Fundamentals of Multiscale Modeling of Structural Materials provides a robust introduction to the computational tools, underlying theory, practical applications, and governing physical phenomena necessary to simulate and understand a wide-range of structural materials at multiple time and length scales. The book offers practical guidelines for modeling common structural materials with well-established techniques, outlining detailed modeling approaches for calculating and analyzing mechanical, thermal and transport properties of various structural materials such as metals, cement/concrete, polymers, composites, wood, thin films, and more.
Computational approaches based on artificial intelligence and machine learning methods as complementary tools to the physics-based multiscale techniques are discussed as are modeling techniques for additively manufactured structural materials. Special attention is paid to how these methods can be used to develop the next generation of sustainable, resilient and environmentally-friendly structural materials, with a specific emphasis on bridging the atomistic and continuum modeling scales for these materials.
1. Introduction - p. xiii 2. Part One: Methods and approaches to multiscale modeling - p. xiv 3. Chapter One. Electronic structure and density functional theory - p. 3 4. Chapter Two. Atomistic molecular modeling methods - p. 37 5. Chapter Three. Particle-based mesoscale modeling and coarse-graining methods - p. 75 6. Chapter Four. Fast homogenization through clustering-based reduced-order modeling - p. 113 7. Abstract - p. 113 8. Contents - p. 113 9. Abbreviations - p. 114 10. Notation - p. 114 11. Operators and symbols - p. 114 12. Subscripts and superscripts - p. 115 13. Accents - p. 115 14. Sets, domains, and boundaries - p. 115 15. 1 Introduction - p. 116 16. 2 Computational homogenization and multiscale modeling - p. 119 17. 3 Clustering-based reduced-order modeling - p. 118 18. 4 Overview of self-consistent clustering analysis - p. 120 19. 5 Preliminaries on micromechanics - p. 123 20. 6 Offline stage - p. 128 21. 7 Online stage - p. 136 22. 8 Numerical application - p. 150 23. 9 Concluding remarks and future directions - p. 159 24. Appendix - p. 158 25. References - p. 163 26. Chapter Five. Immersogeometric formulation for free-surface flows - p. 169 27. Chapter Six. Machine learning in materials modeling and design - p. 203 28. Part Two: Applications of multiscale modeling to structural materials - p. 236 29. Chapter Seven. Multiscale modeling of failure behaviors in carbon fiber-reinforced polymer composites - p. 239 30. Chapter Eight. Engineering elasticity inspired by natural biopolymers - p. 293 31. Chapter Nine. Multiscale modeling applied to additive manufacturing - p. 333 32. Chapter Ten. Multiscale modeling of supramolecular assemblies of 2D materials - p. 389 33. Index - p. 425
Fundamentals of Multiscale Modeling of Structural Materials provides a robust introduction to the computational tools, underlying theory, practical applications, and governing physical phenomena necessary to simulate and understand a wide-range of structural materials at multiple time and length scales. The book offers practical guidelines for modeling common structural materials with well-established techniques, outlining detailed modeling approaches for calculating and analyzing mechanical, thermal and transport properties of various structural materials such as metals, cement/concrete, polymers, composites, wood, thin films, and more.
Computational approaches based on artificial intelligence and machine learning methods as complementary tools to the physics-based multiscale techniques are discussed as are modeling techniques for additively manufactured structural materials. Special attention is paid to how these methods can be used to develop the next generation of sustainable, resilient and environmentally-friendly structural materials, with a specific emphasis on bridging the atomistic and continuum modeling scales for these materials.
1. Introduction - p. xiii 2. Part One: Methods and approaches to multiscale modeling - p. xiv 3. Chapter One. Electronic structure and density functional theory - p. 3 4. Chapter Two. Atomistic molecular modeling methods - p. 37 5. Chapter Three. Particle-based mesoscale modeling and coarse-graining methods - p. 75 6. Chapter Four. Fast homogenization through clustering-based reduced-order modeling - p. 113 7. Abstract - p. 113 8. Contents - p. 113 9. Abbreviations - p. 114 10. Notation - p. 114 11. Operators and symbols - p. 114 12. Subscripts and superscripts - p. 115 13. Accents - p. 115 14. Sets, domains, and boundaries - p. 115 15. 1 Introduction - p. 116 16. 2 Computational homogenization and multiscale modeling - p. 119 17. 3 Clustering-based reduced-order modeling - p. 118 18. 4 Overview of self-consistent clustering analysis - p. 120 19. 5 Preliminaries on micromechanics - p. 123 20. 6 Offline stage - p. 128 21. 7 Online stage - p. 136 22. 8 Numerical application - p. 150 23. 9 Concluding remarks and future directions - p. 159 24. Appendix - p. 158 25. References - p. 163 26. Chapter Five. Immersogeometric formulation for free-surface flows - p. 169 27. Chapter Six. Machine learning in materials modeling and design - p. 203 28. Part Two: Applications of multiscale modeling to structural materials - p. 236 29. Chapter Seven. Multiscale modeling of failure behaviors in carbon fiber-reinforced polymer composites - p. 239 30. Chapter Eight. Engineering elasticity inspired by natural biopolymers - p. 293 31. Chapter Nine. Multiscale modeling applied to additive manufacturing - p. 333 32. Chapter Ten. Multiscale modeling of supramolecular assemblies of 2D materials - p. 389 33. Index - p. 425
