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From Microstructure Investigations to Multiscale Modeling : Bridging the Gap.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2018Copyright date: ©2017Edition: 1st edDescription: 1 online resource (297 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119484479
Subject(s): Genre/Form: Additional physical formats: Print version:: From Microstructure Investigations to Multiscale ModelingDDC classification:
  • 620.11299
LOC classification:
  • TA405 .F766 2017
Online resources:
Contents:
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Synchrotron Imaging and Diffraction for In Situ 3D Characterization of Polycrystalline Materials -- 1.1. Introduction -- 1.2. 3D X-ray characterization of structural materials -- 1.2.1. Early days of X-ray computed tomography -- 1.2.2. X-ray absorption and Beer Lambert's law -- 1.2.3. X-ray detection -- 1.2.4. Radon's transform and reconstruction -- 1.2.5. Synchrotron X-ray microtomography -- 1.2.6. Phase contrast tomography -- 1.2.7. Diffraction contrast tomography -- 1.3. Nanox: a miniature mechanical stress rig designed for near-field X-ray diffraction imaging techniques -- 1.4. Coupling diffraction contrast tomography with the finite-element method -- 1.4.1. Motivation for image-based mechanical computations -- 1.4.2. 3D mesh generation from tomographic images -- 1.4.3. Toward a fatigue model at the scale of the polycrystal -- 1.5. Conclusion and outlook -- 1.6. Bibliography -- 2. Determining the Probability of Occurrence of Rarely Occurring Microstructural Configurations for Titanium Dwell Fatigue -- 2.1. Introduction -- 2.2. Experimental methods -- 2.2.1. MTR quantification metrics -- 2.2.2. Synthetic microstructure generation -- 2.2.3. Crystallographic analysis for titanium dwell fatigue -- 2.2.4. Block maxima -- 2.3. Results and discussion -- 2.3.1. Probability of occurrence -- 2.3.2. "Hard" MTR size distributions -- 2.3.3. Block maxima -- 2.4. Summary and outlook -- 2.5. Bibliography -- 3. Wave Propagation Analysis in 2D Nonlinear Periodic Structures Prone to Mechanical Instabilities -- 3.1. Introduction -- 3.2. Extensible energy of pantograph for dynamic analysis -- 3.2.1. Expression of the pantographic network energy -- 3.2.2. Dynamic equilibrium equation -- 3.3. Wave propagation in a nonlinear elastic beam.
3.3.1. Legendre-Hadamard ellipticity condition and loss of stability -- 3.3.2. Supersonic and subsonic modes for 1D wave propagation -- 3.3.3. Wave dispersion relation in 2D nonlinear periodic structures -- 3.3.4. Anisotropic behavior of 2D pantographic networks versus the degree of nonlinearity -- 3.4. Conclusion -- 3.5. Appendix -- 3.6. Bibliography -- 4. Multiscale Model of Concrete Failure -- 4.1. Introduction -- 4.2. Meso-scale model -- 4.3. Macroscopic model response -- 4.3.1. Uniaxial tests -- 4.3.2. Failure surface -- 4.4. Conclusions -- 4.5. Acknowledgments -- 4.6. Bibliography -- 5. Discrete Numerical Simulations of the Strength and Microstructure Evolution During Compaction of Layered Granular Solids -- 5.1. Introduction -- 5.2. Numerical simulation -- 5.2.1. Discrete particle simulations of powder compaction -- 5.2.2. Discrete particle simulation of layered compacts -- 5.3. Discussion -- 5.4. Conclusion -- 5.5. Acknowledgements -- 5.6. Bibliography -- 6. Microstructural Views of Stresses in Three-Phase Granular Materials -- 6.1. Microstructural expression of triphasic total stresses -- 6.1.1. Stress description within micro-scale volumes and interfaces of triphasic materials -- 6.1.2. Total stress derivation -- 6.2. Numerical modeling of wet ideal granular materials -- 6.2.1. DEM description of fluid microstructure -- 6.2.2. DEM description of stress and strains -- 6.3. Anisotropy of the capillary stress contribution -- 6.3.1. Mechanical loading -- 6.3.2. Hydraulic loading -- 6.4. Effective stress -- 6.5. Conclusion -- 6.6. Bibliography -- 7. Effect of the Third Invariant of the Stress Deviator on the Response of Porous Solids with Pressure-Insensitive Matrix -- 7.1. Introduction -- 7.2. Problem statement and method of analysis -- 7.2.1. Drucker yield criterion for isotropic materials -- 7.2.2. Unit cell model -- 7.3. Results.
7.3.1. Yield surfaces and porosity evolution -- 7.4. Conclusions -- 7.5. Bibliography -- 8. High Performance Data-Driven Multiscale Inverse Constitutive Characterization of Composites -- 8.1. Introduction -- 8.2. Automated multi-axial testing -- 8.2.1. Loading space -- 8.2.2. Experimental campaign -- 8.3. Constitutive formalisms -- 8.3.1. Small strain formulation -- 8.3.2. Finite strain formulation -- 8.4. Meshless random grid method for experimental evaluation of strain fields -- 8.5. Inverse determination of HDM via design optimization -- 8.5.1. Numerical results of design optimization -- 8.6. Surrogate models for characterization -- 8.6.1. Definition and construction of the surrogate model -- 8.6.2. Characterization by optimization -- 8.6.3. Validation with physical experiments -- 8.7. Multi-scale inversion -- 8.7.1. Forward problem: mathematical homogenization -- 8.7.2. Inverse problem -- 8.8. Computational framework and synthetic experiments -- 8.9. Conclusions and plans -- 8.10. Acknowledgments -- 8.11. Bibliography -- 9. New Trends in Computational Mechanics: Model Order Reduction, Manifold Learning and Data-Driven -- 9.1. Introduction -- 9.1.1. The big picture -- 9.1.2. The PGD at a glance -- 9.2. Constructing slow manifolds -- 9.2.1. From principal component analysis (PCA) to kernel principal component analysis (kPCA) -- 9.2.2. Kernel principal component analysis (kPCA) -- 9.2.3. Locally linear embedding (LLE) -- 9.2.4. Discussion -- 9.3. Manifold-learning-based computational mechanics -- 9.4. Data-driven simulations -- 9.4.1. Data-based weak form -- 9.4.2. Constructing the constitutive manifold -- 9.5. Data-driven upscaling of viscous flows in porous media -- 9.5.1. Upscaling Newtonian and generalized Newtonian fluids flowing in porous media -- 9.6. Conclusions -- 9.7. Bibliography -- List of Authors -- Index.
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Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Synchrotron Imaging and Diffraction for In Situ 3D Characterization of Polycrystalline Materials -- 1.1. Introduction -- 1.2. 3D X-ray characterization of structural materials -- 1.2.1. Early days of X-ray computed tomography -- 1.2.2. X-ray absorption and Beer Lambert's law -- 1.2.3. X-ray detection -- 1.2.4. Radon's transform and reconstruction -- 1.2.5. Synchrotron X-ray microtomography -- 1.2.6. Phase contrast tomography -- 1.2.7. Diffraction contrast tomography -- 1.3. Nanox: a miniature mechanical stress rig designed for near-field X-ray diffraction imaging techniques -- 1.4. Coupling diffraction contrast tomography with the finite-element method -- 1.4.1. Motivation for image-based mechanical computations -- 1.4.2. 3D mesh generation from tomographic images -- 1.4.3. Toward a fatigue model at the scale of the polycrystal -- 1.5. Conclusion and outlook -- 1.6. Bibliography -- 2. Determining the Probability of Occurrence of Rarely Occurring Microstructural Configurations for Titanium Dwell Fatigue -- 2.1. Introduction -- 2.2. Experimental methods -- 2.2.1. MTR quantification metrics -- 2.2.2. Synthetic microstructure generation -- 2.2.3. Crystallographic analysis for titanium dwell fatigue -- 2.2.4. Block maxima -- 2.3. Results and discussion -- 2.3.1. Probability of occurrence -- 2.3.2. "Hard" MTR size distributions -- 2.3.3. Block maxima -- 2.4. Summary and outlook -- 2.5. Bibliography -- 3. Wave Propagation Analysis in 2D Nonlinear Periodic Structures Prone to Mechanical Instabilities -- 3.1. Introduction -- 3.2. Extensible energy of pantograph for dynamic analysis -- 3.2.1. Expression of the pantographic network energy -- 3.2.2. Dynamic equilibrium equation -- 3.3. Wave propagation in a nonlinear elastic beam.

3.3.1. Legendre-Hadamard ellipticity condition and loss of stability -- 3.3.2. Supersonic and subsonic modes for 1D wave propagation -- 3.3.3. Wave dispersion relation in 2D nonlinear periodic structures -- 3.3.4. Anisotropic behavior of 2D pantographic networks versus the degree of nonlinearity -- 3.4. Conclusion -- 3.5. Appendix -- 3.6. Bibliography -- 4. Multiscale Model of Concrete Failure -- 4.1. Introduction -- 4.2. Meso-scale model -- 4.3. Macroscopic model response -- 4.3.1. Uniaxial tests -- 4.3.2. Failure surface -- 4.4. Conclusions -- 4.5. Acknowledgments -- 4.6. Bibliography -- 5. Discrete Numerical Simulations of the Strength and Microstructure Evolution During Compaction of Layered Granular Solids -- 5.1. Introduction -- 5.2. Numerical simulation -- 5.2.1. Discrete particle simulations of powder compaction -- 5.2.2. Discrete particle simulation of layered compacts -- 5.3. Discussion -- 5.4. Conclusion -- 5.5. Acknowledgements -- 5.6. Bibliography -- 6. Microstructural Views of Stresses in Three-Phase Granular Materials -- 6.1. Microstructural expression of triphasic total stresses -- 6.1.1. Stress description within micro-scale volumes and interfaces of triphasic materials -- 6.1.2. Total stress derivation -- 6.2. Numerical modeling of wet ideal granular materials -- 6.2.1. DEM description of fluid microstructure -- 6.2.2. DEM description of stress and strains -- 6.3. Anisotropy of the capillary stress contribution -- 6.3.1. Mechanical loading -- 6.3.2. Hydraulic loading -- 6.4. Effective stress -- 6.5. Conclusion -- 6.6. Bibliography -- 7. Effect of the Third Invariant of the Stress Deviator on the Response of Porous Solids with Pressure-Insensitive Matrix -- 7.1. Introduction -- 7.2. Problem statement and method of analysis -- 7.2.1. Drucker yield criterion for isotropic materials -- 7.2.2. Unit cell model -- 7.3. Results.

7.3.1. Yield surfaces and porosity evolution -- 7.4. Conclusions -- 7.5. Bibliography -- 8. High Performance Data-Driven Multiscale Inverse Constitutive Characterization of Composites -- 8.1. Introduction -- 8.2. Automated multi-axial testing -- 8.2.1. Loading space -- 8.2.2. Experimental campaign -- 8.3. Constitutive formalisms -- 8.3.1. Small strain formulation -- 8.3.2. Finite strain formulation -- 8.4. Meshless random grid method for experimental evaluation of strain fields -- 8.5. Inverse determination of HDM via design optimization -- 8.5.1. Numerical results of design optimization -- 8.6. Surrogate models for characterization -- 8.6.1. Definition and construction of the surrogate model -- 8.6.2. Characterization by optimization -- 8.6.3. Validation with physical experiments -- 8.7. Multi-scale inversion -- 8.7.1. Forward problem: mathematical homogenization -- 8.7.2. Inverse problem -- 8.8. Computational framework and synthetic experiments -- 8.9. Conclusions and plans -- 8.10. Acknowledgments -- 8.11. Bibliography -- 9. New Trends in Computational Mechanics: Model Order Reduction, Manifold Learning and Data-Driven -- 9.1. Introduction -- 9.1.1. The big picture -- 9.1.2. The PGD at a glance -- 9.2. Constructing slow manifolds -- 9.2.1. From principal component analysis (PCA) to kernel principal component analysis (kPCA) -- 9.2.2. Kernel principal component analysis (kPCA) -- 9.2.3. Locally linear embedding (LLE) -- 9.2.4. Discussion -- 9.3. Manifold-learning-based computational mechanics -- 9.4. Data-driven simulations -- 9.4.1. Data-based weak form -- 9.4.2. Constructing the constitutive manifold -- 9.5. Data-driven upscaling of viscous flows in porous media -- 9.5.1. Upscaling Newtonian and generalized Newtonian fluids flowing in porous media -- 9.6. Conclusions -- 9.7. Bibliography -- List of Authors -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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