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Initiative (ASCI) were birthed within the
Department of Energy (DOE) and managed by the national labs within the US. Within ASCI, the basic recognized premise was to provide more accurate and precise simulation-based design and analysis tools. Because of the requirements for greater complexity in the simulations, parallel computing and multiscale modeling became the major challenges that needed to be addressed. With this perspective, the idea of experiments shifted from the large-scale complex tests to multiscale experiments that provided material models with validation at different length scales. If the modeling and simulations were physically based and less empirical, then a predictive capability could be realized for other conditions. As such, various multiscale modeling methodologies were independently being created at the DOE national labs: Los Alamos National Lab (LANL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL), and Oak Ridge National Laboratory (ORNL). In addition, personnel from these national labs encouraged, funded, and managed academic research related to multiscale modeling. Hence, the creation of different methodologies and computational algorithms for parallel environments gave rise to different emphases regarding multiscale modeling and the associated multiscale experiments.
326:
levels of success. Multiple scientific articles were written, and the multiscale activities took different lives of their own. At SNL, the multiscale modeling effort was an engineering top-down approach starting from continuum mechanics perspective, which was already rich with a computational paradigm. SNL tried to merge the materials science community into the continuum mechanics community to address the lower-length scale issues that could help solve engineering problems in practice.
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to reduce nuclear underground tests in the mid-1980s, with the last one in 1992, the idea of simulation-based design and analysis concepts were birthed. Multiscale modeling was a key in garnering more precise and accurate predictive tools. In essence, the number of large-scale systems level tests that were previously used to validate a design was reduced to nothing, thus warranting the increase in simulation results of the complex systems for design verification and validation purposes.
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390:(information about atoms and/or groups of atoms is included), mesoscale or nano-level (information about large groups of atoms and/or molecule positions is included), level of continuum models, level of device models. Each level addresses a phenomenon over a specific window of length and time. Multiscale modeling is particularly important in
334:
multiscale modeling and simulation-based design were invariant to the type of product and that effective multiscale simulations could in fact lead to design optimization, a paradigm shift began to occur, in various measures within different industries, as cost savings and accuracy in product warranty estimates were rationalized.
232:
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The recent surge of multiscale modeling from the smallest scale (atoms) to full system level (e.g., autos) related to solid mechanics that has now grown into an international multidisciplinary activity was birthed from an unlikely source. Since the US Department of Energy (DOE) national labs started
985:
Adamson, S.; Astapenko, V.; Chernysheva, I.; Chorkov, V.; Deminsky, M.; Demchenko, G.; Demura, A.; Demyanov, A.; et al. (2007). "Multiscale multiphysics nonempirical approach to calculation of light emission properties of chemically active nonequilibrium plasma: Application to Ar GaI3 system".
412:
In meteorology, multiscale modeling is the modeling of the interaction between weather systems of different spatial and temporal scales that produces the weather that we experience. The most challenging task is to model the way through which the weather systems interact as models cannot see beyond
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Essentially, the idea of filling the space of system-level “tests” was then proposed to be filled by simulation results. After the
Comprehensive Test Ban Treaty of 1996 in which many countries pledged to discontinue all systems-level nuclear testing, programs like the Advanced Strategic Computing
325:
At LANL, LLNL, and ORNL, the multiscale modeling efforts were driven from the materials science and physics communities with a bottom-up approach. Each had different programs that tried to unify computational efforts, materials science information, and applied mechanics algorithms with different
321:
The advent of parallel computing also contributed to the development of multiscale modeling. Since more degrees of freedom could be resolved by parallel computing environments, more accurate and precise algorithmic formulations could be admitted. This thought also drove the political leaders to
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has been proven to be sufficient for describing the dynamics of a broad range of fluids. However, its use for more complex fluids such as polymers is dubious. In such a case, it may be necessary to use multiscale modeling to accurately model the system such that the stress tensor can be extracted
329:
Once this management infrastructure and associated funding was in place at the various DOE institutions, different academic research projects started, initiating various satellite networks of multiscale modeling research. Technological transfer also arose into other labs within the
Department of
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The growth of multiscale modeling in the industrial sector was primarily due to financial motivations. From the DOE national labs perspective, the shift from large-scale systems experiments mentality occurred because of the 1996 Nuclear Ban Treaty. Once industry realized that the notions of
377:
In physics and chemistry, multiscale modeling is aimed at the calculation of material properties or system behavior on one level using information or models from different levels. On each level, particular approaches are used for the description of a system. The following levels are usually
425:, cannot see the smaller cloud systems. So we need to come to a balance point so that the model becomes computationally feasible and at the same time we do not lose much information, with the help of making some rational guesses, a process called parametrization.
1348:
Multiscale
Modeling of Materials (MMM-Tools) Project at Dr. Martin Steinhauser's group at the Fraunhofer-Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, at Freiburg, Germany. Since 2013, M.O. Steinhauser is associated at the University of Basel,
120:
1224:
Tao, Wei-Kuo; Chern, Jiun-Dar; Atlas, Robert; Randall, David; Khairoutdinov, Marat; Li, Jui-Lin; Waliser, Duane E.; Hou, Arthur; et al. (2009). "A Multiscale
Modeling System: Developments, Applications, and Critical Issues".
957:
Knizhnik, A.A.; Bagaturyants, A.A.; Belov, I.V.; Potapkin, B.V.; Korkin, A.A. (2002). "An integrated kinetic Monte Carlo molecular dynamics approach for film growth modeling and simulation: ZrO2 deposition on Si surface".
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received the Nobel Prize in
Chemistry in 2013 for the development of a multiscale model method using both classical and quantum mechanical theory which were used to model large complex chemical systems and reactions.
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Besides the many specific applications, one area of research is methods for the accurate and efficient solution of multiscale modeling problems. The primary areas of mathematical and algorithmic development include:
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The aforementioned DOE multiscale modeling efforts were hierarchical in nature. The first concurrent multiscale model occurred when
Michael Ortiz (Caltech) took the molecular dynamics code Dynamo, developed by
308:
Horstemeyer 2009, 2012 presented a historical review of the different disciplines (mathematics, physics, and materials science) for solid materials related to multiscale materials modeling.
587:
Martins, Ernane de
Freitas; da Silva, Gabriela Dias; Salvador, Michele Aparecida; Baptista, Alvaro David Torrez; de Almeida, James Moraes; Miranda, Caetano Rodrigues (2019-10-28).
100:
of solving problems that have important features at multiple scales of time and/or space. Important problems include multiscale modeling of fluids, solids, polymers, proteins,
1029:
da Silva, Gabriela Dias; de
Freitas Martins, Ernane; Salvador, Michele Aparecida; Baptista, Alvaro David Torrez; de Almeida, James Moraes; Miranda, Caetano Rodrigues (2019).
227:{\displaystyle {\begin{array}{lcl}\rho _{0}(\partial _{t}\mathbf {u} +(\mathbf {u} \cdot \nabla )\mathbf {u} )=\nabla \cdot \tau ,\\\nabla \cdot \mathbf {u} =0.\end{array}}}
401:, multiscale modeling addresses challenges for decision-makers that come from multiscale phenomena across organizational, temporal, and spatial scales. This theory fuses
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417:) which can see each possible cloud structure for the whole globe is computationally very expensive. On the other hand, a computationally feasible
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since it allows the prediction of material properties or system behavior based on knowledge of the process-structure-property relationships.
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Baeurle, S. A. (2008). "Multiscale modeling of polymer materials using field-theoretic methodologies: A survey about recent developments".
811:
Karplus, Martin (2014-09-15). "Development of
Multiscale Models for Complex Chemical Systems: From H+H2 to Biomolecules (Nobel Lecture)".
571:
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Kmiecik, Sebastian; Gront, Dominik; Kolinski, Michal; Wieteska, Lukasz; Dawid, Aleksandra Elzbieta; Kolinski, Andrzej (2016-06-22).
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Multiscale Modeling Group: Institute of Physical & Theoretical Chemistry, University of Regensburg, Regensburg, Germany
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the limit of the model grid size. In other words, to run an atmospheric model that is having a grid size (very small ~
112:
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Hosseini, SA; Shah, N (2009). "Multiscale modelling of hydrothermal biomass pretreatment for chip size optimization".
763:
Levitt, Michael (2014-09-15). "Birth and Future of Multiscale Modeling for Macromolecular Systems (Nobel Lecture)".
406:
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Oden, J. Tinsley; Vemaganti, Kumar; Moës, Nicolas (1999-04-16). "Hierarchical modeling of heterogeneous solids".
46:
1031:"From Atoms to Pre-salt Reservoirs: Multiscale Simulations of the Low-Salinity Enhanced Oil Recovery Mechanisms"
649:
Zeng, Q. H.; Yu, A. B.; Lu, G. Q. (2008-02-01). "Multiscale modeling and simulation of polymer nanocomposites".
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An Introduction to Computational Multiphysics II: Theoretical Background Part I Harvard University video series
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409:. Multiscale decision-making draws upon the analogies between physical systems and complex man-made systems.
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589:"Uncovering the Mechanisms of Low-Salinity Water Injection EOR Processes: A Molecular Simulation Viewpoint"
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Multiscale Modeling Tools for Protein Structure Prediction and Protein Folding Simulations, Warsaw, Poland
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356:
at Sandia National Labs, and with his students embedded it into a finite element code for the first time.
463:
914:
De Pablo, Juan J. (2011). "Coarse-Grained Simulations of Macromolecules: From DNA to Nanocomposites".
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Tadmore, E.B.; Ortiz, M.; Phillips, R. (1996-09-27). "Quasicontinuum Analysis of Defects in Solids".
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859:"Multiscale Modeling of Biological Functions: From Enzymes to Molecular Machines (Nobel Lecture)"
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Multiscale modeling for Materials Engineering: Set-up of quantitative micromechanical models
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Department of Energy Summer School on Multiscale Mathematics and High Performance Computing
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Multiscale Material Modelling on High Performance Computer Architectures, MMM@HPC project
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as well as various physical and chemical phenomena (like adsorption, chemical reactions,
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Chen, Shiyi; Doolen, Gary D. (1998-01-01). "Lattice Boltzmann Method for Fluid Flows".
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Multiscale Materials Modeling: Fourth International Conference, Tallahassee, FL, USA
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Practical Aspects of Computational Chemistry: Methods, Concepts and Applications
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Multiscale Conceptual Model Figures for Biological and Environmental Science
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without requiring the computational cost of a full microscale simulation.
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external links, and converting useful links where appropriate into
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Modeling Materials: Continuum, Atomistic and Multiscale Techniques
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Multiscale Modeling of Fluids and Solids - Theory and Applications
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1382:(E. B. Tadmor and R. E. Miller, Cambridge University Press, 2011)
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Integrated Computational Materials Engineering (ICME) for Metals
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Integrated Computational Materials Engineering (ICME) for Metals
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International Journal for Multiscale Computational Engineering
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models (information about individual atoms is included),
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713:"Coarse-Grained Protein Models and Their Applications"
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Mississippi State University ICME Cyberinfrastructure
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Computer Methods in Applied Mechanics and Engineering
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In a wide variety of applications, the stress tensor
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382:(information about electrons is included), level of
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1392:SIAM Journal of Multiscale Modeling and Simulation
1107:. In Leszczyński, Jerzy; Shukla, Manoj K. (eds.).
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1288:may not follow Knowledge's policies or guidelines
405:and multiscale mathematics and is referred to as
322:encourage the simulation-based design concepts.
1227:Bulletin of the American Meteorological Society
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486:Integrated computational materials engineering
392:integrated computational materials engineering
256:is given as a linear function of the gradient
330:Defense and industrial research communities.
56:. Consider transferring direct quotations to
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1324:Learn how and when to remove this message
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1074:. Cambridge: Cambridge University Press.
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111:An example of such problems involve the
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813:Angewandte Chemie International Edition
765:Angewandte Chemie International Edition
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988:Journal of Physics D: Applied Physics
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1343:Multiscale Modeling of Flow Flow
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526:Annual Review of Fluid Mechanics
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1105:"Multiscale Modeling: A Review"
960:Computational Materials Science
115:for incompressible fluid flow.
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546:10.1146/annurev.fluid.30.1.329
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857:Warshel, Arieh (2014-09-15).
636:10.1016/S0045-7825(98)00224-2
1132:Horstemeyer, M. F. (2012).
1103:Horstemeyer, M. F. (2009).
1008:10.1088/0022-3727/40/13/S06
730:10.1021/acs.chemrev.6b00163
651:Progress in Polymer Science
562:Steinhauser, M. O. (2017).
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407:multiscale decision-making
1179:10.1080/01418619608243000
690:10.1007/s10910-008-9467-3
380:quantum mechanical models
346:, Chapter 1, Section 1.3.
1159:Philosophical Magazine A
501:Multiresolution analysis
421:(GCM), with grid size ~
378:distinguished: level of
272:{\displaystyle \nabla u}
54:summarize the quotations
476:Computational mechanics
113:Navier–Stokes equations
1247:10.1175/2008BAMS2542.1
1197:Bioresource Technology
875:10.1002/anie.201403689
825:10.1002/anie.201403924
777:10.1002/anie.201403691
481:Equation-free modeling
459:Network-based modeling
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292:{\displaystyle \tau }
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419:Global climate model
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279:. Such a choice for
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538:1998AnRFM..30..329C
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