Model Validation and Uncertainty Quantification, Volume 3 (eBook)
IX, 378 Seiten
Springer International Publishing (Verlag)
978-3-319-54858-6 (ISBN)
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:
Uncertainty Quantification in Material Models
Uncertainty Propagation in Structural Dynamics
Practical Applications of MVUQ
Advances in Model Validation & Uncertainty Quantification: Model Updating
Model Validation & Uncertainty Quantification: Industrial Applications
Controlling Uncertainty
Uncertainty in Early Stage Design
Modeling of Musical Instruments
Overview of Model Validation and UncertaintyPreface 5
Contents 6
1 Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports Subject to Varying Axial Tensile and Compressive Loads 9
1.1 Introduction 9
1.2 System Description 10
1.3 The Beam's First Mode Eigenfrequency and Coupling Coefficient for Varying Axial Loads 12
1.3.1 Transducer Receptance Model 12
1.3.2 Transducer Receptance Model Fit 13
1.3.3 Experimental Results of the First Eigenfrequency and Coupling Coefficient for Varying Axial Loads 14
1.4 Experimental Vibration Attenuation with RL- and RLC-Shunt for Varying Axial Loads 15
1.5 Conclusion 16
References 16
2 Correlation of Non-contact Full-Field Dynamic Strain Measurements with Finite Element Predictions 17
2.1 Introduction 17
2.2 Measurement Campaign 18
2.2.1 Measurement System 18
2.2.2 Test Hardware 20
2.2.3 Test Environment and Setup 20
2.3 FE Model and Test Planning 20
2.4 Correlation of Mode Shapes 21
2.5 Correlation of Full-Field Strain Measurements 23
2.6 Concluding remarks 27
References 28
3 Nonlinear Prediction Surfaces for Estimating the Structural Response of Naval Vessels 29
3.1 Introduction 29
3.2 Available Data and Analysis 30
3.3 Development of Theoretical Prediction Surfaces 30
3.3.1 Operational Conditions and Theoretical Response 31
3.3.2 Development of Functional Forms 32
3.4 Application and Results 33
3.5 Conclusions 35
References 36
4 A Case Study in Predictive Modeling Beyond the Calibration Domain 37
4.1 Introduction 37
4.2 Testing of a Mass-Spring System to Develop a Model that Predicts the Lifted Weight 38
4.3 Testing of a Single Propeller to Develop a Model that Predicts the Lifting Force 40
4.4 Assessment of the Quadcopter Lifting Capacity Model in the Forecasting Regime 42
4.5 Conclusion 44
References 45
5 A Brief Overview of Code and Solution Verification in Numerical Simulation 46
5.1 Introduction 46
5.2 The Consistency and Convergence of Modified Equations 47
5.3 The Regime of Asymptotic Convergence of Discrete Solutions 49
5.4 State-of-the-Practice of Code and Solution Verification 50
5.5 The Bounds of Numerical Uncertainty 51
5.6 An Application of Solution Verification to a One-dimensional Advection Solver 52
5.7 Conclusion 55
References 56
6 Robust Optimization of Shunted Piezoelectric Transducers for Vibration Attenuation Considering Different Values of Electromechanical Coupling 58
6.1 Introduction 58
6.2 Frequency Transfer Function of the Single Mass Oscillator 59
6.3 Robust Optimization Approach 61
6.4 Numerical Results 63
6.5 Conclusion 65
References 65
7 Parameter Estimation and Uncertainty Quantification of a Subframe with Mass Loaded Bushings 67
7.1 Introduction 67
7.2 Theory 69
7.2.1 Deterministic Model Updating Procedure 70
7.2.2 Model Parameter Uncertainties 71
7.3 Model Preparation 72
7.3.1 Experimental Modal Analysis 72
7.3.2 Finite Element Models 74
7.3.3 Parameter Selection 74
7.4 Calibration and Validation Results 75
7.5 Conclusions 81
References 81
8 Vibroacoutsic Modelling of Piano Soundboards through Analytical Approaches in Frequency and Time Domains 83
References 86
9 Combined Experimental and Numerical Investigation of Vibro-Mechanical Properties of Varnished Wood for Stringed Instruments 87
9.1 Introduction 87
9.2 Approach 88
9.3 Conclusions 89
References 89
10 Towards Robust Sustainable System Design: An Engineering Inspired Approach 90
Nomenclature 90
Abbreviations 90
Accent 91
Roman Symbols 91
Greek Symbols 92
Greek Symbols 92
10.1 Introduction 92
10.2 Multi-Pole System Analysis 93
10.2.1 System Synthesis 93
10.2.2 Analysis Under Uncertainty 94
10.2.2.1 Energetic System Analysis 95
10.2.2.2 Economic System Analysis 96
10.2.2.3 Uncertainty Assignment 98
10.2.3 Stochastic Optimization 100
10.2.4 Sensitivity Analysis 102
10.3 Conclusion 104
References 104
11 Linear Parameter-Varying (LPV) Buckling Control of an Imperfect Beam-Column Subject to Time-Varying Axial Loads 107
11.1 Introduction 107
11.2 System Description and Mathematical Model of Beam-Column System 108
11.2.1 Finite Element State Space Model of Beam-Column System 110
11.2.2 System Identification and Model Validation of Beam-Column System 111
11.3 Reduced State Space Control Model and LPV Control 112
11.3.1 Modal State Space Control Model 112
11.3.2 Quadratically Stable Gain-Scheduled LPV Control 113
11.4 Experimental Results for Active Buckling Control 114
11.5 Conclusion 115
References 116
12 Quantification and Evaluation of Uncertainty in the Mathematical Modelling of a Suspension Strut Using Bayesian Model Validation Approach 117
12.1 Introduction 117
12.2 Suspension Strut MAFDS 118
12.3 Simplified 2DOF Mathematical Models of MAFDS 118
12.3.1 Motivation 118
12.3.2 Five Different Approaches to Model the Stiffness and the Damping 119
12.3.3 Solution of the Relative Compression Response of the 2DOF Mathematical Models (a) to (e) 121
12.4 Experimental Setup of MAFDS 122
12.5 Approach to Evaluate Model Uncertainty 124
12.5.1 Estimation of Posterior Probability of zr,max 124
Prior Probability p(Hzr,max,n) 124
Likelihood p(Azr,max | Hzr,max,n) 125
Total Probability p(Azr,max) 125
Posterior Probability p(Hzr,max,n|Azr,max) 125
12.5.2 Bayes Factor 126
12.5.3 Quantification of Uncertainty of Mathematical Models (a) to (e) 126
Deterministic Comparison of zr,max 126
Comparison of B for Mathematical Models (a) to (e) 127
12.6 Conclusion and Outlook 127
References 128
13 Unsupervised Novelty Detection Techniques for Structural Damage Localization: A Comparative Study 129
13.1 Introduction 129
13.2 Literature Review 130
13.3 Methodologies 130
13.3.1 GM Method 130
13.3.2 OC-SVM 131
13.3.3 Density Peaks-Based fast Clustering 131
13.4 Damage-Sensitive Features 132
13.4.1 Crest Factor 132
13.4.2 Transmissibility 133
13.5 Experimental Setup 133
13.6 Comparative Case Studies 134
13.7 Conclusion 135
References 136
14 Global Load Path Adaption in a Simple Kinematic Load-Bearing Structure to Compensate Uncertainty of Misalignment Due to Changing Stiffness Conditions of the Structure's Supports 137
14.1 Introduction 137
14.2 Truss Structure Example MAFDS 138
14.3 Mathematical Model of the 2D Two Mass Oscillator 139
14.3.1 Internal and External Forces 140
14.3.2 Equation of Motion System 141
14.3.3 LuGre Friction Model 142
14.3.4 Controller for Semi-active Friction Force 143
14.3.5 State Space Model with Control 143
14.4 Numerical Simulation of Load Path Adaption 144
14.5 Conclusion 147
References 148
15 Assessment of Uncertainty Quantification of Bolted Joint Performance 149
15.1 Introduction 149
15.1.1 Uncertainties in Theoretical Predictions 149
15.1.2 Uncertainties in Experiments 150
15.1.3 Uncertainties Comparison Between Theoretical Predictions Using Experimental Data 151
15.1.4 Introduction of Variation of Uncertainties in Testing 153
15.1.5 Control of Uncertainty by Varying Torque Applied to Structural Bolts 154
15.1.6 Limits of Control of Uncertainties 155
15.2 Literature Review 159
15.3 Conclusions 160
References 161
16 Sensitivity Analysis and Bayesian Calibration for 2014 Sandia Verification and Validation Challenge Problem 162
17 Non-probabilistic Uncertainty Evaluation in the Concept Phase for Airplane Landing Gear Design 164
17.1 Introduction 164
17.2 Info-Gap Theory 165
17.3 Uncertainty Quantification in Landing Gear Design Concepts via Info-Gap Approach 166
17.3.1 Guideline 166
17.3.2 Mathematical Modeling and Achieving Comparability Between the Concepts 167
17.3.2.1 Achieving Comparability Between the Concepts 168
17.3.2.2 Selected Properties for Comparing the Concepts' Compression Stroke Capability Under Uncertainty 168
17.3.2.3 Deterministic Comparison of Static Compression Stroke Behavior 169
17.3.3 Uncertainty Model 170
17.3.4 Performance Requirement 170
17.3.5 Robustness to Uncertainty 170
17.4 Conclusion 172
References 172
18 Modular Analysis of Complex Systems with Numerically Described Multidimensional Probability Distributions 173
18.1 Introduction 173
18.2 Development of Research 174
18.3 General Considerations on Systems and Modules 174
18.4 Using Probability Values 175
18.5 Numerical Described Multidimensional Probability Distributions (NDMPD) 175
18.6 Active and Passive Elements, Modules and Interfaces 176
18.6.1 Working with NDMPD as Description of System Behavior 177
18.6.2 Database of Knowledge 178
18.6.3 Summary and Conclusions 178
References 178
19 Methods for Component Mode Synthesis Model Generation for Uncertainty Quantification 179
19.1 Introduction 179
19.2 A Brief Review of Craig-Bampton Reduced Order Models 180
19.2.1 Model Generation and DOF Identification 180
19.2.2 Fixed-Interface Modes 180
19.2.3 Constraint Modes 180
19.2.4 Craig-Bampton Transformation Matrix 181
19.2.5 Reduced Stiffness and Mass Matrices 181
19.3 Craig-Bampton Generation for UQ Studies 181
19.3.1 REMAP Technique 181
19.3.2 COMP Technique 182
19.4 Application of REMAP and COMP Techniques 182
19.5 Conclusions 189
References 189
20 Parameterization of Large Variability Using the Hyper-Dual Meta-model 191
20.1 Motivation 191
20.2 Hyper-Dual Meta-model Formulation 192
20.2.1 Analytical Example 192
20.3 Determining Parameter Sensitivity 193
20.3.1 Finite Difference 194
20.3.2 Complex and Multi-complex Step 195
20.3.3 Hyper-Dual 196
20.3.4 Comparison of Methods 197
20.4 Selection of Basis Function 198
20.5 Numerical Examples 198
20.5.1 Brake-Reuß Beam 199
20.5.1.1 Small Parameter Sweep 199
20.5.1.2 Large Parameter Sweep 201
20.5.1.3 Distribution Propagation 202
20.5.2 Geometric Change 203
20.5.2.1 Reliability Analysis 204
20.5.2.2 Design Analysis 207
20.6 Conclusions 209
References 209
21 Similitude Analysis of the Frequency Response Function for Scaled Structures 211
21.1 Introduction 211
21.2 Governing Equations 213
21.3 Experimental Results 214
21.4 Conclusions 218
References 218
22 MPUQ-b: Bootstrapping Based Modal Parameter Uncertainty Quantification—Fundamental Principles 220
Abbreviations 220
Nomenclature 221
22.1 Introduction 221
22.2 Bootstrapping 222
22.2.1 Basic Principles and Procedure 222
22.2.2 Advantages and Limitations 224
22.3 Studies on a SDOF System 225
22.3.1 SDOF System 225
22.3.2 Numerical Study: Design and Procedure 225
22.4 Results and Analysis 228
22.4.1 Effect of Number of Averages 229
22.4.2 Effect of Frequency Resolution 233
22.4.3 Effect of Noise 234
22.5 Conclusions 237
References 237
23 MPUQ-b: Bootstrapping Based Modal Parameter Uncertainty Quantification—Methodology and Application 239
Abbreviations 239
Nomenclature 239
23.1 Introduction 240
23.2 MPUQ-b: Bootstrapping Based Modal Parameter Uncertainty Quantification 242
23.2.1 Procedure 242
23.2.2 Features 244
23.3 Validation Studies on a Numerical System 245
23.3.1 Description of Numerical Experiment 245
23.3.2 Results and Discussions 246
23.3.2.1 Quantitative Analysis 247
23.3.2.2 Qualitative Analysis 248
23.3.2.3 Normality Checks 250
23.3.2.4 Comparison with Monte Carlo Simulations 251
23.4 Conclusions 253
References 253
24 Evaluation of Truck-Induced Vibrations for a Multi-Beam Highway Bridge 255
24.1 Introduction 255
24.2 Test Structure and Experimental Program 256
24.3 Data Analysis and Results 257
24.3.1 RMS Analysis 258
24.4 Conclusions and Future Work 260
References 261
25 Innovations and Info-Gaps: An Overview 262
25.1 Info-Gap Theory: A First Look 262
25.2 Gap-Closing Electrostatic Actuators 263
25.3 Conclusion 270
References 270
26 Bayesian Optimal Experimental Design Using Asymptotic Approximations 271
26.1 Optimal Experimental Design 271
26.2 Applications 272
26.3 Conclusions 273
References 273
27 Surrogate-Based Approach to Calculate the Bayes Factor 274
27.1 Introduction 274
27.2 Methodology 275
27.3 Example 275
27.3.1 Problem Definition 275
27.3.2 Monte Carlo Estimate 276
27.3.3 Proposed Method 276
27.3.4 Concluding Remarks 276
References 278
28 Vibrational Model Updating of Electric Motor Stator for Vibration and Noise Prediction 279
28.1 Introduction 279
28.2 Multiphysical Model 280
28.2.1 Vibrational Model 281
28.3 Experimental Campaign 281
28.4 Baseline Model Definition 281
28.5 Anisotropic Damping 283
28.6 Operational Correlation and Updating Analysis 284
28.6.1 Frequency Response Calibration Metrics 284
28.6.1.1 Frequency Response Assurance Criterion (FRAC) 284
28.6.1.2 Square Deviation (SD) 284
28.6.1.3 Mean Squared Error (MSE) 285
28.6.1.4 Correlation Metric Selection 285
28.6.2 Construction Parameters and Boundary Conditions 285
28.6.3 Sensitivity Analysis of Damping Parameters 285
28.6.4 Surrogate Models 286
28.6.5 FRF Updating 287
28.7 Conclusions 288
References 288
29 A Comparison of Computer-Vision-Based Structural Dynamics Characterizations 290
29.1 Introduction 290
29.2 Experimental Test-Setup 291
29.3 Iterative Lucas-Kanade Optical Flow Estimation and Point Tracking 291
29.4 Hungarian Registration Algorithm 293
29.5 Particle Filters for Point Tracking 294
29.6 Conclusion 295
References 296
30 Sequential Gauss-Newton MCMC Algorithm for High-Dimensional Bayesian Model Updating 297
30.1 Introduction 297
30.1.1 Sequential MCMC Algorithm 298
30.1.2 Importance Resampling 299
30.1.3 MCMC Sampling 300
30.1.3.1 Hessian Informed Metropolis-Hastings Algorithm 301
30.1.3.2 Gauss-Newton Approximation of Hessian 302
30.1.4 Summary of the Sequential Gauss-Newton Algorithm 303
30.2 Illustrative Examples 304
30.3 Numerical Results and Discussion 304
30.4 Conclusion 307
References 307
31 Model Calibration with Big Data 309
31.1 Introduction 309
31.2 Background 310
31.2.1 Bayesian Calibration 310
31.2.2 Gaussian Process (GP) Surrogate Model 310
31.2.3 MapReduce Framework 311
31.3 Proposed Methodology 312
31.4 Numerical Example 313
31.4.1 Experimental Setup 313
31.4.2 Data Processing 313
31.4.3 Finite Element Model 314
31.4.4 Surrogate Model Training 314
31.4.5 Calibration 314
31.5 Conclusion 315
References 315
32 Towards Reducing Prediction Uncertainties in Human Spine Finite Element Response: In-Vivo Characterization of Growth and Spine Morphology 317
32.1 Introduction 317
32.2 Methods 318
32.3 Results 318
32.4 Discussion 321
32.5 Concluding Remarks 322
References 323
33 Structural Damage Detection Using Convolutional Neural Networks 324
33.1 Introduction 324
33.2 Deep Neural Networks 325
33.3 Data Preparation 326
33.4 Proposed Architecture for Damage Detection 327
33.4.1 CNN Architecture 327
33.4.2 Training 328
33.4.3 Results 328
33.5 Conclusion 329
References 330
34 Experimental Model Validation of an Aero-Engine Casing Assembly 331
34.1 Introduction 331
34.2 Experimental Setup 332
34.3 Finite Element Modelling of the Aero-Engine Casin Assembly 333
34.4 Conclusions 336
A.1 Appendix 337
References 339
35 Damage Detection in Railway Bridges Under Moving Train Load 340
35.1 Introduction 340
35.2 Numerical Simulation 341
35.2.1 Finite Element Model 341
35.2.2 Effects of Operational Variability on Modal Properties of Train-Bridge System 341
35.3 Signal Energy Based Damage Detection 343
35.3.1 Normalization of Signal Energy 343
35.3.2 Identification of Damage 343
35.4 Results 343
35.5 Conclusions 344
References 344
36 Multi-Fidelity Calibration of Input-Dependent Model Parameters 346
36.1 Background 346
36.1.1 Non-Linearity in Structural Dynamics 346
36.1.2 Damping Calibration 347
36.1.3 Bayesian Calibration 347
36.1.4 Surrogate Models 347
36.1.5 Model Calibration Under Uncertainty 347
36.2 Multi-Fidelity Calibration Method for Input-Dependent System Parameters 348
36.3 Numerical Example 349
36.3.1 Problem Description 349
36.3.2 Results 350
36.3.3 Discussion 353
36.4 Conclusion 353
References 353
37 Empirically Improving Model Adequacy in Scientific Computing 354
37.1 Introduction 354
37.2 Current State of the Art in Calibration of Models Against Experiments 355
37.3 Research and Methods 356
37.3.1 Methodology: Statistically Rigorous Framework for Model Calibration 356
37.3.2 Gaussian Process Models for Emulating ?(=·) 357
37.3.3 Gaussian Process Models for Emulating (=·,=·) 357
37.4 Conceptual Demonstration 358
37.5 Discussion and Conclusion 359
References 360
38 Mixed Geometrical-Material Sensitivity Analysis for the Study of Complex Phenomena in Musical Acoustics 361
38.1 Introduction 361
38.2 Numerical Modeling of Violin 362
38.3 Results and Conclusion 363
References 364
39 Experimental Examples for Identification of Structural Systems Using Degree of Freedom-Based Reduction Method 365
39.1 Introduction 365
39.2 Experiment 366
39.3 Analysis 366
39.4 Summary 367
References 367
| Erscheint lt. Verlag | 7.6.2017 |
|---|---|
| Reihe/Serie | Conference Proceedings of the Society for Experimental Mechanics Series | Conference Proceedings of the Society for Experimental Mechanics Series |
| Zusatzinfo | IX, 378 p. 239 illus., 184 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Themenwelt | Naturwissenschaften ► Physik / Astronomie |
| Technik ► Maschinenbau | |
| Wirtschaft ► Betriebswirtschaft / Management | |
| Schlagworte | Controlling Uncertainty • IMAC 2017 Proceedings • Modeling of Musical Instruments • Overview of Model Validation and Uncertainty • Practical Applications of MVUQ • Quality Control, Reliability, Safety and Risk • Uncertainty in Early Stage Design • Uncertainty Propagation in Structural Dynamics • Uncertainty Quantification in Material Models |
| ISBN-10 | 3-319-54858-1 / 3319548581 |
| ISBN-13 | 978-3-319-54858-6 / 9783319548586 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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