The coordinate orthogonality check (CORTHOG) and multi-objective optimization considering pseudo-orthogonality as an objective function are introduced to overcome several limitations present in current model updating methods. It was observed that the use of the CORTHOG to remove four inaccurate degrees-of-freedom (DOF) was able to increase the orthogonality between mode shape vectors. The multi-objective model updating process generated a Pareto front with 38 unique optimal solutions. Four critical points were identified along the Pareto front, of which decreased the natural frequency error by greater than 2.84% and further increased the orthogonality between mode shape vectors. Therefore, it has been demonstrated that both steps of the methodology are critical to significantly reduce the overall errors of the system and to generate a finite element (FE) model that best describes physical reality. Additionally, the methodology introduced in this work generated a feasible computational runtime allowing for it to be easily adapted to widespread applications.

References

1.
Avitabile
,
P.
, and
Pechinsky
,
F.
,
1998
, “
The Coordinate Orthogonality Check (CORTHOG)
,”
Mech. Syst. Signal Process.
,
12
(
3
), pp.
395
414
.
2.
Allemang
,
R. J.
,
2003
, “
The Modal Assurance Criterion Twenty Years of Use and Abuse
,”
J. Sound Vib.
,
37
(
8
), pp.
14
23
.http://www.sandv.com/downloads/0308alle.pdf
3.
Imregun
,
M.
, and
Visser
,
W. J.
,
1991
, “
A Review of Model Updating Techniques
,”
Shock Vib. Dig.
,
23
(
1
), pp.
9
20
.
4.
Mottershead
,
J. E.
, and
Friswell
,
M. I.
,
1993
, “
Model Updating in Structural Dynamics: A Survey
,”
J. Sound Vib.
,
167
(
2
), pp.
347
375
.
5.
Friswell
,
M.
, and
Mottershead
,
J. E.
,
2013
,
Finite Element Model Updating in Structural Dynamics
, Vol.
38
,
Springer
, Berlin.
6.
Ewins
,
D. J.
,
2000
,
Modal Testing: Theory and Practice
, Vol.
2
,
Research Studies Press
,
Letchworth, UK
.
7.
MathWorks
,
2017
,
MatLab Programming Fundamentals
,
The MathWorks
, Natick, MA.
8.
Mottershead
,
J. E.
,
Link
,
M.
, and
Friswell
,
M. I.
,
2011
, “
The Sensitivity Method in Finite Element Model Updating: A Tutorial
,”
J. Mech. Syst. Signal Process.
,
25
(
7
), pp.
2275
2296
.
9.
Modak
,
S. V.
,
Kundra
,
T. K.
, and
Nakra
,
B. C.
,
2000
, “
Model Updating Using Constrained Optimization
,”
J. Mech. Res. Commun.
,
27
(
5
), pp.
543
551
.
10.
Sehgal
,
S.
, and
Kumar
,
H.
,
2016
, “
Structural Dynamic Model Updating Techniques: A State of the Art Review
,”
Arch. Comput. Methods Eng.
,
23
(
3
), pp.
515
533
.
11.
Modak
,
S. V.
,
2014
, “
Model Updating Using Uncorrelated Modes
,”
J. Sound Vib.
,
333
(
11
), pp.
2297
2322
.
12.
Messac
,
A.
, and
Mattson
,
C. A.
,
2002
, “
Generating Well-Distributed Sets of Pareto Points for Engineering Design Using Physical Programming
,”
Optim. Eng.
,
3
(
4
), pp.
431
450
.
13.
Das
,
I.
, and
Dennis
,
J. E.
,
1997
, “
A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems
,”
Struct. Optim.
,
14
(
1
), pp.
63
69
.
14.
Koski
,
J.
,
1985
, “
Defectiveness of Weighting Method in Multi-Criterion Optimization of Structures
,”
Numer. Methods Biomed. Eng.
,
1
(
6
), pp.
333
337
.
15.
Kim
,
I. Y.
, and
De Weck
,
O. L.
,
2005
, “
Adaptive Weighted-Sum Method for Bi-Objective Optimization: Pareto Front Generation
,”
Struct. Multidiscip. Optim.
,
29
(
2
), pp.
149
158
.
16.
Kim
,
G.
, and
Park
,
Y. S.
,
2014
, “
An Improved Procedure for Updating Finite Element Model Based on an Interactive Multiobjective Programming
,”
Mech. Syst. Signal Process.
,
43
(
1–2
), pp.
260
271
.
17.
Nakayama
,
H.
, and
Furukawa
,
K.
,
1985
, “
Satisficing Trade-Off Method With an Application to Multiobjective Structural Design
,”
Large Scale Syst.
,
8
, pp.
47
57
.https://www.researchgate.net/publication/268853546_Satisficing_trade-off_method_with_an_application_to_multiobjective_structure_design
18.
Nakayama
,
H.
,
1999
,
Advances in Multicriteria Analysis: Aspiration Level Approach to Interactive Multi-Objective Programming and Its Applications
,
Kluwer Academic
, Boston, MA.
19.
Nakayama
,
H.
,
1992
, “
Trade-Off Analysis Using Parametric Optimization Techniques
,”
Eur. J. Oper. Res.
,
60
(
1
), pp.
87
98
.
20.
Konak
,
A.
,
Coit
,
D. W.
, and
Smith
,
A. E.
,
2006
, “
Multi-Objective Optimization Using Genetic Algorithms: A Tutorial
,”
Reliab. Eng. Syst. Saf.
,
91
(
9
), pp.
992
1007
.
21.
Deb
,
K.
,
2014
, “
Multi-Objective Optimization
,”
Search Methodologies
,
Springer
, Boston, MA.
22.
Deb
,
K.
,
2001
,
Multi-Objective Optimization Using Evolutionary Algorithms
, Vol.
16
,
Wiley
, Hoboken, NJ.
23.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithms in Search, Optimization, and Machine Learning
,
Addison-Wesley
, Boston, MA.
24.
Kim
,
I. Y.
, and
de Weck
,
O. L.
,
2005
, “
Variable Chromosome Length Genetic Algorithm for Progressive Refinement in Topology Optimization
,”
Struct. Multidiscip. Optim.
,
26
(
9
), pp.
445
456
.
25.
Kim
,
G. H.
, and
Park
,
Y. S.
,
2004
, “
An Improved Updating Parameter Selection Method and Finite Element Model Update Using Multiobjective Optimization Technique
,”
J. Mech. Syst. Signal Process.
,
18
(
1
), pp.
59
78
.
26.
Cheng
,
F. Y.
, and
Li
,
D.
,
1998
, “
Genetic Algorithm Development for Multiobjective Optimisation of Structures
,”
AIAA J.
,
36
(
6
), pp.
1105
1112
.
27.
Christodoulou
,
K.
,
Ntotsios
,
E.
,
Papadimitriou
,
C.
, and
Panetsos
,
P.
,
2008
, “
Structural Model Updating and Prediction Variability Using Pareto Optimal Models
,”
Comput. Methods Appl. Mech. Eng.
,
198
, pp.
18
149
.
28.
Jaishi
,
B.
, and
Ren
,
W. X.
,
2007
, “
Finite Element Model Updating Based on Eigenvalue and Strain Energy Residuals Using Multiobjective Optimization Technique
,”
Mech. Syst. Signal Process.
,
21
(
5
), pp.
2295
2317
.
29.
Jin
,
S. S.
,
Cho
,
S.
,
Jung
,
H. J.
,
Lee
,
J. J.
, and
Yun
,
C. B.
,
2014
, “
A New Multi-Objective Approach to Finite Element Model Updating
,”
J. Sound Vib.
,
333
(
11
), pp.
2323
2338
.
30.
Deb
,
K.
, and
Gupta
,
S.
,
2011
, “
Understanding Knee Point in Bi-Criteria Problems and Their Implications as Preferred Solution Principles
,”
Eng. Optim.
,
43
(
11
), pp.
1175
1204
.
31.
Deb
,
K.
,
Pratap
,
A.
,
Agarwal
,
S.
, and
Meyarivan
,
T.
,
2002
, “
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
,”
IEEE Trans. Evol. Comput.
,
6
(
2
), pp.
182
197
.
32.
Duff
,
I. S.
,
Grimes
,
R. G.
, and
Lewis
,
J. G.
,
1992
,
Users' Guide for the Harwell-Boeing Sparse Matrix Collection
,
Rutherford Appleton Library
,
Didcot, UK
, Report No. RAL-92-086.
33.
O'Callahan
,
J.
,
Avitabile
,
P.
, and
Riemer
,
R.
,
1989
, “
System Equivalent Reduction Expansion Process (SEREP)
,”
Seventh International Modal Analysis Conference
, Las Vegas, NV, Jan. 30–Feb. 2, pp.
29
37
.
34.
Guyan
,
R. J.
,
1965
, “
Reduction of Stiffness and Mass Matrices
,”
AIAA J.
,
3
(
2
), p.
380
.
35.
Avitabile
,
P.
,
Pechinsky
,
F.
, and
O'Callahan
,
J.
,
1992
, “
Study of Modal Vector Correlation Using Various Techniques for Model Reduction
,”
International Modal Analysis Conference
, San Diego, CA, Feb. 3–7, pp. 572–583.
36.
Avitabile
,
P.
, and
Pechinsky
,
F.
,
1994
, “
Coordinate Orthogonality Check (CORTHOG)
,”
12th International Modal Analysis Conference
, Honolulu, HI, Jan. 31–Feb. 3, p. 573.
37.
Foster
,
T.
,
1993
, “
Evaluation of DOF Selection for Modal Vector Correlation and Expansion Studies
,” M.Sc. thesis, University of Massachusetts Lowell, Lowell, MA.
38.
Mains
,
M.
, and
Vold
,
H.
,
1995
, “
Investigation of the Effects of Transducer Cross-Sensitivity and Misalignment Error on Modal Vector Correlation
,”
13th International Modal Analysis Conference
, Nashville, TN, Feb. 13–16, pp.
1048
1056
.
39.
Avitabile
,
P.
,
2017
,
Modal Testing: A Practitioner's Guide
,
Wiley
, Hoboken, NJ.
40.
Warwick
,
B. T.
,
Kim
,
I. Y.
, and
Mechefske
,
C. K.
,
2018
, “
Substructuring Verification of a Rear Fuselage Mounted Twin-Engine Aircraft
,”
J. Aerosp. Sci. Technol.
(in press).
41.
Warwick
,
B. T.
,
Mechefske
,
C. K.
, and
Kim
,
I. Y.
,
2018
, “
Effect of Stiffener Configuration on Bulkhead Modal Parameters
,”
ASME
Paper No. DETC2018-85385.
42.
MathWorks
,
2016
,
MatLab Optimization Toolbox—User's Guide
,
The MathWorks
, Natick, MA.
You do not currently have access to this content.