Mesoscale materials modeling

Mesoscale simulations model a material as a collection of units, called beads. Each bead might represent a substructure, molecule, monomer, micelle, micro-crystalline domain, solid particle, or an arbitrary region of a fluid. Multiple beads might be connected, typically by a harmonic potential, in order to model a polymer. A simulation is then conducted in which there is an interaction potential between beads and sometimes dynamical equations of motion. This is very hard to do with extremely large molecular dynamics calculations because they would have to be very accurate to correctly reflect the small free energy differences between microstates. There are algorithms for determining an appropriate bead size from molecular dynamics and Monte Carlo simulations. 

Similarly to catalysis, the properties of these composite materials are also determined by a hierarchy of structures on very different length/time scales. Therefore, linking mesoscale molecular models and continuum descriptions is relevant for their understanding and optimization. Together with advanced synthesis methods and functional testing, it is thus necessary also to develop new improved computational methods to provide an understanding of materials properties and to assist in the development of new functional materials. 

Our studies show that the MM5/11P. C conjugation can provide useful prediction of airborne transport of hazardous materials near the surface. It also demonstrates that the accuracy of HPAC computation strongly depends on the performance of MM5. The forecast skill of mesoscale models is likely to be a function of weather scenarios and the terrain over which the models are being run. Because the numerical techniques are different and the model physics (e.g., PBL, surface, and moist processes) vary considerably among different mesoscale meteorological models, we anticipate that there would be discrepancies between the predictions of individual models. 

The method developed in this book is also used to provide input parameters for composite models which can be used to predict the thermoelastic and transport properties of multiphase materials. The prediction of the morphologies and properties of such materials is a very active area of research at the frontiers of materials modeling. The prediction of morphology will be discussed in Chapter 19, with emphasis on the rapidly improving advanced methods to predict thermodynamic equilibrium phase diagrams (such as self-consistent mean field theory) and to predict the dynamic pathway by which the morphology evolves (such as mesoscale simulation methods). Chapter 20 will focus on both analytical (closed-form) equations and numerical simulation methods to predict the thermoelastic properties, mechanical properties under large deformation, and transport properties of multiphase polymeric systems. 

Fig. 1 Hierarchical multiscale, multiparadigm approach to materials modeling, from QM to the mesoscale, incorporating breakthrough methods to handle complex chemical processes (eFF, ReaxFF). Adapted from our multiscale group site http //www.wag.caltech.edu/multiscale...

The evolution of T, is just an exercise in mesoscale thermodynamics [13]. These expressions, in combination with (7.54), incorporate concepts of heterogeneous deformation into a eonsistent mierostruetural model. Aspects of local material response under extremely rapid heating and cooling rates are still open to question. An important contribution to the micromechanical basis for heterogeneous deformation would certainly be to establish appropriate laws of flow-stress evolution due to rapid thermal cycling that would provide a physical basis for (7.54). 

Lipfert, F.W. Dupuis, L.R. Schaedler, J.S. Methods for Mesoscale Modeling for Materials Damage Assessment, Brookhaven National Laboratory Report to U.S. Environmental Protection Agency, BNL 37508, April 1985. 

We have shown in this chapter that microscale measurements can provide a good screening method for the design of fire-resistant materials modified by nanoparticles (and fire retardants) and also, they can be used to quantitatively model and predict the behavior in mesoscale experiments even though an additional parameter is needed to predict the reduced MLR in the mesoscale experiments. The major breakthroughs and challenges are the following ... 

Of course, nanocomposites are not the only area where mesoscale theories are being used to predict nanostructure and morphology. Other applications include—but are not limited to—block copolymer-based materials, surfactant and lipid liquid crystalline phases, micro-encapsulation of drugs and other actives, and phase behavior of polymer blends and solutions. In all these areas, mesoscale models are utilized to describe—qualitatively and often semi-quantitatively—how the structure of each component and the overall formulation influence the formation of the nanoscale morphology. 

The need for further exploration of electronic orders and inhomogeneities in oxides is evident. Experiments need to be designed so as to isolate long-range strain effects, with theoretical efforts towards building coarse-grained models to understand the mesoscale physics. This issue is particularly important for exploiting the electronic softness of these materials for applications. 

Microscale fluid turbulence is, by deflnition, present only when the continuous fluid phase is present. The coefficients Bpv describe the interaction of the particle phase with the continuous phase. In contrast, Bpvf models rapid fluctuations in the fluid velocity seen by the particle that are not included in the mesoscale drag term Ap. In the mesoscale particle momentum balance, the term that generates Bpv will depend on the fluid-phase mass density and, hence, will be null when the fluid material density (pf) is null. In any case, Bpv models momentum transfer to/from the particle phase in fluid-particle systems for which the total momentum is conserved (see discussion leading to Eq. (5.17)). 

Multiscale modeling molecular, nanoscale, mesoscale (physics, materials science, computer science, mathematics)... 

In contrast to the nano-scale, where the periodic arrangement of atoms on crystal lattices is well established, and the macro-scale, where a continuous distribution of matter is assumed, adequate quantitative descriptions are notably lacking for structure at the micro- and mesoscales, where properties are described in terms of the behavior of dislocations, material in grains, particles of different phases and the boundaries among them. The traditional means of describing these microstructural attributes with descriptive terms that call to mind familiar shapes fails to provide an adequate quantitative basis for transferring this information to quantitative models. 

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