Computational Methods for Design Motivating Applications and Introduction to Modeling John A. Burns...
-
Upload
bennett-patterson -
Category
Documents
-
view
216 -
download
0
Transcript of Computational Methods for Design Motivating Applications and Introduction to Modeling John A. Burns...
Computational Methods for Design
Motivating Applicationsand Introduction to Modeling
John A. BurnsCenter for Optimal Design And Control
Interdisciplinary Center for Applied MathematicsVirginia Polytechnic Institute and State University
Blacksburg, Virginia 24061-0531
A Short Course in Applied Mathematics
2 February 2004 – 7 February 2004
N∞M∞T Series Two Course
Canisius College, Buffalo, NY
Who, What and Why
? WHO MIGHT BE INTERESTED ? STUDENTS IN MATH, ENGINEERING and SCIENCES
? WHAT WILL I TALK ABOUT ? HOW DIFFERENTIAL EQUATIONS ARISE AS
FUNDAMENTAL MODELS IN ALL BRANCHES OF MODERN SCIENCE AND ENGINEERING - MODELING
A SHORT REVIEW/SUMMARY OF THE “BASIC” MATHEMATICS REQUIRED TO UNDERSTAND THE PROBLEMS
A COLLECTION OF CURRENT REAL WORLD APPLICATIONS WHERE NEW MATHEMATICS HAD TO BE DEVELOPED IN ORDER TO SOLVE THESE PROBLEMS
AN INTRODUCTION TO NUMERICAL METHODS NEEDED FOR OPTIMAL DESIGN AND CONTROL OF PHYSICAL AND BIOLOGICAL SYSTEMS
INTRODUCE THE CONTINUOUS SENSITIVITY EQUATION METHODS
Who, What and Why
? WHY DO THIS ? FOR THE STUDENT…
IT IS FUN (AT LEAST IT CAN BE FUN) TO SEE WHY MATHEMATICS IS SO IMPORTANT …
MATHEMATICS IS THE ENABLING SCIENCEFOR MOST OF THE GREAT BREAKTHROUGHS IN
MODERN SCIENCE AND TECHNOLOGY
FOR ME … IT IS FUN (AT LEAST IT CAN BE FUN) I CAN TALK ABOUT THE RESEARCH PROJECTS AT ICAM I CAN TRY TO EXPLAIN WHY …
I HAVE THE BEST JOB IN THE WORLD
Joint Effort Virginia Tech
J. Borggaard, J. Burns, E. Cliff, T. Herdman,T. Iliescu, D. Inman, B. King, E. Sachs
J. Singler, E. Vugrin Texas Tech
D. Gilliam, V. Shubov George Mason University
L. ZietsmanOTHERS ...
D. Rubio (U. Buenos Aires)J. Myatt (AFRL)A. Godfrey (AeroSoft, Inc.)M. Eppard (Aerosoft, Inc.)K. Belvin (NASA) ….
FUNDING FROMAFOSR
DARPA
NASA
FBI
Course Outline Lecture 1 - High Level Description of
Applications Lecture 2 – Some “Simple” Applications Lecture 3 – Elementary Differential Equations Lecture 4 – Introduction to Sensitivities Lecture 5 - Design and Optimization Problems
IF ENOUGH TIME … Modeling and Control of the Growth of Cancer Cells Problems Involving Bioterrorism
General LectureFrom Nano-Technology to Large Space Structures or
How Mathematical Research is Becoming the Enabling Science From the Ultra Small to the Ultra Large
Today’s Topics
Design of Wind Tunnel Test Facilities System Biology: Epidemics and Populations Design and Optimization of Ink Jet Printers Manufacturing Thin Films: Nano-Technology Design of Scram Jets Design and Control of VERY Large Space
Structures
Thing to Remember
A GOOD THEORY CAN LEADTO GREAT ALGORITHMS
MATHEMATICS IS OFTEN THE ENABLING SCIENCE
BIG TECHNOLOGICAL ADVANCES HAVE COME BECAUSE WE HAVE
GENERATEDNEW MATHEMATICS Differentiation of functions with respect to shapes
Integration of set-valued functions Control of infinite dimensional systems …
FIRST APPLICATION
AERODYNAMIC DESIGN
Free-Jet Test Concept
WIND TUNNEL
Design of Wind Tunnel Facility
This problem is based on a research effort that started with a joint project between the Air Force's Arnold Engineering Design Center (AEDC) and ICAM at Virginia Tech. The goal of the initial project was to help develop a practical computational algorithm for designing test facilities needed in the free-jet test program. At the start of the project, the main bottleneck was the time required to compute cost function gradients used in an optimization loop. Researchers at ICAM attacked this problem by using the appropriate variational equations to guide the development of efficient computational algorithms this initial idea has since been refined and has now evolved into a practical methodology known as the Sensitivity Equation Method (SEM) for optimal design.
Design of Wind Tunnel Facility
For the example here we discuss a 2D version of the problem. The green sheet represents a cut through the engine reference plane and leads to the following problem.
Real forebody test shapes have been determined by expensive cut-and-try methods.
Goal is to use computational - optimization tools to automate this process
Design of Optimal Forebody
INFLOWOUTFLOW
TEST CELL WALL
CENTERLINE FOREBODY
S
DATA GENERATED AT Mach # = 2.0 AND LONG FOREBODY
INFLOWOUTFLOW
TEST CELL WALL
CENTERLINE
SHORT FOREBODY
S
FOREBODY RESTRICTED TO 1/2 LENGTHMATCH
Long and Short Forebody
direction- yin momentum - energy, - direction-x in momentum - density -
)y,x(n)y,x(E)y,x(m,)y,x(
LONG FOREBODY
SHORTFOREBODY
Design of Optimal Test Forebody
Data Optimal DesignInitial Design
direction- yin momentum - energy, - direction-x in momentum - density -
)y,x(n)y,x(E)y,x(m,)y,x(
Momentum in x-direction - m(x,y)
Design of Optimal Test Forebody
DEVELOPED A NEW MATHEMATICAL METHOD
“CONTINUOUS SENSITIVITY EQUATION METHOD”
HOW WELL DID WE DO ???
HOW DID WE DO IT?
Design of Optimal Test Forebody
OPTIMIZATION LOOPS (TRUST REGION METHOD)
INITIAL ITR # 1 ITR # 5ITR # 2 ITR # 12
THE “SENSITIVITY EQUATION METHOD” WAS100 TIMES FASTER
THAN PREVIOUS “STATE OF THE ART” METHODS
NEXT APPLICATION
SYSTEM BIOLOGY/EPIDEMICS
Epidemic Models
Susceptible Infected
Removed ASSUME A WELL MIXEDUNIFORM POPULATION
Epidemic Models SIR Models (Kermak – McKendrick, 1927)
Susceptible – Infected – Recovered/Removed
( ) ( ) ( )d
S t S t I tdt
( ) ( ) ( ) ( )d
I t S t I t I tdt
( ) ( )d
R t I tdt
( ) ( ) ( ) constantS t I t R t N
Epidemic Models (SARS) SEIJR: Susceptibles – Exposed - Infected - Removed
)()()(
)()()(
222
111
tPtSrtSdt
d
tPtSrtSdt
d
)()()(
)()()()(
)()()()(
21
2
1
tJtItRdt
d
tJtItJdt
d
tItkEtIdt
d
)()()()()()( 2211 tkEtPtSrtPtSrtEdt
d
)(/))()()(()( tNtlJtqEtItP
Model of SARS Outbreak in Canada
byChowell, Fenimore, Castillo-Garsow & Castillo-Chavez (J. Theo. Bio.)
MORE ACCURATE – MORE COMPLEX – MORE DIFFICULT
Other Problems Cancer
Cell Growth Vascularization Capillary Formulation
– Reaction diffusion– Moving boundary problems
Heart Models Nerve Membranes Blood flows
– FitzHugh-Nagumo– Navier-Stokes
Enzyme Kinetics Biochemistry Cell Growth
– Michaelis-Menton– Extensions …
J. D. Murray,Mathematical Biology: I and II,Springer, 2002 (2003).
Reference
FAR OUT PROBLEMS
TRANSIMS - EpiSIMSC. Barrett - Los Alamos R. Laubenbacher - VBI
Ω(t)
10 years for transportation model Clearly a “fake” cloud …
Dynamic Pathogen & Migration
MODELS? ID? SENSITIVITY? COMPUTATIONAL TOOLS?WHAT ARE THE (SOME) PROBLEMS?
“SEIJR” Model: Improved
DIFFUSION CONVECTION
HIGHLYCOMPLEX
NEXT APPLICATION
DESIGN OF PRINTERS
Design of Ink Jet Printers
Tektronix Graphics, Printing & Imaging Division (FUNDING - NSF)
Design of Ink Jet Printers
ADJUST THE ACTUATOR
SENSORCONTROL
NEXT APPLICATION
NANO-TECHNOLOGY
Control of Thin Film Growth
Ei = .1 eV Ei = 5.0 eV
“VARIABLE ENERGY ION SOURCE”
OR
Control of Thin Film Growth
Optimized ion beam processing through Modulated Energy Deposition • Low energy for initial monolayers
• Moderate energy for intermediate layers
• High energy to flatten film surface
Successful proof-of-concept experiments using Modulated Energy Deposition approach (Honeywell)Successful proof-of-concept experiments using Modulated Energy Deposition approach (Honeywell)
Cambridge Hydrodynamics, SC Solutions, U. Colorado, Oak Ridge National Lab
Atomistic Model-Based Design of GMR Processes. Virginia(PI: H. Wadley)
Control of Thin Film Growth
MD SIMULATION
MD SIMULATION
Control of Thin Film Growth
h(t,x,y )q =
d
:
Sensitivity of h(t,x,y,,,,, d ) to - h(t,x,y,,,,, d )
? Model: The Equations ?
),y,x,t(F)y,x,t(hD
)y,x,t(h))y,x,t(h()y,x,t(ht
4
22
]1[
Phenomenological models (Ortiz, Repetteo, Si, Zangwill, … 1990s)
q
Molecular Dynamic Models (Alder, Wainwright, … 1950s)
),p,,),t(r,t(f)t(rm
)(rf),,p,,,rr(u)(r
r...,r,rr
NN
dtd
Nri
jij,i
jiN
TN
N
i
2
2
21
UU
Position of N - atoms
q
q
N 10 9 ORDINARY
DIFF EQUATIONS
Models (Ortiz, Repetteo, Si)Raistrick, I. And Hawley, M., Scanning Tunneling and Atomic Force Microscope Studiesof Thin Sputtered Films of YBa2Cu3O7 , Interfaces in High Tc Superconducting Systems, Shinde, S. L. and Rudman, D. A. (eds.), 1993, 28-70.
Control of Thin Film Growth
Phenomenological models (Ortiz, Repetteo, Si, Zangwill, … 1990s)
)l/)y,x,t(h(fV
),y,x,t(F)y,x,t(hD
)y,x,t(h))y,x,t(h()y,x,t(ht
]1[4
22
u
ze)z()z(f 1Transition Function
• Predicts negative film growth• Parameter identification impossible• Not even necessary in YBCO films!
p)q/z(e)z,q,p(f • Removes negative film growth• Parameters can be tuned• Include more deposition processes
Generalized Transition Function (Stein, VA TECH)
Need “Reasonable” Model
ze)z()z(f 1
Negative Film Height !
Mean Film Height
Mean Film Height
p)q/z(e)z,q,p(f
Mean Height
For YBCO FilmNO TRANSITION FUNCTION
0f
Parameterized Models
• General transition function provides flexibility• However, need to include deposition energy + ...
p)q/z(e)z,q,p(f
MORE ACCURATE – MORE COMPLEX – MORE DIFFICULT
NEXT APPLICATION
JET ENGINES
Design of Injection Scram Jets
q1U
q2U
j
q3j
Design/Control Variables
Slip LIne
Air
H2
U
UJ
j
H2
Design of Injection Scram Jets
Objective: DETERMINE BEST ANGLE
Free-stream & Design Variables Free-stream: N2 / O2 mixture
M = 3, T = 800 K Injectant: H2
M = 1.7, T = 291 K Momentum ratio = 1.7
Slip LIne
Air
H2
Virginia TechGene Cliff
&AeroSoft, Inc.
Andy GodfreyMark Eppard
q1U
q2U
j
q3j
Design/Control Variables
U
UJ
j
SHAPE
NEXT APPLICATION
LARGE SPACE STRUCTURES
Control of Large Space Structures
NIA
Active ShapeAnd Vibration
Control
SkilledR&D
Workforce
Inflatable/RigidizableAnd Assembled
Structures
VT- ICAM Modeling
VT- ICAMNASA LaRC
FUNDING FROM DARPA and NASA
Control of Large Space Structures
Solar Array Flight experiment had unexpected thermal deformation
Early satellites lost because of thermal instabilities
Hubble had large thermal excitations (later fixed)
All of these where not modeled and hence unpredicted
Photos courtesy of W. K. Belvin, NASA Langley
shadesunlight
AVOID THESE PROBLEMS IN FUTURE SPACE STRUCTURES
NEW APPLICATIONS REQUIRE STRUCTURES > 100 m2
Inflatable Assembled Structures
UV Curing Thermosets Thermoplastics Elastic Memory Stem Aluminum
Temperature, ºC
Psi, Pa
Inflatable/RigidizableAnd Assembled
Structures
Inflatable Truss Structures
Deploy and assemble into large structures
New Mathematical Theory
SENSOR
(MFCTM)Flexible Actuators
2
2
2 2 3( , ) [ ( , ) ( , )] ( ) ( )
2 2 2 y t x EI y t x y t x b x u t
t x x x t
INFINITE DIMENSIONAL OPTIMAL CONTROL THEORY IMPLIES
2''( ) EI ( ) ( , ) ( ) ( , )1 220 0
L Loptu t k x y t x dx k x y t x dxtx
VERY PRACTICAL INFORMATION
New Mathematical Models
2
2 2
02 2 3( , ) [ ( , ) ( ) ( , ) ]
2 2y t x EI y t x s y t s x ds
t x x x t
Including Thermal Effects Changes Everything
02
2 3( , ) ( , ) ( , ) ( , )
2t x t x y t x f t x
t x x t
( , )x t x ADD THERMAL
EQUATIONS ( ) ( )b x u t
MORE ACCURATE – MORE COMPLEX – MORE DIFFICULT
Remarks
COMPUTATIONAL MATHEMATICS, SCIENCE AND ENGINEERING WILL BE THE KEY TO FUTURE BREAKTHROUGHS
CMS&E MUST BE DONE RIGHT
LOTS OF APPLICATIONS OPPORTUNITIES FOR MATHEMATICS TO
LEAD THE WAY TO NEW SOLUTIONS = JOB SECURITY FOR APPLIED MATHEMATICIANS NEW MODELS NEED TO BE DEVELOPED
PHYSICS, CHEMISTRY, BIOLOGY … FLUID DYNAMICS, STRUCTURAL DYNAMICS … …