280,339 research outputs found
Photon elastic scattering simulation: validation and improvements to Geant4
Several models for the simulation of photon elastic scattering are
quantitatively evaluated with respect to a large collection of experimental
data retrieved from the literature. They include models based on the form
factor approximation, on S-matrix calculations and on analytical
parameterizations; they exploit publicly available data libraries and
tabulations of theoretical calculations. Some of these models are currently
implemented in general purpose Monte Carlo systems; some have been implemented
and evaluated for the first time in this paper for possible use in Monte Carlo
particle transport. The analysis mainly concerns the energy range between 5 keV
and a few MeV. The validation process identifies the newly implemented model
based on second order S-matrix calculations as the one best reproducing
experimental measurements. The validation results show that, along with
Rayleigh scattering, additional processes, not yet implemented in Geant4 nor in
other major Monte Carlo systems, should be taken into account to realistically
describe photon elastic scattering with matter above 1 MeV. Evaluations of the
computational performance of the various simulation algorithms are reported
along with the analysis of their physics capabilities
An Ultra Fast Image Generator (UFig) for wide-field astronomy
Simulated wide-field images are becoming an important part of observational
astronomy, either to prepare for new surveys or to test measurement methods. In
order to efficiently explore vast parameter spaces, the computational speed of
simulation codes is a central requirement to their implementation. We introduce
the Ultra Fast Image Generator (UFig) which aims to bring wide-field imaging
simulations to the current limits of computational capabilities. We achieve
this goal through: (1) models of galaxies, stars and observational conditions,
which, while simple, capture the key features necessary for realistic
simulations, and (2) state-of-the-art computational and implementation
optimizations. We present the performances of UFig and show that it is faster
than existing public simulation codes by several orders of magnitude. It allows
us to produce images more quickly than SExtractor needs to analyze them. For
instance, it can simulate a typical 0.25 deg^2 Subaru SuprimeCam image (10k x
8k pixels) with a 5-sigma limiting magnitude of R=26 in 30 seconds on a laptop,
yielding an average simulation time for a galaxy of 30 microseconds. This code
is complementary to end-to-end simulation codes and can be used as a fast,
central component of observational methods relying on simulations.Comment: Submitted to Astronomy and Computing. 13 pages, 9 figure
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
Bending models of lipid bilayer membranes: spontaneous curvature and area-difference elasticity
We preset a computational study of bending models for the curvature
elasticity of lipid bilayer membranes that are relevant for simulations of
vesicles and red blood cells. We compute bending energy and forces on
triangulated meshes and evaluate and extend four well established schemes for
their approximation: Kantor and Nelson 1987, Phys. Rev. A 36, 4020, J\"ulicher
1996, J. Phys. II France 6, 1797, Gompper and Kroll 1996, J. Phys. I France 6,
1305, and Meyer et. al. 2003 in Visualization and Mathematics III, Springer,
p35, termed A, B, C, D. We present a comparative study of these four schemes on
the minimal bending model and propose extensions for schemes B, C and D. These
extensions incorporate the reference state and non-local energy to account for
the spontaneous curvature, bilayer coupling, and area-difference elasticity
models. Our results indicate that the proposed extensions enhance the models to
account for shape transformation including budding/vesiculation as well as for
non-axisymmetric shapes. We find that the extended scheme B is superior to the
rest in terms of accuracy, and robustness as well as simplicity of
implementation. We demonstrate the capabilities of this scheme on several
benchmark problems including the budding-vesiculating process and the
reproduction of the phase diagram of vesicles
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons
It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed in order to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, since most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent STDP and branch-strength potentiation could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that in a simplified setup these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles, and to acquire nonlinear computational capabilities, such as for example the capability to bind multiple input features. Hence our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms
Modeling performance of response surface methodology and artificial neural network
In recent years, response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering. Both RSM and ANN strategies have particular computational properties that makes them suitable for making predictions, but differ in their extrapolation and interpolation capabilities on complex non-linear processes, and thus potentially conflict in their predictive accuracy. This study models and compares the capabilities of RSM and ANN in predicting the tensile strength of a 6 mm thick mild steel gas tungsten arc welded plate based on the effects of input variables such as weld current, weld speed, gas flow rate and filler rod. The RSM and ANN based models for prediction were compared using the coefficient of determination criteria. With a higher value of 0.836, the ANN model proved to be a better modeling technique than the RSM model.Keywords: Soft Computing Techniques, Response Surface Method, Artificial Neural Networ
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
The predictive capabilities of turbulent flow simulations, critical for
aerodynamic design and weather prediction, hinge on the choice of turbulence
models. The abundance of data from experiments and simulations and the advent
of machine learning have provided a boost to these modeling efforts. However,
simulations of turbulent flows remain hindered by the inability of heuristics
and supervised learning to model the near-wall dynamics. We address this
challenge by introducing scientific multi-agent reinforcement learning
(SciMARL) for the discovery of wall models for large-eddy simulations (LES). In
SciMARL, discretization points act also as cooperating agents that learn to
supply the LES closure model. The agents self-learn using limited data and
generalize to extreme Reynolds numbers and previously unseen geometries. The
present simulations reduce by several orders of magnitude the computational
cost over fully-resolved simulations while reproducing key flow quantities. We
believe that SciMARL creates new capabilities for the simulation of turbulent
flows
A numerical analysis of the plastic wake influence on plasticity induced crack closure
Fatigue crack closure has been studied by means of finite element method since long time
ago. Most work has been performed considering bi-dimensional models. Lately, the use of threedimensional
models has been extended. Nevertheless, the methodology employed has been taken from
that developed for bi-dimensional cases.
There are a great number of previous bi-dimensional studies which analyse different numerical
parameters and optimise them. The current computational capabilities allow a comprehensive study of
the influence of the different modelling parameters in a similar way to those studies carried out with bidimensional
models, with the advantage, that the evolution along the thickness of the analysed
parameters can be taken into consideration.
In particular, one of the key issues is related to the plastic wake length which is developed during the
previous loading cycles. This residual stresses have a great influence on the crack opening and closure
values. As the numerical analysis are complex and computationally expensive, the length of the
simulated wake is a critical parameter.
In this work, a comprehensive study of the effect of the plastic wake in fatigue crack closure is made.
On this purpose, a CT aluminium specimen has been modelled three-dimensionally and several
calculations have been made in order to evaluate the influence of the simulated plastic wake length. The
numerical analysis is made in terms of crack closure and opening values as in terms of the stress and
strain fields near the crack front.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
The flow of power law fluids in elastic networks and porous media
The flow of power law fluids, which include shear thinning and shear
thickening as well as Newtonian as a special case, in networks of
interconnected elastic tubes is investigated using a residual based pore scale
network modeling method with the employment of newly derived formulae. Two
relations describing the mechanical interaction between the local pressure and
local cross sectional area in distensible tubes of elastic nature are
considered in the derivation of these formulae. The model can be used to
describe shear dependent flows of mainly viscous nature. The behavior of the
proposed model is vindicated by several tests in a number of special and
limiting cases where the results can be verified quantitatively or
qualitatively. The model, which is the first of its kind, incorporates more
than one major non-linearity corresponding to the fluid rheology and conduit
mechanical properties, that is non-Newtonian effects and tube distensibility.
The formulation, implementation and performance indicate that the model enjoys
certain advantages over the existing models such as being exact within the
restricting assumptions on which the model is based, easy implementation, low
computational costs, reliability and smooth convergence. The proposed model can
therefore be used as an alternative to the existing Newtonian distensible
models; moreover it stretches the capabilities of the existing modeling
approaches to reach non-Newtonian rheologies.Comment: 12 pages, 4 figure
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