Fundamentals of Multiscale Modeling of Structural Materials

Description

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. 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 A B C D E F G H I J K L M N O P Q R S T U V W