This paper addresses the sensitivity analysis of time-dependent computer models. Often, in practice, we partition the inputs into a subset of inputs relevant to the application studied, and a complement subset of nuisance inputs that are not of interest. We propose sensitivity measures for the relevant inputs of such dynamic computer models. The subset of nuisance inputs is used to create replication-type information to help quantify the uncertainty of sensitivity measures (or indices) for the relevant inputs. The method is first demonstrated on an analytical example. Then we use the proposed method in an application about the safety of restraint systems in light tactical vehicles. The method indicates that chest deflection curves are more sensitive to the addition of pretensioners and load limiters than to the type of seatbelt.

References

References
1.
Cacuci
,
D. G.
,
Ionescu-Bujor
,
M.
, and
Navon
,
M.
,
2005
,
Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems
,
CRC Press
, Boca Raton, FL.
2.
Santner
,
T. J.
,
Williams
,
B. J.
, and
Notz
,
W. I.
,
2003
,
The Design and Analysis of Computer Experiments
,
Springer
,
New York
.
3.
Saltelli
,
A.
,
Tarantola
,
S.
,
Campolongo
,
F.
, and
Ratto
,
M.
,
2004
,
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
,
Wiley
, Chichester, UK.
4.
Fang
,
K. T.
,
Li
,
R.
, and
Sudjianto
,
A.
,
2007
,
Design and Modeling for Computer Experiments
,
Chapman and Hall
, Boca Raton, FL.
5.
Morris
,
M. D.
,
1991
, “
Factorial Sampling Plans for Preliminary Computational Experiments
,”
Technometrics
,
33
(
2
), pp.
161
174
.
6.
Kucherenko
,
S.
,
Rodriguez-Fernandez
,
M.
,
Pantelides
,
C.
, and
Shah
,
N.
,
2009
, “
Monte Carlo Evaluation of Derivative-Based Global Sensitivity Measures
,”
Reliab. Eng. Syst. Saf.
,
94
(
7
), pp.
1135
1148
.
7.
Vohra
,
M.
,
Alexanderian
,
A.
,
Safta
,
C.
, and
Mahadevan
,
S.
,
2018
, “
Sensitivity-Driven Adaptive Construction of Reduced-Space Surrogates
,”
J. Sci. Comput.
, (epub).
8.
Oakley
,
J. E.
, and
O'Hagan
,
A.
,
2004
, “
Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
,”
J. R. Stat. Soc. Ser. B
,
66
(
3
), pp.
751
769
.
9.
Oakley
,
J.
,
2009
, “
Decision-Theoretic Sensitivity Analysis for Complex Computer Models
,”
Technometrics
,
51
(
2
), pp.
121
129
.
10.
Liu
,
Y.
,
Yin
,
X.
,
Arendt
,
P.
,
Chen
,
W.
, and
Huang
,
H.-Z.
,
2010
, “
A Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
,”
ASME J. Mech. Des.
,
132
(
3
), p.
031006
.
11.
Jiang
,
Z.
,
German
,
B.
, and
Chen
,
W.
,
2016
, “
Multidisciplinary Statistical Sensitivity Analysis Considering Both Aleatory and Epistemic Uncertainties
,”
AIAA J.
,
54
(
4
), pp.
1326
1338
.
12.
Hu
,
Z.
, and
Mahadevan
,
S.
,
2018
, “
Probability Models for Data-Driven Global Sensitivity Analysis
,”
Reliab. Eng. Syst. Saf.
, (in Press).
13.
Fellini
,
R.
,
Kokkolaras
,
M.
,
Michelena
,
N. F.
,
Papalambros
,
P. Y.
,
Saitou
,
K.
,
Perez-Duarte
,
A.
, and
Fenyes
,
P.
,
2004
, “
A Sensitivity-Based Commonality Strategy for Family Products of Mild Variation, With Application to Automotive Body Structures
,”
Struct. Multidiscip. Optim.
,
27
(
1–2
), pp.
89
96
.
14.
Kucherenko
,
S.
, and
Iooss
,
B.
,
2017
, “
Derivative-Based Global Sensitivity Measures
,”
Handbook of Uncertainty Quantification
,
Springer
, Cham, Switzerland, pp.
1241
1263.
15.
Sarin
,
H.
,
Kokkolaras
,
M.
,
Hulbert
,
G. M.
,
Papalambros
,
P. Y.
,
Barbat
,
S.
, and
Yang
,
R.-J.
,
2010
, “
Comparing Time Histories for Validation of Simulation Models: Error Measures and Metrics
,”
ASME J. Dyn. Syst. Meas. Control
,
132
(6), p.
061401
.
16.
Campbell
,
K.
,
McKay
,
M.
, and
Williams
,
B.
,
2006
, “
Sensitivity Analysis When Model Outputs Are Functions
,”
Reliab. Eng. Syst. Saf.
,
91
(
10–11
), pp.
1468
1472
.
17.
Kennedy
,
M.
,
Anderson
,
C.
,
O'Hagan
,
A.
,
Lomas
,
M.
,
Woodward
,
I.
,
Gosling
,
J.
, and
Heinemeyer
,
A.
,
2008
, “
Quantifying Uncertainty in the Biospheric Carbon Flux for England and Wales
,”
J. R. Stat. Soc. Ser. A
,
171
, pp.
109
135
.
18.
Drignei
,
D.
,
2010
, “
Functional ANOVA in Computer Models With Time Series Output
,”
Technometrics
,
52
(
4
), pp.
430
437
.
19.
Drignei
,
D.
, and
Mourelatos
,
Z.
,
2012
, “
Parameter Screening in Statistical Dynamic Computer Model Calibration Using Global Sensitivities
,”
ASME J. Mech. Des.
,
134
(
8
), p.
081001
.
20.
Rohatgi
,
V. K.
,
1984
,
Statistical Inference
,
Wiley
,
New York
.
21.
Sacks
,
J.
,
Welch
,
W. J.
,
Mitchell
,
T. J.
, and
Wynn
,
H. P.
,
1989
, “
Design and Analysis of Computer Experiments
,”
Stat. Sci.
,
4
(
4
), pp.
409
423
.
22.
Vardeman
,
S.
, and
Jobe
,
M.
,
2001
,
Basic Engineering: Data Collection and Analysis
,
Brooks/Cole
,
Duxbury, MA
.
23.
LSTC
,
2019
, “
LS-DYNA
,” Livermore Software Technology Corporation, Livermore, CA, accessed Feb. 28, 2019, http://www.lstc.com/
24.
Reed
,
M. P.
, and
Ebert
,
S. M.
,
2013
, The Seated Soldier Study: Posture and Body Shape in Vehicle Seats, University of Michigan Transportation Research Institute, Ann Arbor, MI, Report No. UMTRI-2013-13.
25.
Drignei
,
D.
,
Mourelatos
,
Z.
,
Kosova
,
E.
,
Hu
,
J.
,
Reed
,
M.
,
Rupp
,
J.
,
Gruber
,
R.
, and
Scherer
,
R.
,
2016
, “
Uncertainty Assessment in Restraint System Optimization for Occupants of Tactical Vehicles
,”
SAE Int. J. Mater. Manuf.
,
9
, pp.
436
443
.
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