Fundamentals of Multiscale Modeling of Structural Materials

Cover
Front Matter
Copyright
Introduction
Contributors
Preface
Electronic structure and density functional theory
A brief introduction to electronic structure methods
The theoretical framework behind density functional theory
Where does DFT come from?
A formulation of DFT a la Kohn-Sham
DFT levels of theory and the zoo of exchange-correlation functionals
Where are the Van der Waals interactions in DFT?
Basis sets
Localized basis sets
Plane waves
Pseudopotentials
Using DFT to calculate properties of solids
Crystal structure
Elastic constants
Surface energy
Adsorption energies
Band structure
Density of states and absorption spectra
Finding transition state
Recommended further reading
References
Atomistic molecular modeling methods
Contents
The history and significance of atomistic simulations
What is atomistic modeling and what is it good for?
The zoo of atomistic modeling methods
Modeling interatomic interactions using empirical force fields
Bonded interactions
Nonbonded interactions
A short comment on force field parameterization
Challenges and limitations of empirical force fields
Integrating the dynamics of atoms: Molecular dynamics (MD)
Ensembles and molecular dynamics at constant temperature and/or pressure
How to calculate properties from an MD simulation
Structural and thermodynamic properties
Dynamical properties
Some odds and ends of atomistic simulations
Concluding remark
References
Particle-based mesoscale modeling and coarse-graining methods
Contents
Introduction to mesoscale modeling of materials
Overview of coarse-graining modeling strategies
Particle-based mesoscale modeling techniques
Classical molecular dynamics
Langevin dynamics
Dissipative particle dynamics (DPD)
Multiscale coarse-graining methods
Generic coarse-graining methods
A classical generic coarse-graining model: FENE model
Generalized generic coarse-graining models
Chemistry-specific coarse-graining methods
United-atom
Iterative Boltzmann inversion (IBI)
Inverse Monte Carlo (IMC)
Energy renormalization (ER)
Force matching
Relative entropy
Martini approach
Strain energy conservation
Concluding remarks
References
Fast homogenization through clustering-based reduced-order modeling
Contents
Introduction
Computational homogenization and multiscale modeling
Clustering-based reduced-order modeling
Overview of self-consistent clustering analysis
Preliminaries on micromechanics
Introduction
Problem formulation
The auxiliary homogeneous problem
Green operator under linear elastic isotropy
The Lippmann-Schwinger integral equation
Offline stage
Step 1: Conduct DNS linear elastic analyses
FFT-based homogenization basic scheme
Step 2: Perform clustering-based domain decomposition
Step 3: Compute cluster interaction tensors
Online stage
Continuous Lippmann-Schwinger integral equation
Discretized Lippmann-Schwinger integral equation
Numerical solution of the reduced microscale equilibrium problem
The homogenized consistent tangent modulus
The reference homogeneous elastic material
Self-consistent scheme
Numerical application
Definition of the heterogeneous material RVE
Offline stage: Conduct DNS linear elastic analyses
Offline stage: Perform clustering-based domain decomposition
Offline stage: Compute cluster interaction tensors
Online stage: Multiscale equilibrium problem
Concluding remarks and future directions
Appendix
Linearization of the discretized Lippmann-Schwinger equilibrium equations
Self-consistent scheme optimization problem
References
Immersogeometric formulation for free-surface flows
Contents
Introduction
Governing equations of free-surface flow
Level set method
Navier-Stokes equations of incompressible flows
Semidiscrete formulation
Residual-based variational multiscale method
Redistancing and mass conservation
Weak enforcement of Dirichlet boundary conditions
Tetrahedral finite cell method
Recursive refinement of quadrature points
Inside-outside test by ray-tracing method
Time integration
Generalized-α method
Predictor stage
Multicorrector stage
Fully coupled linear solver
Numerical examples
Solitary wave impacting a stationary platform
Dam break with obstacle
Planning of a DTMB 5415 ship model
Summary and future work
References
Machine learning in materials modeling and design
Contents
Introduction
What is data science?
What is machine learning?
Types of machine learning
Supervised learning
Unsupervised learning
Semisupervised learning
Reinforcement learning
Math preliminaries for machine learning
Overview of machine learning algorithms
Data selection and feature selection
Classification of machine learning
Decision tree algorithm
Random forest
k-nearest neighbor
Feature reduction methods
Principal component analysis
T-distributed stochastic neighbor embedding
Linear discriminant analysis
Regression models
Linear regression
Polynomial regression
Regularized linear regression
Deep learning
Applications of machine learning in materials design and modeling
Machine learning in prediction of materials properties
A surrogate model to predict glass transition of conjugated polymers
Quantitative structure-property relationship (QSPR) for Tg prediction of polymers
Material classification via machine learning
Decision tree to synthesize new AB2C Heusler compounds
Random forest for studying the chemical toxicity of quantum dots
Advances of machine learning in molecular model development
Development of molecular force fields via machine learning
Machine learning-informed coarse-grained modeling
Application of machine learning in designing biomaterials
AlphaFold prediction of protein structure
AlphaFold network
Future outlook
References
Multiscale modeling of failure behaviors in carbon fiber-reinforced polymer composites
Introduction
Synopsis of the multiscale modeling framework
Nanoscale characterization of the interphase region
Microscale model development for UD CFRP composites
UD RVE model and constitutive laws for microstructure components
Boundary conditions and RVE size
Failure analysis of UD CFRP composites under uniaxial stress state
Failure envelopes of UD CFRP composites under multiaxial stress state
Failure envelopes of σ22-τ12 and σ22-τ23
Failure envelopes of σ11-τ12
Proposed failure criteria
Validation of the proposed failure criteria
Failure criteria of σ22-τ12
Failure criteria of σ11-τ12
Failure envelopes of σ22-τ23
Elastic-plastic-damage model for homogenized UD CFRP composites
Proposed elastic-plastic-damage model
Validation of the proposed elastic-plastic-damage model
Mesoscale model development for woven composites
Woven composites description
Mesoscale RVE model generation for woven composites
Constitutive and damage laws
Results predicted by the woven RVE model
Experimental and computational stress-strain curves
Damage initiation and propagation process
Macroscale model of U-shaped part made of UD and woven CFRP composites
Conclusions
References
Engineering elasticity inspired by natural biopolymers
Contents
Introduction
Sequence and structure in elastomeric biopolymers
Elastomeric sequences and motifs
Secondary and tertiary structure of elastomeric protein polymers
Cross-linking for tuning elastomeric biopolymer properties
Intrinsic and extrinsic factors modulating elastomeric protein elasticity
Conformational entropic effects in elastin-based materials
Solvent and hydration effects in elastomeric proteins
Effects of solvent on elastin and ELP conformations
Hydration level effect on XLPs
Hydrophobic hydration and elasticity of elastin/ELPs
Temperature as a trigger for modulating elastomeric biomaterials
Temperature-associated conformational variations of resilin- and elastin-based materials
Modulators of LCST/UCST in resilin- and elastin-based materials
Computational approaches to elastomeric protein polymers
Case study 1: Computational smart polymer design based on elastin protein mutability
Case study 2: Elasticity, structure, and relaxation of extended proteins under force
Case study 3: Effect of sodium chloride on the structure and stability of spider silks N-terminal protein domain
Case study 4: Molecular model of human tropoelastin and implications of associated mutations
Conclusion
References
Multiscale modeling applied to additive manufacturing
Introduction
Simulating additive manufacturing process
Molten pool dynamic modeling
Heat source modeling
Common heat source
Electron beam absorption modeling
Laser absorption modeling
Metal evaporation modeling
Simulation of keyhole dynamics
Single-track, multitrack, and multilayer simulation
Simulating microstructure evolution
Simulation of dendrite growth
Simulation of grain evolution
Precipitation process in the EB-PBF
Mechanical properties simulation
Thermal stress simulation for multiple tracks and layers
Grain-level residual stress simulation
Crystal plasticity framework
Simulation of grain-level residual stress
Multiscale modeling of structure-property relationship
Grain structure reconstruction
Polycrystal-scale plasticity model
Summary
References
Multiscale modeling of supramolecular assemblies of 2D materials
Introduction
Coarse-graining modeling methods for 2D materials
Overview of the coarse-graining technique
Coarse-graining model of graphene
Coarse-graining model of graphene oxide
Mesoscale model of graphene
Coarse-graining model of multilayer graphene
Summary
Multiscale modeling of crumpled sheet and supramolecular assemblies
Crumpled graphene
Size effects on the crumpling behaviors of graphene
Effects of defects on the crumpling behaviors of graphene
Effects of self-adhesion on the crumpling behaviors of macromolecular sheets
Nanostructured supramolecular assemblies
Mechanical behavior of graphene foam
Temperature effects on the mechanical and dynamic behaviors of graphene foam
Multilayer graphene assemblies
Multilayer graphene-reinforced nanocomposites
Conclusion and future outlook
References
Index
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