74,336 research outputs found
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
Bridging Physics and Biology Teaching through Modeling
As the frontiers of biology become increasingly interdisciplinary, the
physics education community has engaged in ongoing efforts to make physics
classes more relevant to life sciences majors. These efforts are complicated by
the many apparent differences between these fields, including the types of
systems that each studies, the behavior of those systems, the kinds of
measurements that each makes, and the role of mathematics in each field.
Nonetheless, physics and biology are both sciences that rely on observations
and measurements to construct models of the natural world. In the present
theoretical article, we propose that efforts to bridge the teaching of these
two disciplines must emphasize shared scientific practices, particularly
scientific modeling. We define modeling using language common to both
disciplines and highlight how an understanding of the modeling process can help
reconcile apparent differences between the teaching of physics and biology. We
elaborate how models can be used for explanatory, predictive, and functional
purposes and present common models from each discipline demonstrating key
modeling principles. By framing interdisciplinary teaching in the context of
modeling, we aim to bridge physics and biology teaching and to equip students
with modeling competencies applicable across any scientific discipline.Comment: 10 pages, 2 figures, 3 table
Laser-plasma interactions with a Fourier-Bessel Particle-in-Cell method
A new spectral particle-in-cell (PIC) method for plasma modeling is presented
and discussed. In the proposed scheme, the Fourier-Bessel transform is used to
translate the Maxwell equations to the quasi-cylindrical spectral domain. In
this domain, the equations are solved analytically in time, and the spatial
derivatives are approximated with high accuracy. In contrast to the
finite-difference time domain (FDTD) methods that are commonly used in PIC, the
developed method does not produce numerical dispersion, and does not involve
grid staggering for the electric and magnetic fields. These features are
especially valuable in modeling the wakefield acceleration of particles in
plasmas. The proposed algorithm is implemented in the code PLARES-PIC, and the
test simulations of laser plasma interactions are compared to the ones done
with the quasi-cylindrical FDTD PIC code CALDER-CIRC.Comment: submitted to Phys. Plasma
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