Multiscale simulation framework

I expect that SA of stochastic and multiscale models will be important in traditional tasks such as the identification of rate-determining steps and parameter estimation. I propose that SA will also be a key tool in controlling errors in information passing between scales. For example, within a multiscale framework, one could identify what features of a coarse-level model are affected from a finer scale model and need higher-level theory to improve accuracy of the overall multiscale simulation. Next a brief overview of SA for deterministic systems is given followed by recent work on SA of stochastic and multiscale systems. 

Xiang Z, Cao D, Lan J, Wang W, Broom DP Multiscale simulation and modelhng of adsorptive processes for energy gas storage and carbon dioxide capture in porous coordination frameworks. Energy Environ Sd 3(10) 1469—1487, 2010. 

This contribution outlines a multiscale simulation approach for analysis of a Wurster coating process occurring in a fluidized bed. The processes occurring in the apparatus are described on four different time and length scales The Discrete Element Method coupled with Computational Fluid Dynamics, where each particle is considered as a separate entity and its motion in fluid field is calculated, play a central role in the modeling framework. On the macroscale, the Population Balance Model describes the particle... 

In this review, we introduce another approach to study the multiscale structures of polymer materials based on a lattice model. We first show the development of a Helmholtz energy model of mixing for polymers based on close-packed lattice model by combining molecular simulation with statistical mechanics. Then, holes are introduced to account for the effect of pressure. Combined with WDA, this model of Helmholtz energy is further applied to develop a new lattice DFT to calculate the adsorption of polymers at solid-liquid interface. Finally, we develop a framework based on the strong segregation limit (SSL) theory to predict the morphologies of micro-phase separation of diblock copolymers confined in curved surfaces. 

Erson, E.Z., Cavusoglu, M.C. A software framework for multiscale and multilevel physiological model integration and simulation. IEEE Engineering in Medicine and Biology Society. Annual Conference 2008,5449-5453 (2008)... 

In this chapter, we describe some of our progress in theory, methods, computational techniques, and tools towards first-principles-based multiscale, multiparadigm simulations, in particular, for systems that exhibit intricate chemical behavior. We map the document over the hierarchical framework depicted in Fig. 1, threading the description from QM up through mesoscale classical approximations, presenting significant and relevant example applications to different fields at each level. 

Heterogeneous Multiscale Method The heterogeneous multiscale method provides a general framework for dealing with multiscale phenomena and can be easily applied to the coupling of continuum and atomistic (molecular dynamics) simulations at finite temperature. 

The ultimate goal of multiscale modeling and simulation is to produce a global framework for system-level analyses of processes and phenomena relevant to human scales that are governed by phenomena occurring at much finer length and time scales. 

Time-stepper-based methods are, in effect, alternative ensembles for performing microscopic (molecular dynamics, kMC, Brownian dynamics) simulations. limovative multiscale/multilevel techniques proposed over the last decade that can be integrated in an equation-free, time-stepper-based framework include the quasi-continuum methods of Phillips and coworkers (Phillips, 2001 Ortiz and Philhps, 1999) and the optimal prediction methods of Chorin and cowoikers (Chorin et al., 1998, 2000) (see the discussion in Kevrekidis et al., 2003). 

The integration of federated databases with predictive modeling and simulation tools represents an important opportunity for major advances in the effective use of massive amounts of data. The framework will need to include computational tools, evaluated experimental data, active databases, and knowledge-based software guides for generating chemical and physical property data on demand with quantitative measures of uncertainty. The approach has to provide vahdated, predictive simulation methods for complicated systems with seamless multiscale and multidisciplinary integration to predict properties and to model physical phenomena and processes. The results must be in a form that can be visualized and used by even a nonexpert. 

Mishra, S.K. et al. (2008) Spatiotempo-ral compound wavelet matrix framework for multiscale/multiphysics reactor simulation case study of a heterogeneous reaction/diffusion system. Int.J. Chem. Reactor Eng., 6 (A28), A28-1-A28-42. 

MiUer B-E, Tadmor EB A unified framework and performance benchmark of fourteen multiscale atomistic/continuum coupling methods. Model Simul Mater Sci Eng 17 053001, 2009. 

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