b+T13 +T14 +T15a +T16
-22.27%-78.91%
-30%
-24%
-18%
-12%
-6%
0%
% R
educ
tion
in C
O2
-100%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%
% R
educ
tion
in N
OX
626262Prof. Dimitri Mavris
The Collaborative Visualization Environment (CoVE) A Means to Enable Visual Analytics
646464Prof. Dimitri Mavris
Concluding Remarks
In Summary Elements Needed for the Successful Transition of the Next Generation Aerospace Vehicles:
– Advocacy for the proposed concepts and their associated design approaches. Support to overcome organizational barriers
– Fundamentally sound systems engineering processes tailored to new systems and system of systems formulations
– Practical methods and processes that can handle “Extrapolations” beyond the realm of the historical databases. A physics based approach to design.
– Exploitation of innovative technological breakthroughs
– System level modeling and simulation to assess the technological gaps and to provide guidance, goals, i t t