 | Goves Y. Parameter identification of structural dynamic models by inverse statistical analysis: Diss … Dr.-Ing. / Deutsches Zentrum für Luft- und Raumfahrt, Institut für Aeroelastik, Göttingen. - Köln: DLR, Bibliotheks- und Informationswesen, 2012. - XXXII, 123 S. - (Forschungsbericht 2012-18). - Bibliogr.: S.113-123. - ISSN 1434-8454
|
Acknowledgements .............................................. vii
Abstract ....................................................... ix
Zusammenfassung ................................................ xi
Kurzfassung .................................................. xiii
List of Figures ............................................... xxv
List of Tables .............................................. xxvii
List of Symbols .............................................. xxix
1 Introduction ................................................. 1
1.1 Motivation and objectives ............................... 1
1.2 Organisation of the text ................................ 2
1.3 Research contributions and originalities ................ 3
2 Literature Review ............................................ 5
2.1 Finite element and stochastic simulation methods ........ 5
2.2 Computational model updating ............................ 7
2.3 Uncertainty in experimental modal data .................. 8
2.4 Stochastic model updating ............................... 9
3 Basic theoretical background for EMA and statistics ......... 11
3.1 Experimental modal analysis (EMA) ...................... 11
3.1.1 Phase Resonance Method (PRM) .................... 12
3.1.2 Phase Separation Method (PSM) ................... 15
3.1.3 Fully automated modal parameter selection
(FAMPS) ......................................... 19
3.2 Quality criteria for modal parameters .................. 20
3.2.1 Modal Phase Collinearity (MPC) .................. 22
3.2.2 Mean Phase Deviation (MPD) ...................... 23
3.2.3 Mode Indicator Function (MIF) ................... 23
3.3 Correlation of modal properties ........................ 24
3.3.1 Modal Scale Factor (MSF) ........................ 24
3.3.2 Modal Assurance Criterion (MAC) ................. 25
3.3.3 Realisation of complex mode shapes .............. 26
3.3.4 Examples for correlation of modal properties .... 26
3.4 Errors, probability and statistics ..................... 27
3.4.1 Probability density and distribution
functions ....................................... 29
3.4.2 Random variables ................................ 31
3.4.3 Sampling techniques for Monte Carlo
Simulation ...................................... 33
4 Uncertainty and variability in experimental modal data ...... 37
4.1 Introduction ........................................... 37
4.2 Case study 1: Sources of variability on modal
parameters ............................................. 41
4.2.1 Experimental set-up for case study .............. 42
4.2.2 Quantification and classification of test
scatter sources ................................. 46
4.3 Case study 2: Repeated modal survey test by
reassembling the joints ................................ 49
4.3.1 Data analysis for variability from
reassembling .................................... 49
4.3.2 Eigenfrequency variability from reassembling .... 51
4.3.3 Mode shape variability from reassembling ........ 52
5 Stochastic model updating ................................... 57
5.1 Introduction ........................................... 57
5.2 Parameter adjustment ................................... 58
5.2.1 Mean parameter adjustment ....................... 58
5.2.2 Parameter covariance matrix adjustment .......... 60
5.2.3 Error localisation .............................. 62
5.3 Sensitivity analysis and residuals ..................... 62
5.3.1 Choice of residuals ............................. 63
5.3.2 Eigenvalue and mode shape sensitivity ........... 64
5.3.3 Combination of different residuals .............. 66
5.4 Implementation of updating algorithm ................... 67
5.4.1 Weighted residual ............................... 68
5.4.2 Iteration bounds ................................ 68
5.4.3 Sample size for Monte Carlo Simulation .......... 70
5.4.4 Stop criteria ................................... 70
6 Test cases for validation of SMU algorithm .................. 73
6.1 Introduction ........................................... 73
6.2 Test case 1: numerical 3-DoF example ................... 73
6.2.1 Test case 1.1: Consistent parameter set ......... 75
6.2.2 Test case 1.2: Inconsistent parameter set ....... 77
6.2.3 Discussion of test case 1 ....................... 78
6.2.4 Comparison to other SMU methods for test
case 1 .......................................... 80
6.3 Test case 2: Numerical case study for AIRMOD model ..... 81
6.3.1 AIRMOD finite element model for numerical case
study ........................................... 82
6.3.2 Parameter selection and adjustment .............. 83
6.3.3 Test case 2.1: Consistent parameter set ......... 83
6.3.4 Test case 2.2: Inconsistent parameter set ....... 85
6.3.5 Discussion of test case 2 ....................... 88
6.4 Test case 3: Real test data from AIRMOD and a shell
model .................................................. 90
6.4.1 AIRMOD finite element model for real test
case (shell-model) .............................. 90
6.4.2 Parameter selection and residual definition
for updating .................................... 92
6.4.3 Discussion of test case 3 ....................... 94
6.5 Test case 4: Real test data from AIRMOD and a solid
model .................................................. 96
6.5.1 AIRMOD finite element model for real test case
(solid-model) ................................... 96
6.5.2 Parameter selection and residual definition
for updating .................................... 99
6.5.3 Discussion of test case 4 ...................... 100
6.6 Discussion of validation strategy ..................... 105
7 Conclusions and outlook .................................... 109
7.1 Summary ............................................... 109
7.2 Future work ........................................... 111
|
|
