In order to perform the accurate tuning of a machine and improve its performance to the requested tasks, the knowledge of the reciprocal influence among the system's parameters is of paramount importance to achieve the sought result with minimum effort and time. Numerical simulations are an invaluable tool to carry out the system optimization, but modeling limitations restrict the capabilities of this approach. On the other side, real tests and measurements are lengthy, expensive, and not always feasible. This is the reason why a mixed approach is presented in this work. The combination, through recursive cokriging, of low-fidelity, yet extensive, numerical model results, together with a limited number of highly accurate experimental measurements, allows to understand the dynamics of the machine in an extended and accurate way. The results of a controllable experiment are presented and the advantages and drawbacks of the proposed approach are also discussed.

References

References
1.
Fernandez-Godino
,
M. G.
,
Park
,
C.
,
Kim
,
N.-H.
, and
Haftka
,
R. T.
,
2017
, “
Review of Multi-Fidelity Models
,” e-print
arXiv: 1609.07196
.https://arxiv.org/abs/1609.07196
2.
Friswell
,
M. I.
, and
Mottershead
,
J. E.
,
1995
,
Finite Element Model Updating in Structural Dynamics. Solid Mechanics and Its Applications
,
Kluwer Academic Publishers Group
, Dordrecht, The Netherlands.
3.
Lucifredi
,
A.
,
Mazzieri
,
C.
, and
Rossi
,
M.
,
2000
, “
Application of Multiregressive Linear Models, Dynamic Kriging Models and Neural Network Models to Predictive Maintenance of Hydroelectric Power Systems
,”
Mech. Syst. Signal Process.
,
14
(
3
), pp.
471
494
.
4.
Liu
,
X.
,
Li
,
M.
, and
Xu
,
M.
,
2016
, “
Kriging Assisted On-Line Torque Calculation for Brushless DC Motors Used in Electric Vehicles
,”
Int. J Automot. Technol.
,
17
(
1
), pp.
153
164
.
5.
Krige
,
D. G.
,
1951
, “
A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand
,”
J. Southern Afr. Inst. Min. Metall.
,
52
(
6
), pp.
119
139
.
6.
Matheron
,
G.
,
1963
, “
Principles of Geostatistics
,”
Econ. Geol.
,
58
(
8
), pp.
1246
1266
.
7.
Sacks
,
J.
,
Welch
,
W.
,
J.
,
Toby
,
M.
, and
Wynn
,
H. P.
,
1989
, “
Design and Analysis of Computer Experiments
,”
Stat. Sci.
,
4
(
4
), pp.
409
423
.
8.
Kennedy
,
M. C.
, and
O'Hagan
,
A.
,
2000
, “
Predicting the Output From a Complex Computer Code When Fast Approximations are Available
,”
Biometrika
,
87
(
1
), pp.
1
13
.
9.
Gratiét
,
L. L.
, and
Cannamela
,
C.
,
2015
, “
Kriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques With Extensions to Multi-Fidelity Computer Codes
,”
Technometrics
,
57
(
3
), pp.
418
427
.
10.
Gratiét
,
L. L.
,
2012
, “
Bayesian Analysis of Hierarchical Multi-Fidelity Codes
,”
SIAM J. Uncertainty Quantif.
,
1
(
1
), pp.
244
269
.
11.
Gratiét
,
L. L.
,
2013
, “
Multi-Fidelity Gaussian Process Regression for Computer Experiments
,” Ph.D. thesis, University of Paris VII Denis-Diderot, Paris, France.
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