44,945 research outputs found
Model Reduction for Multiscale Lithium-Ion Battery Simulation
In this contribution we are concerned with efficient model reduction for
multiscale problems arising in lithium-ion battery modeling with spatially
resolved porous electrodes. We present new results on the application of the
reduced basis method to the resulting instationary 3D battery model that
involves strong non-linearities due to Buttler-Volmer kinetics. Empirical
operator interpolation is used to efficiently deal with this issue.
Furthermore, we present the localized reduced basis multiscale method for
parabolic problems applied to a thermal model of batteries with resolved porous
electrodes. Numerical experiments are given that demonstrate the reduction
capabilities of the presented approaches for these real world applications
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
A Particle-based Multiscale Solver for Compressible Liquid-Vapor Flow
To describe complex flow systems accurately, it is in many cases important to
account for the properties of fluid flows on a microscopic scale. In this work,
we focus on the description of liquid-vapor flow with a sharp interface between
the phases. The local phase dynamics at the interface can be interpreted as a
Riemann problem for which we develop a multiscale solver in the spirit of the
heterogeneous multiscale method, using a particle-based microscale model to
augment the macroscopic two-phase flow system. The application of a microscale
model makes it possible to use the intrinsic properties of the fluid at the
microscale, instead of formulating (ad-hoc) constitutive relations
Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales
With the increasing interplay between experimental and computational
approaches at multiple length scales, new research directions are emerging in
materials science and computational mechanics. Such cooperative interactions
find many applications in the development, characterization and design of
complex material systems. This manuscript provides a broad and comprehensive
overview of recent trends where predictive modeling capabilities are developed
in conjunction with experiments and advanced characterization to gain a greater
insight into structure-properties relationships and study various physical
phenomena and mechanisms. The focus of this review is on the intersections of
multiscale materials experiments and modeling relevant to the materials
mechanics community. After a general discussion on the perspective from various
communities, the article focuses on the latest experimental and theoretical
opportunities. Emphasis is given to the role of experiments in multiscale
models, including insights into how computations can be used as discovery tools
for materials engineering, rather than to "simply" support experimental work.
This is illustrated by examples from several application areas on structural
materials. This manuscript ends with a discussion on some problems and open
scientific questions that are being explored in order to advance this
relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J.
Mater. Sc
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
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