| Preface |
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 δ(=·) |
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 |