34,670 research outputs found
Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)
Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we
introduce a novel crowd simulation method that runs at interactive rates for
hundreds of thousands of agents. Our method enables the detailed modeling of
per-agent behavior in a Lagrangian formulation. We model short-range and
long-range collision avoidance to simulate both sparse and dense crowds. On the
particles representing agents, we formulate a set of positional constraints
that can be readily integrated into a standard PBD solver. We augment the
tentative particle motions with planning velocities to determine the preferred
velocities of agents, and project the positions onto the constraint manifold to
eliminate colliding configurations. The local short-range interaction is
represented with collision and frictional contact between agents, as in the
discrete simulation of granular materials. We incorporate a cohesion model for
modeling collective behaviors and propose a new constraint for dealing with
potential future collisions. Our new method is suitable for use in interactive
games.Comment: 9 page
The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
The XDEM multi-physics and multi-scale simulation platform roots in the Ex-
tended Discrete Element Method (XDEM) and is being developed at the In- stitute
of Computational Engineering at the University of Luxembourg. The platform is
an advanced multi- physics simulation technology that combines flexibility and
versatility to establish the next generation of multi-physics and multi-scale
simulation tools. For this purpose the simulation framework relies on coupling
various predictive tools based on both an Eulerian and Lagrangian approach.
Eulerian approaches represent the wide field of continuum models while the
Lagrange approach is perfectly suited to characterise discrete phases. Thus,
continuum models include classical simulation tools such as Computa- tional
Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended
configuration of the classical Discrete Element Method (DEM) addresses the
discrete e.g. particulate phase. Apart from predicting the trajectories of
individual particles, XDEM extends the application to estimating the thermo-
dynamic state of each particle by advanced and optimised algorithms. The
thermodynamic state may include temperature and species distributions due to
chemical reaction and external heat sources. Hence, coupling these extended
features with either CFD or FEA opens up a wide range of applications as
diverse as pharmaceutical industry e.g. drug production, agriculture food and
processing industry, mining, construction and agricultural machinery, metals
manufacturing, energy production and systems biology
Electrokinetic Lattice Boltzmann solver coupled to Molecular Dynamics: application to polymer translocation
We develop a theoretical and computational approach to deal with systems that
involve a disparate range of spatio-temporal scales, such as those comprised of
colloidal particles or polymers moving in a fluidic molecular environment. Our
approach is based on a multiscale modeling that combines the slow dynamics of
the large particles with the fast dynamics of the solvent into a unique
framework. The former is numerically solved via Molecular Dynamics and the
latter via a multi-component Lattice Boltzmann. The two techniques are coupled
together to allow for a seamless exchange of information between the
descriptions. Being based on a kinetic multi-component description of the fluid
species, the scheme is flexible in modeling charge flow within complex
geometries and ranging from large to vanishing salt concentration. The details
of the scheme are presented and the method is applied to the problem of
translocation of a charged polymer through a nanopores. In the end, we discuss
the advantages and complexities of the approach
Lattice and Continuum Modelling of a Bioactive Porous Tissue Scaffold
A contemporary procedure to grow artificial tissue is to seed cells onto a
porous biomaterial scaffold and culture it within a perfusion bioreactor to
facilitate the transport of nutrients to growing cells. Typical models of cell
growth for tissue engineering applications make use of spatially homogeneous or
spatially continuous equations to model cell growth, flow of culture medium,
nutrient transport, and their interactions. The network structure of the
physical porous scaffold is often incorporated through parameters in these
models, either phenomenologically or through techniques like mathematical
homogenization. We derive a model on a square grid lattice to demonstrate the
importance of explicitly modelling the network structure of the porous
scaffold, and compare results from this model with those from a modified
continuum model from the literature. We capture two-way coupling between cell
growth and fluid flow by allowing cells to block pores, and by allowing the
shear stress of the fluid to affect cell growth and death. We explore a range
of parameters for both models, and demonstrate quantitative and qualitative
differences between predictions from each of these approaches, including
spatial pattern formation and local oscillations in cell density present only
in the lattice model. These differences suggest that for some parameter
regimes, corresponding to specific cell types and scaffold geometries, the
lattice model gives qualitatively different model predictions than typical
continuum models. Our results inform model selection for bioactive porous
tissue scaffolds, aiding in the development of successful tissue engineering
experiments and eventually clinically successful technologies.Comment: 38 pages, 16 figures. This version includes a much-expanded
introduction, and a new section on nonlinear diffusion in addition to polish
throughou
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
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