93,354 research outputs found
Statistical coarse-graining as an approach to multiscale problems in magnetism
Multiscale phenomena which include several processes occuring simultaneously
at different length scales and exchanging energy with each other, are
widespread in magnetism. These phenomena often govern the magnetization
reversal dynamics, and their correct modeling is important. In the present
paper, we propose an approach to multiscale modeling of magnets, applying the
ideas of coarse graining. We have analyzed the choice of the weighting function
used in coarse graining, and propose an optimal form for this function. Simple
tests provide evidence that this approach may be useful for modeling of
realistic magnetic systems.Comment: 5 RevTeX pages, 2 figure
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
Multiscale modeling in biology
The 1966 science-fction film Fantastic Voyage captured the public imagination with a clever idea: what fantastic things might we see and do if we could minaturize ourselves and travel through the bloodstream as corpuscles do? (This being Hollywood, the answer was that we'd save a fellow scientist from evildoers.
From Electrons to Finite Elements: A Concurrent Multiscale Approach for Metals
We present a multiscale modeling approach that concurrently couples quantum
mechanical, classical atomistic and continuum mechanics simulations in a
unified fashion for metals. This approach is particular useful for systems
where chemical interactions in a small region can affect the macroscopic
properties of a material. We discuss how the coupling across different scales
can be accomplished efficiently, and we apply the method to multiscale
simulations of an edge dislocation in aluminum in the absence and presence of H
impurities.Comment: 4 page
Reduced finite element square techniques (RFE2): towards industrial multiscale fe software
Reduced order modeling techniques proposed by the authors are assessed for an industrial case study of a 3D reinforced composite laminate. Essentially, the main dominant strain micro-structural modes are obtained through standard reduced order modeling techniques applied over snapshots of a representative training strain space. Additionally, a reduced number of integration points is obtained by exactly integrating the main energy modes resulting from the training energy snapshots. The outcome consists of a number of dominant strain modes integrated over a remarkably reduced number of integration points which provide the support to evaluate the constitutive behavior of the micro-structural phases. Results are discussed in terms of the consistency of the multiscale analysis, tunability of the microscopic material parameters and speed up ratios comparing a high fidelity simulation and the multiscale reduced order model
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