Early in the design process, there is often mixed epistemic model uncertainty and aleatory parameter uncertainty. Later in the design process, the results of high-fidelity simulations or experiments will reduce epistemic model uncertainty and may trigger a redesign process. Redesign is undesirable because it is associated with costs and delays; however, it is also an opportunity to correct a dangerous design or possibly improve design performance. In this study, we propose a margin-based design/redesign method where the design is optimized deterministically, but the margins are selected probabilistically. The final design is an epistemic random variable (i.e., it is unknown at the initial design stage) and the margins are optimized to control the epistemic uncertainty in the final design, design performance, and probability of failure. The method allows for the tradeoff between expected final design performance and probability of redesign while ensuring reliability with respect to mixed uncertainties. The method is demonstrated on a simple bar problem and then on an engine design problem. The examples are used to investigate the dilemma of whether to start with a higher margin and redesign if the test later in the design process reveals the design to be too conservative, or to start with a lower margin and redesign if the test reveals the design to be unsafe. In the examples in this study, it is found that this decision is related to the variance of the uncertainty in the high-fidelity model relative to the variance of the uncertainty in the low-fidelity model.

References

1.
Hoffman
,
F. O.
, and
Hammonds
,
J. S.
,
1994
, “
Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability
,”
Risk Anal.
,
14
(
5
), pp.
707
712
.
2.
Paté-Cornell
,
M. E.
,
1996
, “
Uncertainties in Risk Analysis: Six Levels of Treatment
,”
Reliab. Eng. Syst. Saf.
,
54
(
2–3
), pp.
95
111
.
3.
Ferson
,
S.
, and
Ginzburg
,
L. R.
,
1996
, “
Different Methods are Needed to Propagate Ignorance and Variability
,”
Reliab. Eng. Syst. Saf.
,
54
(
2–3
), pp.
133
144
.
4.
Faber
,
M. H.
,
2005
, “
On the Treatment of Uncertainties and Probabilities in Engineering Decision Analysis
,”
ASME J. Offshore Mech. Arct. Eng.
,
127
(
3
), pp.
243
248
.
5.
O'Hagan
,
A.
, and
Oakley
,
J. E.
,
2004
, “
Probability is Perfect, But We Can't Elicit it Perfectly
,”
Reliab. Eng. Syst. Saf.
,
85
(
1–3
), pp.
239
248
.
6.
Wright
,
I. C.
,
1997
, “
A Review of Research Into Engineering Change Management: Implications for Product Design
,”
Des. Stud.
,
18
(
1
), pp.
33
42
.
7.
Jarratt
,
T. A. W.
,
Eckert
,
C. M.
,
Caldwell
,
N. H. M.
, and
Clarkson
,
P. J.
,
2011
, “
Engineering Change: An Overview and Perspective on the Literature
,”
Res. Eng. Des.
,
22
(
2
), pp.
103
124
.
8.
Clarkson
,
P. J.
,
Simons
,
C.
, and
Eckert
,
C.
,
2004
, “
Predicting Change Propagation in Complex Design
,”
ASME J. Mech. Des.
,
126
(
5
), pp.
788
797
.
9.
Ollinger
,
G. A.
, and
Stahovich
,
T. F.
,
2004
, “
RedesignIT—A Model-Based Tool for Managing Design Changes
,”
ASME J. Mech. Des.
,
126
(
2
), pp.
208
216
.
10.
Chen
,
L.
,
Macwan
,
A.
, and
Li
,
S.
,
2006
, “
Model-Based Rapid Redesign Using Decomposition Patterns
,”
ASME J. Mech. Des.
,
129
(
3
), pp.
283
294
.
11.
Hamraz
,
B.
,
Caldwell
,
N. H. M.
, and
John Clarkson
,
P.
,
2012
, “
A Multidomain Engineering Change Propagation Model to Support Uncertainty Reduction and Risk Management in Design
,”
ASME J. Mech. Des.
,
134
(
10
), p.
100905
.
12.
Romli
,
F. I.
, and
Harmin
,
M. Y.
,
2015
, “
Use of Monte Carlo Method to Estimate Subsystem Redesign Risk for Complex Products: Aircraft Redesign Case Study
,”
Aircr. Eng. Aerosp. Technol.
,
87
(
6
), pp.
563
570
.
13.
Villanueva
,
D.
,
Haftka
,
R. T.
, and
Sankar
,
B. V.
,
2011
, “
Including the Effect of a Future Test and Redesign in Reliability Calculations
,”
AIAA J.
,
49
(
12
), pp.
2760
2769
.
14.
Matsumura
,
T.
, and
Haftka
,
R. T.
,
2013
, “
Reliability Based Design Optimization Modeling Future Redesign With Different Epistemic Uncertainty Treatments
,”
ASME J. Mech. Des.
,
135
(
9
), p.
091006
.
15.
Villanueva
,
D.
,
Haftka
,
R. T.
, and
Sankar
,
B. V.
,
2014
, “
Accounting for Future Redesign to Balance Performance and Development Costs
,”
Reliab. Eng. Syst. Saf.
,
124
, pp.
56
67
.
16.
Price
,
N. B.
,
Matsumura
,
T.
,
Haftka
,
R. T.
, and
Kim
,
N. H.
,
2014
, “
Deciding How Conservative a Designer Should be: Simulating Future Tests and Redesign
,”
AIAA
Paper No. AIAA 2014-1010.
17.
Price
,
N. B.
,
Balesdent
,
M.
,
Defoort
,
S.
,
Riche
,
R. L.
,
Kim
,
N. H.
, and
Haftka
,
R. T.
,
2016
, “
Simulating Future Test and Redesign Considering Epistemic Model Uncertainty
,”
AIAA
Paper No. AIAA 2016-0950.
18.
Saleh
,
J. H.
,
Mark
,
G.
, and
Jordan
,
N. C.
,
2009
, “
Flexibility: A Multi-Disciplinary Literature Review and a Research Agenda for Designing Flexible Engineering Systems
,”
J. Eng. Des.
,
20
(
3
), pp.
307
323
.
19.
De Neufville
,
R.
, and
Scholtes
,
S.
,
2011
,
Flexibility in Engineering Design
,
Engineering systems, MIT Press
,
Cambridge, MA
.
20.
Roser
,
C.
, and
Kazmer
,
D.
,
1999
, “
Risk Effect Minimization Using Flexible Product and Process Design
,” ASME Paper No. DETC1999 DFM-8959.
21.
Roser
,
C. H.
,
2000
, “
A Flexible Design Methodology
,” Ph.D. thesis, University of Massachusetts Amherst, Amherst, MA.
22.
Roser
,
C.
,
Kazmer
,
D.
, and
Rinderle
,
J.
,
2003
, “
An Economic Design Change Method
,”
ASME J. Mech. Des.
,
125
(
2
), pp.
233
239
.
23.
Tu
,
J.
,
Choi
,
K. K.
, and
Park
,
Y. H.
,
1999
, “
A New Study on Reliability-Based Design Optimization
,”
ASME J. Mech. Des.
,
121
(
4
), pp.
557
564
.
24.
Du
,
X.
, and
Chen
,
W.
,
2004
, “
Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design
,”
ASME J. Mech. Des.
,
126
(
2
), pp.
225
233
.
25.
Youn
,
B. D.
, and
Choi
,
K. K.
,
2004
, “
Selecting Probabilistic Approaches for Reliability-Based Design Optimization
,”
AIAA J.
,
42
(
1
), pp.
124
131
.
26.
Mahadevan
,
S.
, and
Rebba
,
R.
,
2006
, “
Inclusion of Model Errors in Reliability-Based Optimization
,”
ASME J. Mech. Des.
,
128
(
4
), pp.
936
944
.
27.
Gano
,
S. E.
,
Renaud
,
J. E.
,
Agarwal
,
H.
, and
Tovar
,
A.
,
2006
, “
Reliability-Based Design Using Variable-Fidelity Optimization
,”
Struct. Infrastruct. Eng.
,
2
(
3–4
), pp.
247
260
.
28.
Jiang
,
Z.
,
Chen
,
W.
,
Fu
,
Y.
, and
Yang
,
R.-J.
,
2013
, “
Reliability-Based Design Optimization With Model Bias and Data Uncertainty
,”
SAE Int. J. Mater. Manuf.
,
6
(
3
), pp. 502–516.
29.
Eckert
,
C.
,
Earl
,
C.
,
Lebjioui
,
S.
, and
Isaksson
,
O.
,
2013
, “
Components Margins Through the Product Lifecycle
,”
Product Lifecycle Management for Society
(IFIP Advances in Information and Communication Technology), A. Bernard, L. Rivest, and D. Dutta, eds., Springer, Berlin, pp.
39
47
.
30.
Thornton
,
A. C.
,
1998
, “
Optimism vs. Pessimism: Design Decisions in the Face of Process Capability Uncertainty
,”
ASME J. Mech. Des.
,
123
(
3
), pp.
313
321
.
31.
Hansen
,
N.
,
2006
, “
The CMA Evolution Strategy: A Comparing Review
,”
Towards a New Evolutionary Computation
,
Springer
,
Berlin
, pp.
75
102
.
32.
Kadane
,
J.
, and
Wolfson
,
L. J.
,
1998
, “
Experiences in Elicitation
,”
J. R. Stat. Soc, Ser. D (Stat.)
,
47
(
1
), pp.
3
19
.
33.
Federal Aviation Regulations
,
2015
, “
§25.613 Material Strength Properties and Material Design Values
,” Federal Aviation Administration, Washington, DC.
34.
Federal Aviation Regulations
,
2015
, “
§25.303 Factor of Safety
,” Federal Aviation Administration, Washington, DC.
35.
Choi
,
S.-K.
,
Grandhi
,
R. V.
, and
Canfield
,
R. A.
,
2007
,
Reliability-Based Structural Design
,
Springer-Verlag
,
London
.
36.
Agte
,
J. S.
,
Sobieszczanski-Sobieski
,
J.
, and
Sandusky
,
R. R.
,
1999
, “
Supersonic Business Jet Design Through Bi-Level Integrated System Synthesis
,” SAE Technical Paper No. 1999-01-5622.
37.
Rockafellar
,
R. T.
, and
Uryasev
,
S.
,
2000
, “
Optimization of Conditional Value-At-Risk
,”
J. Risk
,
2
(
3
), pp.
21
42
.
You do not currently have access to this content.