Transcript Multi-disciplinary Design Optimization

Aerospace Education & Research in the
Area of Design
Diamond Jubilee Lectures 2003-04
Department of Aerospace Engineering
Indian Institute of Science, Bangalore
K. Sudhakar
Centre for Aerospace Systems Design & Engineering
Indian Institute of Technology
Mumbai 400 076
October 30, 2003
Year 1997
“Technology Perspective – The Next Decade”
Aeronautics Research & Development Board
AR&DB/TC/001, May 1997
Suggested centres of excellence to be
supported by AR&DB are;
– CFD
– Advanced Composites
– Systems Design & Engineering ? ?
Years 1990 -1997
• Aerospace Design as a discipline at IITB
– Specialization dropped
– Courses had tapered off
– Design, Build Or Open ended problems shunned
– No research interest among faculty
• March 1998 : AR&DB Sanction arrives
CASDE : July 1998
Mission
CASDE shall strive to
develop and retain strong links with
Indian Aerospace Industry
and shall engage in
R&D activities with worldwide visibility
http://www.casde.iitb.ac.in/
Objectives of CASDE
• M. Tech Programme in Systems Design &
Engineering
• Modeling & Simulation Laboratory
• System Design Methodologies
• Awareness Creation
M. Tech in Systems Design & Engineering
• What do others teach?
• What can draw / retain student interest?
• What will faculty want to teach?
• What will industry want?
What all should be taught?
Form a composite team •
Brain-storming session •
http://www.casde.iitb.ac.in/History/PastEvents/lonavla/odw-see.pdf
M. Tech in Systems Design & Engineering
• Courses of study
– System Modeling & Simulation$
– Optimization for Engineering Design$
– Systems Engineering Principles$
– Statistical Methods for Analysis & Design
– Multi-disciplinary Design Optimization# (MDO)
– Applied Mechatronics$ (hands on course)
• System Level Studies – RC Model Aircraft
$ Also available as short courses
# Coordinates a Special Interest Group on MDO (SIG-MDO)
http://www.casde.iitb.ac.in/edu/batch_2003/curriculum.htm
Laboratory and Other Infrastructure
• Wind tunnel balance
• Propulsion system test facilities
• IM&S Laboratory 
http://www.casde.iitb.ac.in/IMSL/
– COTS sensors, actuators, . . . .
– R/C Model construction facilities, training
– Data acquisition cards
– Software
254
Torque sensor setup
580
Propeller test facility
50 gm force.
12
Torque
sensor
55
All dimensions
are in mm
IM&S Laboratory
http://www.casde.iitb.ac.in/IMSL/resources.html
Launch Vehicle Simulator from VSSC
http://www.casde.iitb.ac.in/IMSL/vssc.html
Applied Mechatronics
Hands on course
• 2 hrs lecture + 3 hrs lab per week
• 2 projects
http://www.casde.iitb.ac.in/Mechatronics/
Student Projects
2001
2000
Instrumented. 2.5 kg, 1.6 m.
Videography. 0.9 kg, 0.6 m.
2002
Appreciated
Solar. 0.13 kg, 0.25 m.
http://www.casde.iitb.ac.in/IMSL/student-projects.html
ME Dual Degree Project : HILS
• Flight Dynamics & sensor models
• RTLinux + Comedi
• Real time simulation
• Choose WP NGC
• On-board Computer?
• Use hobby grade actuators
• Out of window display
@16actuators
MHz
468332
RC
servo
Overflying
Mumbai
RAM 1 MB, FLASH 256 kB
Aileron, elevator,
throttle
Autonomous
Flightrudder,
: 4 Way
Points
8 x 12 bit ADC @ 100 kHz
15 PWM / 25 DIO
30 gm; 50 x 75 x 12 mm
http://www.casde.iitb.ac.in/Publications/pdfdoc/vishisht-DDP-2003.pdf
Flapping Wing Flight
Dual-Degree Project in Aero: Flapping wing
• Unsteady aerodynamics for prescribed motion
• Aero elastic analysis for prescribed actuation
• Wing construction - Polyurethane foam. (IDC)
• Actuation mechanism for testing. (Robotics)
B. Tech Project in Robotics
(Robotics group)
• Mechanism design
• Kinematics prescribed
• Loads prescribed
Awareness Creation - 2003
• January - CEP in Applied Mechatronics
• February - 3rd Meeting of SIG-MDO
• April - Workshop on MDO @DRDL
• August - Brainstorming on System Analysis
• September - Int. Conf. MSO-DMES
We also!
• Traveling Course on Design, Build and Fly. (CASDE+ADA)
Student Projects as Case Studies.
http://www.casde.iitb.ac.in/IMSL/des-bld-fly.html
– Naval Institute of Aeronautical Technology, Cochin.
– Dept. of Aerospace Engineering, MIT, Chennai
– Dept. of Aerospace Engineering, Parks College of Eng.,Coimbatore
• AeSI Wright Flyer Design Competition. (CASDE+ADA)
http://www.casde.iitb.ac.in/we-also/des-comp/
We also!
• AeSI Schools Outreach Programme. (CASDE+ADA)
30 events, 103 Schools, 5,600 students
(Good part of events by CASDE)
http://www.casde.iitb.ac.in/we-also/school-outreach/
Arya explaining the intricacies of flight
mechanics
http://www.casde.iitb.ac.in/MDO/
Systems Engineering Process
Level-3
Analysis
Level-2
Analysis
Context
Level-1
Analysis
Super
System
Solution from
lower level
Requirements to
lower level
Focus
of CASDE
System
Sub
System
•
Level – 1 : Good understanding of system; knowledge base, heuristic;
Computationally less expensive;
Usually not available for new systems.
• Level – 3 : Physics based modeling; computationally intensive,
applicable to new systems (V&V?)
CASDE Activities
Research activity
Level-3
Analysis
Level-2
Analysis
Level-1
Analysis
– High fidelity models in design loop ( CFD, . .)
– Multi-Disciplinary Analysis (MDA) leading to
Multi-disciplinary Design Optimization (MDO)
Challenges!
• Human / Admin
– People coming together (Design, A/D, Str, Prop,
Controls)
– Synchronizing funds (taken care of by ARDB)
• Technical
– Non-availability of disciplinary codes
– Neither here nor there!
MDO Elements
• Architectures
• Sensitivity Analysis
• Surrogate Modeling
• Variable Complexity
MDO Elements
• Architectures
Optimizer
• Sensitivity Analysis
• Surrogate Modeling
System Analysis
• Variable Complexity
How are the couplings
handled?
MDO Elements
System Analysis
• Architectures
• Sensitivity Analysis
• Surrogate Modeling
• Variable Complexity
F
X
How are the couplings
handled?
• dF/dX? f/X, f/Y . . .  dF/dX
• How to evaluate f/x?
~ Finite difference
~ Continous/Discrete Adjoint
~ Automatic Differentiation
User Supplied Gradients
Complex Analysis
Code in Fortran
Manually extract
sequence of
mathematical
operations
Manually differentiate
mathematical
functions - chain rule
FORTRAN
source code
that can evaluate
gradients
Code the complex
derivative evaluator
in Fortran
User Symbolic Maths
Complex Analysis
Code in FORTARN
Manually extract
sequence of
mathematical
operations
Use symbolic math
packages to automate
derivative evaluation
FORTRAN
source code
that can evaluate
gradients
Code the complex
derivative evaluator
in Fortran
Automatic Extraction of Formulae
Complex Analysis
Code in FORTARN
Parse and
extract the sequence
of mathematical
operations
Use symbolic math
packages to automate
derivative evaluation
FORTRAN
source code
that can evaluate
gradients
Code the complex
derivative evaluator
in Fortran
Gradients by ADIFOR
Complex Analysis
Code in FORTARN
Automated
Differentiation
Package
FORTRAN
source code
that can evaluate
gradients
ADIFOR Ver 2.0
• First applied to VLM
• Recently to 3-D Euler
x
– Multiblock, Structured grid
Code
ADIFOR
– Central difference, FVM
– JST scheme of artificial dissipation.
x
– Multistage Runge-Kutta schemes.
ADIFORed
Code
– Implicit residual smoothing and local time stepping
No of lines
Exec. time (min)
f
Original Code
ADIFORed Code
4,090
11,889
6.58
28.25
f, df/dx
ADIFOR Ver 2.0
• 3D Euler
• ONERA M6 Wing


3D Euler

ADIFORed
3D Euler
(L/D)
(L/D), d(L/D)/d
Error in Finite Difference Estimate of d(L/D)/d
=0.2
=0.02
=0.002
=0.0002
3.06
38.10
4.44
7.08
77.25
4.11
2.46
1.73
1.56
15.09
MDO Elements
• Architectures
• Sensitivity Analysis
• Surrogate Modeling
• Variable Complexity
• Response Surfaces?
• Design of Experiments
• Design & Analysis of
Computer Experiments
• Designs?
MDO Elements
• Architectures
• Sensitivity Analysis
• Surrogate Modeling
• Variable Complexity
Mix high & low fidelity methods
• VLM & Euler
• thumb rules + analysis
Design / MDO Studies
– WingOpt
– 3D Duct
– Hypersonic Vehicle
Design under uncertainty +
MDO of Transport Aircraft Wing
Optimizer
FSQP
I
N
T
E
R
F
A
C
E
History
Block
Aerodynamics
(VLM)
Input
Processor
Aeroelasticity
Iterator
NASTRAN
Interface
Output
Processor
Structures
MSC/
NASTRAN
Analysis Block
3-D Duct Design
• Pressure Recovery?
• Distortion?
• Swirl?
Entry
Exit
Location and shape known
Geometry of duct from Entry to Exit ?
3D-Duct Design Using
High Fidelity Analysis

X2-MAX


X2-MIN

X1-MIN
X1-MAX
Low Fidelity Design Criteria
- Wall angle < 6°
- Diffusion angle < 3°
- 6 * REQ < ROC
Fluent for CFD
RSM / DOE
DACE
Parameterization of HSTDV Body
 n-pl
r2
z
a
x
r1
h1
h2
h3
b
 wcant
 noz
l cowl
h intake
l
1
l 2
l3
rnoz
l mid
Design variables
XD: {1, 2, 3 , n_plan , wc , wfac_pl, tfac_pl,, Hcruise }
l
noz
t
Hypersonic Vehicle – Discipline Interactions
Input variable
1
2
Analysis Model
Ext. Compression
Model : AM1
Output
Y1
Y1: l1, l2, l3, h1, h2, h3
Y2: ma , MI, , pst
Ext. Configuration
Model : AM2
Y3: (X,Y,Z)
Y4: TOGW , C.G., Vol, Fuel mass
n_pl
Aero Model : AM3
Y5: CN, Cm, CA
w_c
Trim Model :
AM4
Y6: TOGW_up, T , T , D
3
SW
ST
Hcr
n_pl, SW
1…, Hcr
Thrust Model :
AM5
Y7: Th_deliv, Lp, Mp
Performance Model : AM6
Variables not shared
Shared variables
Y8: Cruise Range
Y1… Response from AM1
required as input in AM2
MDO-Framework
GUI
Optimizer
Manager
OPT1
Configuration
Server
OPT2
OPT3
MDO
Controller
Data
Server
Database
Execution
Manager
Control
Data
AM1
Name
Server
AM2
Analysis
Manager
AM3
http://www.casde.iitb.ac.in/MDO/
Publications
2000
2001
2002
2003
Journal
0
1
3+2
1+1
International
Conferences
0
1
6
5
Core faculty = 4
Analysis for Design
Optimization
Design
Optimizer
Optimizer
z
f, h, g
Analysis
z
f, h, g
Interface
z
p
Analysis
R(z,p)=0
z = design variables
f = objective
h = equality constr.
g = inequality constr
z = design variables
f = objective
h = equality constr.
g = inequality constr.
R = residue
MDO
Optimizer
z
f, h, g
Interface
z
Analysis-1
R1(z,p1)=0
p
Y12
Y21
Analysis-2
R2(z,p2)=0
z = design variables
f = objective
h = equality constr.
g = inequality constr.
R = residue
MDO-Architectures
MDO
Optimizer
z
z
Analysis-1
R1(z,p1)=0
Y12
f, h, g
Interface
p
Y12
Analysis-2
R2(z,p2)=0
Y21
z = design variables
f = objective
h = equality constr.
g = inequality constr.
R = residue
p
z
Analysis-1
R1(z,p1)=0
Analysis-2
R2(z,p2)=0
Y21
MDO-Architectures
p
z
Y12
Analysis 
Analysis-1
R1(z,p1)=0
Analysis-2
R2(z,p2)=0
Y21
z
p
Y12, Y21
1 = Y12 - Y12*
2 = Y21 - Y21*
Y12*
Analysis-1
R1(z,p1)=0
Analysis-2
R2(z,p2)=0
Y21*
 Evaluator
MDO-Architectures
MDO
Optimizer
z
f, h, g
Interface
z
Analysis-1
R1(z,p1)=0
p
Y12
Analysis-2
R2(z,p2)=0
MDO
Optimizer
z, y12,
y21
f, h, g
1, 2
Interface
z, y12,
y21
Analysis-1
R1(z,p1)=0
p
1, 2
Analysis-2
R2(z,p2)=0
Y21
z = design variables
f = objective
h = equality constr.
g = inequality constr.
R = residue
z = design variables
f = objective
h = equality constr.
g = inequality constr.
R = residue
3D-Duct Design Using
High Fidelity Analysis
X2-MAX



X2-MIN
X1-MIN

X1-MAX
Low Fidelity Design Criteria
- Wall angle < 6°
- Diffusion angle < 3°
- 6 * REQ < ROC
Fluent for CFD
RSM / DOE
DACE
3D-Duct Design Using
High Fidelity Analysis

X2-MAX


X2-MIN

X1-MIN
X1-MAX
Low Fidelity Design Criteria
- Wall angle < 6°
- Diffusion angle < 3°
- 6 * REQ < ROC
Fluent for CFD
RSM / DOE
DACE
http://www.casde.iitb.ac.in/MDO/3d-duct/
MDA - System Analysis
A-1
Parameters
A-2
A-3
A-4
A-5
• Performance
• -ilities?
• life cycle?
• Cost, etc.?
Design & Analysis of Computer
Experiments
• Regression fit + Stochastic process
• Single global fit
• Variability in prediction known and exploitable
x
x
x
x
Computer exp
DACE Fit
x
x
Estimates of
Predictive error
Building Models Using DACE
x
x
x
x
x
x x
x x = Computer exp
DACE Fit
5% predictive
error
Use multi-modal GA to identify ‘n’ highest peaks.
Test if they are higher than 5%
Add computer experiments at those spots
Design - Publications
Journal of Aircraft
Volume 40 Number 4
July 2003
• Total number of papers
- 22
• Number of papers addressing design - 4