Isoperformance: Analysis and Design of
Complex Systems with Desired Outcomes
O. De Weck and M. Jones
Presented by
Student 3 and Student 4
September 25, 2009
Motivation
• Traditional design is based on maximizing performance.
• This focus on pure performance maximization can result in drawbacks in
terms of cost, schedule, and risk
Performance
RISK
Schedule
Cost
• Diminishing returns at the margin of the performance envelope
• Augustine Law Number XV: “The last 10 percent of performance generates
1/3 of the cost and 2/3 of the problems.”
Introducing isoperformance design
• Core concept: accept “good enough” performance instead of seeking
“best achievable”
• Performance = NIB (vs. performance = LIB). Willingness to trade some
performance for benefits in terms of cost, schedule or risk.
Ui
1.0
NIB
nominal-is-better
LIB
larger-is-better
Utility
SIB
smaller-is-better
0.0
Ji
Performance
Image by MIT OpenCourseWare.
• Inverse method that finds all the designs that satisfy a certain level of
performance: these designs exhibit isoperformance
– E.g. design of antennae, digital circuits, or airfoils.
• This subspace of designs can then be explored to find the efficient ones in
terms of secondary metrics.
Methodology
1. Find performance-invariant set of designs
–
–
–
Branch and bound algorithm
Gradient-based contour following algorithm
Progressive vector spline approximation
2. Find an efficient subset of designs
–
–
–
Incorporate cost and risk considerations in this step
Identify non-inferior designs
These constitute a Pareto efficient frontier in the design space
3. Select final design
–
Stakeholder consensus (non numerical objectives)
Methodology
Cost-Risk Objective Space
Venn Diagram
R
n
B: Set of all designs bounded by
“side constraints”
Step 3
Final
design
F
E
P
F: Set of feasible designs
I
adequate performance,
but better cost & risk
set E
I: Set of isoperforming designs
(a)
Xj
P: Pareto efficient set of
non-inferior designs
Xj,UB
E: Set of Pareto efficient designs
that satisfy isoperformance
requirement
Xj,LB
g2
Maximum performance,
but higher cost & risk
Jcr,j
B
Jcr,i
(d)
Step 2
g1
Jz,j
B
Isoperformance
contour I
F
Reachable region
Step 1
Performance target
Xi,LB
Xi,UB
Design Space
(b)
Xi
Performance Objective Space
Jz,i
(c)
Image by MIT OpenCourseWare.
Example 1: Space Telescope
Ud
90 [gcm2]
Qc
0.025
• Step 1: Several hundred potential designs
identified via gradient gradient-based
contour following
• Step 2: Three cost-risk variables
Tgs
0.4 [sec]
A
KrlSO
5000
[Nm/rad]
– Closeness to mid-range of design variables (A)
“Best balance”
– Magnitude of control gain (B)
“Minimum energy”
– Sensitivity of performance to perturbations (C)
“Robust”
Performance Objectives
Ru
3850 [RPM]
C
K zpet
18e+8 [N/m]
Kcf
1e+6
tsp
0.005 [m]
A: min(Jcr1)
Design
Jz,1 WFE
Jz,2 LOS
A: x1iso
20.000
5.2013
0.6324
0.4668
14.32%
2
B: xiso
20.0012
5.0253
0.8960
0.0017
8.7883%
3
C: xiso
20.0001
4.8559
1.5627
1.000
5.3067%
Jcr,2
Mgs
20 [mag]
B: min(Jcr2)
Cost-Risk Objectives
Jcr,1
Us
2.7 [gcm]
B
Jcr,3
Isoperformance tolerance t=0.05.
Images by MIT OpenCourseWare.
C: min(Jcr3)
Topics for discussion (1/3)
• The design selection step of the methodology avoids any
direct comparison across metrics such as multi-attribute
utility theory and states that the final decision is made on the
basis of non quantified objectives and stakeholder consensus.
Can this process be captured in a model?
• In general, there is an infinite set of designs that are capable
of providing the desired level of performance. How do we
discretize the problem so that the algorithm can explore the
design tradespace? How do we choose the design variables,
their range of values, and their discretization? How do we
choose our figures or merit?
Topics for discussion (2/3)
• Can we explore in a systematic way the space of possible
system models (i.e. one step back from exploring the design
tradespace)?
• Is the method presented in the isoperformance paper a
particular example of multi-attribute tradespace exploration
where there is a constraint for all the architectures to have
the same performance? What are the
similarities/differences?
Topics for discussion (3/3)
• To what extent do requirements influence the applicability of
isoperformance design? Are there certain ways in which
systems engineers should formulate requirements in order to
best use the isoperformance design process, or is it applicable
regardless of how requirements are defined?
MIT OpenCourseWare
http://ocw.mit.edu
16.842 Fundamentals of Systems Engineering
Fall 2009
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