Mao | Model Validation and Uncertainty Quantification, Volume 3 | Buch | 978-3-030-77350-2 | sack.de

Buch, Englisch, 186 Seiten, Format (B × H): 210 mm x 280 mm, Gewicht: 494 g

Reihe: Conference Proceedings of the Society for Experimental Mechanics Series

Mao

Model Validation and Uncertainty Quantification, Volume 3

Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021
1. Auflage 2022
ISBN: 978-3-030-77350-2
Verlag: Springer International Publishing

Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021

Buch, Englisch, 186 Seiten, Format (B × H): 210 mm x 280 mm, Gewicht: 494 g

Reihe: Conference Proceedings of the Society for Experimental Mechanics Series

ISBN: 978-3-030-77350-2
Verlag: Springer International Publishing


Model Validation and Uncertainty Quantification, Volume 3:  Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the third volume of nine 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:

  • Inverse Problems and Uncertainty Quantification
  • Controlling Uncertainty
  • Validation of Models for Operating Environments
  • Model Validation & Uncertainty Quantification: Decision Making
  • Uncertainty Quantification in Structural Dynamics
  • Uncertainty in Early Stage Design
  • Computational and Uncertainty Quantification Tools
Mao Model Validation and Uncertainty Quantification, Volume 3 jetzt bestellen!

Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


Chapter 1. Effect of Inspection Errors in Optimal Maintenance Decisions for Deteriorating Quoin Blocks in Miter Gates.- Chapter 2. Model Uncertainty Quantification and Updating of a Boundary Condition Model of a Miter Gate Using Strain Measurements.- Chapter 3. Fusion of Test and Analysis: Artemis I Booster to Mobile Launcher Interface Validation.- Chapter 4. Quantifying the Benefits of Structural Health Monitoring using Value of Information and Decision Risk Modeling.- Chapter 5. Error Localization Examples: Looking for a Needle in a Haystack.- Chapter 6. WaveImage Bridges the Gap Between Measurement and Simulation. An Application Example of How to Create a Modal Digital Twin using FE Model Updating.- Chapter 7. Virtual Sensing for Wind Turbine Blade Full Field Response Estimation in Operational Modal Analysis.- Chapter 8. Dynamics of a Non-Linear Oscillator: Dependencies on Extrinsic Conditions and Model Form Uncertainty.- Chapter 9. Uncertainty Quantification of a Cantilevered Pipeline Conveying Fluid with Motion Limiting Constraints.- Chapter 10. Playability of a 1734 Guarneri Cello: Info-Gap Robustness Analysis of Uncertainty.- Chapter 11. Uncertainty Quantification of Axially-Loaded Beams with Boundary Condition Imperfections.- Chapter 12. Parameter Uncertainties Effects on the Buckling Characteristics of Cylindrical Structures in Thermal Environment.- Chapter 13. The Beginnings of an Error-Based Framework for Digital Twins of Dynamic Systems.- Chapter 14. Hierarchical Bayesian Model Updating for Nonlinear Structures Using Response Time Histories.- Chapter 15. SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method.- Chapter 16. A Forward Model Driven Structural Health Monitoring Paradigm: Damage Detection.- Chapter 17. Best Paper: Uncertainty Quantification of Inducer Eigenvalues using Conditional Assessment of Models and Modal Test of Simpler Systems.- Chapter 18. Application of Speaker Recognition x-Vectors to Structural Health Monitoring.- Chapter 19. Equation Discovery Using an Efficient Variational Bayesian Approach with Spike and Slab Priors.- Chapter 20. Bayesian Finite Element Model Updating Using an Improved Evolution Markov Chain Algorithm.- Chapter 21. Using Dead and Thermal Loads to Capture Behavioral Changes of a Cable-stayed Bridge.- Chapter 22. Vibration-Based Damage Detection Framework of Large Scale Structural Systems.


Zhu Mao, University of Massachusetts Lowell, MA, USA



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