1. Assist. Prof. Dr.-Ing. Mostafa Ranjbar
Multidisciplinary Engineering Design Optimization (MCE 540 Graduate Course – Mechanical Engineering Department)
Instructor:
Assist. Prof. Dr.-Ing. Mostafa Ranjbar
Ph.D. (Dr-Ing.), Multidisciplinary Engineering Design Optimization of Structures,Technische Universität Dresden, Germany, 2011
M.Sc., Vibration Monitoring and Fault Diagnosis of Structures, Tarbiat Modares University, Tehran, Iran, 2000
B.Sc., Mechanical Engineering, Shiraz university, Iran, 1998

2. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________AN INTRODUCTION
LECTURE #1

3. Today’s Lecture Introduction to Optimization
Modern Meta-Heuristic Optimization
Design of Experiments, RSMs & Meta-Modeling
Robust Design
Tools
Applications
Recent Developments & Challenges
Future
Q&A

4. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ INTRODUCTION TO SYSTEM
LECTURE #1

5. “All modern products are designed as a SYSTEM”
The World Around Us
“All modern products are designed as a SYSTEM” 5

6. The World Around Us AIRCRAFT SPACECRAFT AUTOMOBILES BUILDINGS
Aerodynamics
Astrodynamics
Engines
Structure & Seismology
Propulsion
Structures
Body/chassis
Space and Aesthetics
Communications
Aerodynamics/Wind
Controls
Payload & Sensor
Electronics
HVAC
Avionics/Software
Optics
Hydraulics
Networking
Manufacturing
Guidance & Control
Industrial design
Fire & Safety
Others

7. The World Around Us AIRCRAFT SPACECRAFT AUTOMOBILES BUILDINGS
Aerodynamics
Astrodynamics
Engines
Structure & Seismology
Propulsion
Structures
Body/chassis
Space and Aesthetics
Communications
Aerodynamics/Wind
Controls
Payload & Sensor
Electronics
HVAC
Avionics/Software
Optics
Hydraulics
Networking
Manufacturing
Guidance & Control
Industrial design
Fire & Safety
Others

8. The World Within Us
SYSTEMS?

9. More Examples of Systems

10. More Examples of Systems

11. More Examples of Systems

12. More Examples of Systems
Level Specific Name
System Launch vehicle
Subsystem Propulsion
Element SRM
Component Ignition Device
Part Igniter

13. More Examples of Systems
COMPARTMENTALIZATION
Helicopter as an example of a Multidisciplinary Complex System
“Helicopters don’t fly.
They beat the air into submission.”
Dr. Ed Smith

14. MODELING Meta Model Model Physical system RS Model The Modeling Spacek
f(t)
input + -
x(t)
output
Model
Physical system
World

15. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ OPTIMIZATION
LECTURE #1

16. WHAT IS OPTIMIZATION? “Making things better” “Generating more profit”
“Determining the best”
“Do more with less”

17. WHAT IS OPTIMIZATION?
“The determination of values for design variables which minimize (maximize) the objective, while satisfying all constraints”
Principles of Optimal Design: Modeling and Computation
2d Ed. by Panos Y. Papalambros and Douglass J. Wilde, Cambridge University Press, New York, 1988, 2000.

18. OPTIMIZATION Design Space: The space of working (Hill in this case)
Objective: Find the Highest Point.
Design Variables: Longitude and latitude.
Constraints: Stay inside the fences.

19. OPTIMIZATION

20. OPTIMIZATION
Objective Function
Constraints
Bounds
Design Variables

21. SOLVING OPTIMIZATION PROBLEMS
Optimization problems are typically solved using an iterative algorithm:
Responses
Derivatives of responses
(design sensitivities)
Model
Constants
Design variables
Optimizer

22. LOCAL AND GLOBAL OPTIMA
LOCAL OPTIMA
maxima
Local maxima
Local minima
minima
GLOBAL MINIMA

23. Optimization Problems

24. Optimization Problems

25. HISTORICAL PERSPECTIVE
Lagrange (1750): constrained minimization
Cauchy (1847): steepest descent
Dantzig (1947): Simplex method (LP)
Kuhn, Tucker (1951): optimality conditions
Karmakar (1984): interior point method (LP)
Bendsoe, Kikuchi (1988): topology optimization
I just guessed the years, I can’t find them!
Richard Bellman did his PhD in 3 months!

26. Lagrange
Joseph-Louis Lagrange (25 January 1736 – 10 April 1813), born Giuseppe Lodovico (Luigi) Lagrangia, was a mathematician and astronomer
Significant contributions to all fields of analysis, to number theory, and to classical and celestial mechanics.
On the recommendation of Euler and d'Alembert, in 1766 Lagrange succeeded Euler as the director of mathematics at the Prussian Academy of Sciences in Berlin,
Lagrange's treatise on analytical mechanics (Mécanique Analytique, 4. ed., 2 vols. Paris: Gauthier-Villars et fils, ), written in Berlin and first published in 1788, offered the most comprehensive treatment of classical mechanics since Newton and formed a basis for the development of mathematical physics in the nineteenth century.
The method of Lagrange multipliers gives a set of necessary conditions to identify optimal points of equality constrained optimization problems.
This is done by converting a constrained problem to an equivalent unconstrained problem with the help of certain unspecified parameters known as Lagrange multipliers.

27. Lagrange Multipliers (  )
The classical problem formulation
minimize f(x1, x2, ..., xn)
Subject to h1(x1, x2, ..., xn) = 0
can be converted to
minimize L(x, l) = f(x) -  h1(x)
where
L(x, ) is the Lagrangian function
 is an unspecified positive or negative constant called the Lagrangian Multiplier
New problem is:
minimize L(x, l) = f(x) - h1(x)
Suppose that we fix  =  * and the unconstrained minimum of L(x; l) occurs at x = x* and x* satisfies h1(x*) = 0, then x* minimizes f(x) subject to h1(x) = 0.
Trick is to find appropriate value for Lagrangian multiplier l.
This can be done by treating l as a variable, finding the unconstrained minimum of L(x, l) and adjusting l so that h1(x) = 0 is satisfied.

28. Method Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)
Take derivatives of L(x, l) with respect to xi and set them equal to zero.
If there are n variables (i.e., x1, ..., xn) then you will get n equations with n + 1 unknowns (i.e., n variables xi and one Lagrangian multiplier l)
Express all xi in terms of Langrangian multiplier l
Plug x in terms of l in constraint h1(x) = 0 and solve l.
Calculate x by using the just found value for l.
Note that the n derivatives and one constraint equation result in n+1 equations for n+1 variables!

29. Multiple constraints
The Lagrangian multiplier method can be used for any number of equality constraints.
Suppose we have a classical problem formulation with k equality constraints
minimize f(x1, x2, ..., xn)
Subject to h1(x1, x2, ..., xn) = 0
......
hk(x1, x2, ..., xn) = 0
This can be converted in
minimize L(x, l) = f(x) - lT h(x)
Where lT is the transpose vector of Lagrangian multpliers and has length k

30. EXAMPLE HONDA CARS Factory manufactures HONDA CITY and HONDA CIVIC
MANUFACTURING CONSTRAINT; PLANT CAPACITY = 90 cars per day

31. cost or manufacturability
EXAMPLE
I can’t manage
cost or manufacturability
We cannot manage
the assembly line
with both cars
The engineer at his management desk
The manufacturing engineer in the machine shop

32. EXAMPLE
COST of Manufacturing; C (x, y)= 6x2 + 12y2

33. EXAMPLE VARIABLES COST of Manufacturing; OBJECTIVE: MINIMIZE COST
x = No. of HONDA CITY cars produced
y = No. of HONDA CIVIC cars produced
COST of Manufacturing;
C (x, y)= 6x2 + 12y2
OBJECTIVE:
MINIMIZE COST
CONSTRAINT: 90 cars per day
x + y = 90
Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)

34. EXAMPLE VARIABLES COST of Manufacturing; OBJECTIVE: MINIMIZE COST
x = No. of HONDA CITY cars produced
y = No. of HONDA CIVIC cars produced
COST of Manufacturing;
C (x, y)= 6x2 + 12y2
OBJECTIVE:
MINIMIZE COST
CONSTRAINT: 90 cars per day
x + y = 90
Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)
OR x + y - 90 = 0

35. EXAMPLE VARIABLES COST of Manufacturing; OBJECTIVE: MINIMIZE COST
x = No. of HONDA CITY cars produced
y = No. of HONDA CIVIC cars produced
COST of Manufacturing;
C (x, y)= 6x2 + 12y2
OBJECTIVE:
MINIMIZE COST
CONSTRAINT: 90 cars per day
x + y = 90
Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)
C (x, y)= 6x2 + 12y2 - l (x + y - 90 )
Fx = 12x – l
Fy = 24y – l
F l= – x – y + 90

36. EXAMPLE VARIABLES COST of Manufacturing; OBJECTIVE: MINIMIZE COST
x = No. of HONDA CITY cars produced
y = No. of HONDA CIVIC cars produced
COST of Manufacturing;
C (x, y)= 6x2 + 12y2
OBJECTIVE:
MINIMIZE COST
CONSTRAINT: 90 cars per day
x + y = 90
Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)
C (x, y)= 6x2 + 12y2 - l (x + y - 90 )
Fx = 12x – l = 0
Fy = 24y – l = 0
F l= – x – y = 0

37. EXAMPLE VARIABLES COST of Manufacturing; OBJECTIVE: MINIMIZE COST
x = No. of HONDA CITY cars produced
y = No. of HONDA CIVIC cars produced
COST of Manufacturing;
C (x, y)= 6x2 + 12y2
OBJECTIVE:
MINIMIZE COST
CONSTRAINT: 90 cars per day
x + y = 90
Original problem is rewritten as:
minimize L(x, l) = f(x) - l h1(x)
C (x, y)= 6x2 + 12y2 - l (x + y - 90 )
Fx = 12x – l = 0 ; x = l / 12
Fy = 24y – l = 0 ; y = l / 24
F l= – x – y + 90= 0;
l = 720
x = 60 ; y = 30
C (x, y)= 6(60)2 + 12(30)2 ?

38. LINEAR PROGRAMMING CHARACTERISTICS of LP MODELS

39. LINEAR PROGRAMMING
CHARACTERISTICS

40. General LP format

41. LINEAR PROGRAMMING STEPS

42. Steps for LP formulation
Step 1: define decision variables
Step 2: define the objective function
Step 3: state all the resource constraints
Step 4: define non-negativity constraints

43. LINEAR PROGRAMMING MAXIMIZATION PROBLEM

44. Example 1: Max Problem A Maximization Model
Example The Beaver Creek Pottery Company produces bowls and mugs. The two primary resources used are special pottery clay and skilled labour. The two products have the following resource requirements for production and profit per item produced (that is, the model parameters).
Resource available: 40 hours of labour per day and 120 pounds of clay per day. How many bowls and mugs should be produced to maximizing profits give these labour resources?
LP formulation

46. A Maximization Example
LP Model Formulation
A Maximization Example
Product mix problem - Beaver Creek Pottery Company
How many bowls and mugs should be produced to maximize profits given labor and materials constraints?
Product resource requirements and unit profit:

47. Sample of LP Decision variables Objective function Constraints or
Let xi be denoted as xi product to be produced, and
i = 1, 2 or
Let x1 be numbers of product 1 to be produced
and x2 be numbers of product 2 to be produced
Maximize Z=$40x1 + 50x2
subject to
1x1 + 2x2  40 hours of labor
4x2 + 3x2  120 pounds of clay
x1, x2  0
Decision variables
Objective function
Constraints

48. Max LP problem Step 1: define decision variables
Let x1=number of bowls to produce/day
x2= number of mugs to produce/day
Step 2: define the objective function
maximize Z = $40x1 + 50x2
where Z= profit per day
Step 3: state all the resource constraints
1x1 + 2x2  40 hours of labor ( resource constraint 1)
4x1 + 3x2  120 pounds of clay (resource constraint 2)
Step 4: define non-negativity constraints
x10; x2  0
Complete Linear Programming Model:
maximize Z=$40x1 + 50x2
subject to
1x1 + 2x2  40
4x2 + 3x2  120
x1, x2  0

49. A Maximization Example
LP Model Formulation
A Maximization Example
Resource hrs of labor per day
Availability: 120 lbs of clay
Decision x1 = number of bowls to produce per day
Variables: x2 = number of mugs to produce per day
Objective Maximize Z = $40x1 + $50x2
Function: Where Z = profit per day
Resource x1 + 2x2  40 hours of labor
Constraints: 4x1 + 3x2  120 pounds of clay
Non-Negativity x1  0; x2  0
Constraints:

50. Complete Linear Programming Model: Maximize Z = $40x1 + $50x2
LP Model Formulation
A Maximization Example
Complete Linear Programming Model:
Maximize Z = $40x1 + $50x2
subject to: x1 + 2x2  40
4x1 + 3x2  120
x1, x2  0

51. A FEASIBLE SOLUTION
A feasible solution does not violate any of the constraints:
Example x1 = 5 bowls
x2 = 10 mugs
Z = $40x1 + $50x2 = $700
Labor constraint check:
1(5) + 2(10) = 25 < 40 hours, within constraint
Clay constraint check:
4(5) + 3(10) = 70 < 120 pounds, within constraint

52. An INFEASIBLE SOLUTION
An infeasible solution violates at least one of the constraints:
Example x1 = 10 bowls
x2 = 20 mugs
Z = $1400
Labor constraint check:
1(10) + 2(20) = 50 > 40 hours, violates constraint

53. MULTIDISCIPLINARY SYSTEM DESIGN Optimization IN AEROSPACE INDUSTRY ______________________________________ HEURISTIC OPITMIZATION TECHNIQUES
LECTURE #1

54. Imagine that we are going to give away a Lottery To the Best of the mathematicians in this hall . In particular we are going to give away z pesos,
But after you tell us the value of x and y
x and y in the range of 0 to 10

55. z x y

56. “Way forward to solve these type of problems”
MODERN OPTIMIZATION
“Way forward to solve these type of problems”

57. MODERN OPTIMIZATION
Population based, non-gradient, stochastic direct search optimization methods:
Genetic Algorithm
Swarm Intelligence
Simulated Annealing
Ant Colony Optimization
etc

58. COMPARISON GRADIENT BASED HEURISTIC BASED ADVANTAGES DRAWBACKS
Convergence speed
Local minimization
Global minimization
Gradient evaluation
Robustness ?
Not multi-objective*
Multi-objective

59. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ EVOLUTION OF DESIGN TECHNOLOGY
LECTURE #1

60. Evolution of Design Technology
Trial & Error
Empirical
Mathematical
Probabilistic
Deterministic
(Factors of Safety)
Stochastic
(Risk Quantified)
Random
Experimentation
Experience-based ``
DFSS at GE is the same as Probabilistic Design
This is a BIG change in how we approach design – it will be a long journey, one that we’re only years into
The whole purpose of probabilistic design is to be able to answer the question “how much risk is in my design” – a question that cannot be answered when you use factors of safety
The code of Hammurabi laid down the rules for deterministic design
Babylonian 1775 BC
e.g.
In the criminal law the ruling principle was the lex talionis. Eye for eye, tooth for tooth, limb for limb was the penalty for assault upon an amelu. A sort of symbolic retaliation was the punishment of the offending member, seen in the cutting off the hand that struck a father or stole a trust; in cutting off the breast of a wet-nurse who substituted a changeling for the child entrusted to her; in the loss of the tongue that denied father or mother (in the Elamite contracts the same penalty was inflicted for perjury); in the loss of the eye that pried into forbidden secrets. The loss of the surgeon's hand that caused loss of life or limb or the brander's hand that obliterated a slave's identification mark, are very similar. The slave, who struck a freeman or denied his master, lost an ear, the organ of hearing and symbol of obedience. To bring another into danger of death by false accusation was punished by death. To cause loss of liberty or property by false witness was punished by the penalty the perjurer sought to bring upon another. The death penalty was freely awarded for theft and other crimes regarded as coming under that head, for theft involving entrance of palace or temple treasury, for illegal purchase from minor or slave, for selling stolen goods or receiving the same, for common theft in the open (in default of multiple restoration) or receiving the same, for false claim to goods, for kidnapping, for assisting or harbouring fugitive slaves, for detaining or appropriating same, for brigandage, for fraudulent sale of drink, for disorderly conduct of tavern, for delegation of personal service, for misappropriating the levy, for oppression of feudal holders, for causing death of a householder by bad building. The manner of death is not specified in these cases. This death penalty was also fixed for such conduct as placed another in danger of death. A specified form of death penalty occurs in the following cases:-gibbeting (on the spot where crime was committed) for burglary, later also for encroaching on the king's highway, for getting a slave-brand obliterated, for procuring husband's death; burning for incest with own mother, for vestal entering or opening tavern, for theft at fire (on the spot); drowning for adultery, rape of betrothed maiden, bigamy, bad conduct as wife, seduction of daughter-in-law. A curious extension of the talio is the death of creditor's son for his father's having caused the death of debtor's son as mancipium; of builder's son for his father's causing the death of house-owner's son by building the house badly; the death of a man's daughter because her father caused the death of another man's daughter.
Graphical Approaches
Systematic
Experimentation
Computer models
based on system
physics
Point estimates
Computer
Simulations based on
system physics
Robust Solutions

61. Evolution of Design Technology
100:1
CONCEPTUAL
DESIGN
1:1
10:1
PRELIMINARY
DETAIL
PRODUCT
ATTRIBUTES
Conceptual design is crucial to the success of the overall design process and resulting system. It has been estimated that “at least 80% of a Mission’s life-cycle cost is locked in by the concept that is chosen” and “conceptual design decision have a 100:1 leverage on end product quality and cost”

62. Evolution of Design Technology
Freedom
Design Freedom
Evolution of Design Technology
100%
Cost
Cost Committed
Knowledge
Knowledge about Design
Knowledge
50% 0%
Detail Design
Prototype Development
Redesign
Concept
Preliminary Design
Product Release

63. Evolution of Design Technology
Freedom
Design Freedom
Evolution of Design Technology
Ideal time to make design decisions and design changes
100%
Cost
Cost Committed
Worst time to make design decisions and design changes
Knowledge
Knowledge about Design
“The wrong choice of concept in a given design situation can rarely, if ever, be recouped by brilliant detail design.” [Pugh]
Knowledge
50% 0%
Detail Design
Prototype Development
Redesign
Concept
Preliminary Design
Product Release

64. Evolution of Design Methodology
CONVENTIONAL
OPTIMAL
1. Specification 2. Baseline design 3. Analysis (or experiment) 4. Check performance or failure criteria 5. Is design satisfactory? (If yes, then stop) 6. Change design parameters based on intuition and heuristics, return to 3.
1. Specification 2. Baseline design 3. Analysis 4. Check constraints 5. Does design satisfy the optimality conditions? (If yes, then stop) 6. Change design parameters using an optimization strategy, return to 3.

65. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ INTRODUCTION TO MDO
LECTURE #1

66. MDO: Aircraft
Problem formulation is not obvious and requires engineering judgment.
One can only make one thing best at a time.
MANUFACTURING
CONTROLS
PROPULSION
MDO
AERODYNAMICS
STRUCTURES

67. MDO: Spacecraft
MDO

68. MDO of Helicopter
Decomposition of Rotorcraft analysis process

69. MDO of air conditioner pipe shape

70. MDO: Automotive system

71. MDO: Industry

73. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ INGREDIENTS
LECTURE #1

74. MDO: Ingredients
MDO

75. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ FRAMEWORK
LECTURE #1

76. POST OPTIMIZATION ANALYSES
MDO: Framework
Design Variables
SYSTEM
Objective Function
Constraints
POST OPTIMIZATION ANALYSES
OPTIMIZER
DOE

77. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ DESIGN OF EXPERIMENTS
LECTURE #1

78. DESIGN OF EXPERIMENTS
“To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.”
Fisher, R. A., Indian Statistical Congress, Sankhya, 1938.

79. DESIGN of EXPERIMENTS thousand years ago : Experimental Science
description of natural phenomena, Astronomy etc
last few hundred years : Theoretical Science
Newton’s laws, Maxwell’s equations …
last few decades : Computational Science
simulation of complex phenomena
today : e-Science or data-centric science
massive computing
large data exploration and mining
UNIFY : theory, experiment, and simulation

80. DESIGN OF EXPERIMENTS

81. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ RESPONSE SURFACE METHODS
LECTURE #1

82. Black Boxed System Input Response Original System DOE and Experiments 1 -1
DOE and Experiments
RS Model

83. NASTRAN ANSYS ……. Black Boxed System Rewrite Read Input Files
Response
Black Boxed
System
(FE Model)
*.bdf
*.cdb
*.f06 , *.pch
*.rst
NASTRAN
ANSYS
…….
Rewrite
Input Files
Read
Output Files

84. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ META MODELING
LECTURE #1

85. Meta-Modeling Meta Model Model Physical system RS Model
The Modeling Space c k
f(t)
input + -
x(t)
output
Model
Physical system
World

86. META-MODELING based MDO

87. META-MODELING

88. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ UNCERATINTY IN DESIGNS
LECTURE #1

89. CLASSIFICATION of UNCERTAINTY

90. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ robust design
LECTURE #1

91. ROBUST DESIGN
“Robust Parameter Design …is a statistical / engineering methodology that aims at reducing the performance variation of a system (i.e. a product or process) by choosing the setting of its control factors to make it less sensitive to noise variation."
Wu, C. F. J. and M. Hamada, 2000, Experiments: Planning, Analysis, and Parameter Design Optimization, John Wiley & Sons, NY.
Design that results in products or services that can function over a broad range of conditions

92. ROBUST DESIGN
Example: We want to pick x to maximize F
Simply doing a trade study to optimize the value of F would lead the designer to pick this point F
What if I pick this point instead?
This means that values of F as low as this can be expected! x
Robust design: a design whose performance is insensitive to variations.

93. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ FORMULATIONS
LECTURE #1

94. MDO FORMULATIONS Non-hierarchical vs. Hierarchical MDO Architectures
November 14, 2017

95. MDO FORMULATIONS

96. MDO FORMULATIONS Analytical Targeting Cascading ATC Method
Discipline Interaction Variable Elimination
DIVE Method

97. MDO FORMULATIONS (MDF)
EVOLUTIONARY METHODS
ITERATIVE METHODS

98. MDO FORMULATIONS (IDF, AAO, CO, BLISS, MCO)
EVOLUTIONARY METHODS
ITERATIVE METHODS

99. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ PLATFORM & TOOLS
LECTURE #1

100. MDO: Platform & Tools

101. MDO: Platform & Tools

102. Platform & Tools

103. COMMERCIAL SOFTWARES

104. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ APPLICATIONS PAST & PRESENT
LECTURE #1

105. APPLICATIONS STRUCTURAL OPTIMIZATION AIRCRAFT WING DESIGN AIRCRAFT
Schmit 1960, 1965, 1981, 1984 ;
Haftka 1973, 1975, 1977, 1979 [1, 2, 3, 4, 5, 6, 7, 8]
AIRCRAFT WING DESIGN
Ashley 1982, Green 1987, Grossman 1988, Livne, 1990, 1999, Jansen 2010, Ning 2010 [9, 10, 11, 12, 13, 14, 15, 16]
AIRCRAFT
Kroo 1994, Manning 1999, Antoine 2005, Henderson 2012, Alonso 2012 [17, 18, 19, 20, 21]
BRIDGES & BUILDINGS
Ballin 2000, Choudhary 2005, Geyer 2009 [22, 23, 24]
RAILWAY CARS
He 2005, Enblom 2006 [25, 26]
MICROSCOPES
Potsaid 2006 [27]
AUTOMOBILES
McAllister 2004, Kokkolaras 2004 [28,29]
SHIPS
Peri 2003, Kalavalapally 2006 [30, 31]

106. APPLICATIONS PROPELLERS ROTORCRAFT WIND TURBINES SPACECRAFT
Takekoshi 2005, Young 2010 [32, 33]
ROTORCRAFT
Ganguli 2004, Glaz 2009 [34, 35]
WIND TURBINES
Fuglsang 1999, 2002, Kenway 2008 [36, 37, 38]
SPACECRAFT
Braun 1997, Cai 2010 [39, 40]
SPACE LAUNCH VEHICLES
Zingg 2008, Nocedal 2006, Tappeta 1997,
Mateen 2006
Saqlain 2007
Bailey 2007
Rafique 2008, 2009, 2012
Qasim 2008, 2009, 2010

107. APPLICATIONS
SGI and Ford use MDO and response surface models for rapid visualization of design alternatives
Penn State collaborated with Boeing and Lockheed Martin Space Systems to develop visualization interfaces to support design decision-making
Sandia National Labs continues investigating optimization under uncertainty and surrogate- based optimization
NASA-Langley is developing robust optimization methods for aerodynamics and multidisciplinary aero-structural design
University of Utah is developing an approach to optimize structural problems
Georgia Tech is using MDO to design energetic materials
Russia’s Central Aerohydrodynamic Institute is using MDO to develop new concepts for different types of airplanes
Boeing is using MDO to help design the next generation hypersonic aircraft

108. APPLICATIONS
Delft University is defining high-level primitive “building blocks” to support conceptual design of aircraft from a multidisciplinary perspective.
MIT researchers are investigating the impact of new technologies as well as the tradeoffs between noise and engine emissions vs. performance and operating costs for commercial aircraft fleets.
University of Oklahoma improved displacement- based multilevel structural optimization by combining parallel subsystem-level optimizations with a substructuring approach for parallel system-level optimization.
University of Missouri-Columbia used MDO to incorporate the effect of materials processing on a product’s material properties when designing short fiber-reinforced polymer composites.
Wright State University investigated 3D preform shape design in forging problems using reduced basis concepts for extremely large scale nonlinear plastic deformation problems.
University of Michigan investigated the impact of uncertainties in design optimization of hierarchical multilevel systems and applied analytical target cascading to simulation- based design and integrated engineering/marketing decision-making.

109. Particle Swarm Optimization
APPLICATIONS
Gradient
Based
Genetic Algorithm
Particle Swarm Optimization
Simulated Annealing
Hybrid Algorithm
Meta-model Based
Uncertainty Based
Satellite Launch Vehicles
Pribnow 1991
Helmy 1982 Nguyen 1993
Olds 1994
Tsuchiya 2004
Brown 2005
Bayley 2008
Rafique & Qasim 2009
Rafique & Qasim 2010
Rafique, Qasim, & Kamran 2009
Saqlain 2006
Mateen 2004
Solid Rocket Motor
Chiang1983
Harry 1992
Khurram 2009
Kamran, Qasim, Rafique 2011
A Raza
2011, 2012
Kamran, Qasim, Rafique 2013
Missiles
Saqlain 2007
Hartfield 2004
Riddle 2007
Anderson 2000
Burkhalter
Tekinalp, O. and Utalay, S 2000, 2004

110. Particle Swarm Optimization
APPLICATIONS
Gradient
Based
Genetic Algorithm
Particle Swarm Optimization
Simulated Annealing
Hybrid Algorithm
Meta-model Based
Uncertainty Based
Interceptors
(Space Based)
Dennis 2004
(Air Based)
Anderson, M.B., Burkhalter, J.E., and Jenkins, R.M. 2001
(Ground Based)
David G. Hull and David E. Salguero 1994
Anderson 1998, 1999
Boost Phase Interceptor
Qasim, Rafique & Kamran 2008
Qasim, Rafique & Kamran 2009
Qasim, Rafique & Kamran 2010

111. APPLICATIONS in Space Sciences
Kim, Y. and Spencer, D. (2002)
Optimal spacecraft rendezvous using genetic algorithms.
D.R. Myatt, V.M. Becerra, S.J. Nasuto, J.M. Bishop, 2004
Advanced Global Optimisation for Mission Analysis and Design
Olds, A. D., Kluever, C. A., and Cupples, M. (2007).
Interplanetary mission design using differential evolution.
Vasile, M et al (2007, 2008)
Benchmarking different global optimisation techniques for preliminary space trajectory design
On testing global optimization algorithms for space trajectory design
Alonso et al 2008, 2010a, 2010b, 2010c, 2011
GA Optimization of the height of a Low Earth Orbit
Multi-criteria Genetic Optimisation of the Manoeuvres of aTwo-Stage Launcher
From Earth to Kuiper belt: swarm optimisation algorithm applied to interplanetary missions.
Delta-V genetic optimization of a trajectory from Earth to Saturn with fly-by in Mars.
Particle Swarm Optimisation of an Interplanetary Trajectory from Earth to Jupiter

112. APPLICATIONS in Space Sciences
Gage P.J., Braun R.D., Kroo I.M, 1995
“Interplanetary Trajectory Optimisation Using a Genetic Algorithm”
David A. Vallado
Fundamentals of Astrodynamics
Qasim et al 2006
“Optimal Orbit Transfer using Genetic Algorithms”
Ya-Zhong et al 2006
"Optimization of Multiple-Impulse Minimum-Time Rendezvous with Impulse Constraints Using a Hybrid GA”
Rosa et al, 2006
"Genetic Algorithm and Indirect Method Coupling for Low-Thrust Trajectory Optimization”
Santos et al , 2011, 2012
“Optimal Trajectories using Gravity Assisted Maneuver and Solar Electric Propulsion Towards Near- Earth-Objects”
“Rendezvous Maneuvers with Minimal ΔV.”
”Four-Impulsive Rendezvous Maneuvers for Spacecrafts in Circular Orbits Using Genetic Algorithms”.
“Minimum Fuel Multi-Impulsive Orbital Maneuvers Using Genetic Algorithms”.

113. APPLICATIONS : SUMMARY
PAST
PRESENT
Structural optimization
Single objective / NLP
Single discipline
Specialist group
Single workstation
Internal codes tailored for MDO
After-design improvements
Optimization
Conceptual Design
Other domains
Multiple objective + design exploration
Multiple disciplines
Wider user base & guidelines
High Performance Computing
Off-the-shelf MDO tools
Design exploration early design phases
Uncertainty based Robust Design Optimization
Detail Design

114. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ CHALLENGES
LECTURE #1

115. CHALLENGES of MDO Fidelity/expense of disciplinary models: Complexity:
Fidelity is often sacrificed to obtain models with short computation times.
Complexity:
Design variables, constraints and model interfaces must be managed carefully.
Communication:
The user interface is often very unfriendly and it can be difficult to change problem parameters.
Flexibility:
It is easy for an MDO tool to become very specialized and only valid for one particular problem.

116. CHALLENGES of MDO
How do we prevent MDO codes from becoming complex, highly specialized tools which are used by a single person (often the developer!) for a single problem?

117. CHALLENGES of MDO FIDELITY Vs EXPENSE Hand-shaking: Qualitative vs.
Quantitative
right fidelity for the
right application

118. CHALLENGES of MDO BREADTH Vs DEPTH Hand-shaking: Qualitative vs.
Quantitative
right fidelity for the
right application

119. CHALLENGES OF MDO ORGANIZATIONAL CHALLENGES and MISCONCEPTIONS
Hurdles in Human Team Organization
Incompatibility Between Disciplinary analysis codes
Complexities in Software Integration
Acceptability of new methodologies by Industry
MDO is "NOT" a push-button approach:
Eradicating the confusion between MDO, and the misguided futile attempts made earlier for replacing the human element and do automatic design.
MDO is NOT an alternative to traditional Knowledge Based Engineering practice - MDO complements KBE tools!

120. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ ENVIRONMENT
LECTURE #1

121. ENVIRONMENT
MDO is not only about connecting lines of codes, its also about connecting people
Not only supports human decision making, but also changes the environment completely in which and how people design
Supports collaboration via networking
Improves/teaches the engineers
Leads to innovation
Transparency within the multidisciplinary teams
Deal with changes
Make people feel like a team
Changes the mindset of engineers
Helps establish a learning community

122. MULTIDISCIPLINARY SYSTEM DESIGN Optimization ______________________________________ RECENT DEVELOPMENTS
LECTURE #1

123. RECENT DEVELOPMENTS Consensus on the best short term way ahead
Approximation Concepts
MSDO Architectures
Sensitivity Analysis
Optimization Algorithms and Theory
Software Infrastructure
Analysis Methodology

124. RECENT DEVELOPMENTS Improvement in the following Fidelity of Models
J. R. R. A. Martins, J. J. Alonso, and J. J. Reuther, “High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet”, Journal of Aircraft, vol. 41, p. 523–530, 2004.
Architectural Frameworks
A. B. Lambe and J. R. R. A. Martins, “A Unified Description of MDO Architectures”, Proceedings of the 9th World Congress on Structural and Multidisciplinary Optimization
Optimization Algorithms
Rafique A.F., LinShu H., Kamran A., Zeeshan Q., Hyper Heuristic approach for Design and Optimization of Satellite Launch Vehicle. Chinese Journal of Aeronautics, Vol. 24, No. 2, pp 150 – 163, 2011

125. RECENT DEVELOPMENTS Platforms, Specialized Toolkits
ISIGHT
J. Gray, K. T. Moore, and B. A. Naylor, “OpenMDAO: An Open Source Framework for Multidisciplinary Analysis and Optimization”, Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference
R. E. Perez and J. R. R. A. Martins, “pyACDT: An Object- Oriented Framework for Aircraft Design Modelling and Multidisciplinary Optimization”
Flexible Body Dynamics
C. M. Shearer and C. E. Cesnik, “Nonlinear Flight Dynamics of Very Flexible Aircraft”, Journal of Aircraft, vol. 44. p , 2007.
Application of MDO to more complex and Non Conventional Designs
A. J. De Wit and F. Van Keulen, “Overview of Methods for Multilevel and/or Multidisciplinary Optimization”, Proceedings of the 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

126. CATALIST -Aircraft Parametric Modeler CATia Advanced Design Linking & Iteration Software & Tool

127. MDO: 5 year Roadmap

128. MULTIDISCIPLINARY SYSTEM DESIGN Optimization IN AEROSPACE INDUSTRY ______________________________________ FUTURE
LECTURE #1

129. FUTURE Tool boxes People Integration frameworks
System-of-Systems Approach for Assessing New Technologies
Optimization algorithms
MDO architectures
Problem formulation
Decomposition strategies
High performance computing
Parametric geometry
People
Engineers pushing hard for success
Individual disciplines specify their own analysis tools
Training

130. FUTURE WE OUR How can we contribute? Free tool providers Academics
Increase the number of users
Increase capabilities of the community
Solve problems we couldn’t solve before
Academics
Produce papers
Produce students
Grow community

131. THANK YOU FOR YOUR INTEREST