360 research outputs found
Surface flow profiles for dry and wet granular materials by Particle Tracking Velocimetry; the effect of wall roughness
Two-dimensional Particle Tracking Velocimetry (PTV) is a promising technique
to study the behaviour of granular flows. The aim is to experimentally
determine the free surface width and position of the shear band from the
velocity profile to validate simulations in a split-bottom shear cell geometry.
The position and velocities of scattered tracer particles are tracked as they
move with the bulk flow by analyzing images. We then use a new technique to
extract the continuum velocity field, applying coarse-graining with the
postprocessing toolbox MercuryCG on the discrete experimental PTV data. For
intermediate filling heights, the dependence of the shear (or angular) velocity
on the radial coordinate at the free surface is well fitted by an error
function. From the error function, we get the width and the centre position of
the shear band. We investigate the dependence of these shear band properties on
filling height and rotation frequencies of the shear cell for dry glass beads
for rough and smooth wall surfaces. For rough surfaces, the data agrees with
the existing experimental results and theoretical scaling predictions. For
smooth surfaces, particle-wall slippage is significant and the data deviates
from the predictions. We further study the effect of cohesion on the shear band
properties by using small amount of silicon oil and glycerol as interstitial
liquids with the glass beads. While silicon oil does not lead to big changes,
glycerol changes the shear band properties considerably. The shear band gets
wider and is situated further inward with increasing liquid saturation, due to
the correspondingly increasing trend of particles to stick together
Charging and discharging a supercapacitor in molecular simulations
As the world moves more toward unpredictable renewable energy sources, better energy storage devices are required. Supercapacitors are a promising technology to meet the demand for short-term, high-power energy storage. Clearly, understanding their charging and discharging behaviors is essential to improving the technology. Molecular Dynamics (MD) simulations provide microscopic insights into the complex interplay between the dynamics of the ions in the electrolyte and the evolution of the charge distributions on the electrodes. Traditional MD simulations of (dis)charging supercapacitors impose a pre-determined evolving voltage difference between the electrodes, using the Constant Potential Method (CPM). Here, we present an alternative method that explicitly simulates the charge flow to and from the electrodes. For a disconnected capacitor, i.e., an open circuit, the charges are allowed to redistribute within each electrode while the sum charges on both electrodes remain constant. We demonstrate, for a model capacitor containing an aqueous salt solution, that this method recovers the charge–potential curve of CPM simulations. The equilibrium voltage fluctuations are related to the differential capacitance. We next simulate a closed circuit by introducing equations of motion for the sum charges, by explicitly accounting for the external circuit element(s). Charging and discharging of the model supercapacitor via a resistance proceed by double exponential processes, supplementing the usual time scale set by the electrolyte dynamics with a novel time scale set by the external circuit. Finally, we propose a simple equivalent circuit that reproduces the main characteristics of this supercapacitor
Segregation of large particles in dense granular flows: A granular Saffman effect?
We report on the scaling between the lift force and the velocity lag
experienced by a single particle of different size in a monodisperse dense
granular chute flow. The similarity of this scaling to the Saffman lift force
in (micro) fluids, suggests an inertial origin for the lift force responsible
for segregation of (isolated, large) intruders in dense granular flows. We also
observe an anisotropic pressure/stress field surrounding the particle, which
potentially lies at the origin of the velocity lag. These findings are relevant
for modelling and theoretical predictions of particle-size segregation. At the
same time, the suggested interplay between polydispersity and inertial effects
in dense granular flows with stress- and strain-gradients, implies striking new
parallels between fluids, suspensions and granular flows with wide application
perspectives
A simple efficient algorithm for molecular simulations of constant potential electrodes
Increasingly, society requires high power, high energy storage devices for applications ranging from electric vehicles to buffers on the electric grid. Supercapacitors are a promising contribution to meeting these demands, though there still remain unsolved practical problems. Molecular dynamics simulations can shed light on the relevant molecular level processes in electric double layer capacitors, but these simulations are computationally very demanding. Our focus here is on the algorithmic complexity of the constant potential method (CPM), which uses dedicated electrostatics solvers to maintain a fixed potential difference between two conducting electrodes. We show how any standard electrostatics solver—capable of calculating the energies and forces on all atoms—can be used to implement CPM with a minimum of coding. As an example, we compare our generalized implementation of CPM, based on invocations of the particle–particle–particle–mesh routine of the Large-scale Atomic/Molecular Massively Parallel Simulator, with a traditional implementation based on a dedicated re-implementation of Ewald summation. Both methods yield comparable results on four test systems, with the former achieving a substantial gain in speed and improved scalability. The step from dedicated electrostatic solvers to generic routines is made possible by noting that CPM’s traditional narrow Gaussian point-spread of atomic charges on the electrodes effectively endows point-like atoms with chemical hardness, i.e., an intra-atomic energy quadratic in the charge
The influence of material properties and process parameters on the spreading process in additive manufacturing
Laser powder bed fusion (LPBF) is an additive manufacturing (AM) technology.
To achieve high product quality, the powder is best spread as a uniform, dense
layer. The challenge for LPBF manufacturers is to develop a spreading process
that can produce a consistent layer quality for the many powders used, which
show considerable differences in spreadability. Therefore, we investigate the
influence of material properties, process parameters and the type of spreading
tool on the layer quality. The discrete particle method is used to simulate the
spreading process and to define metrics to evaluate the powder layer
characteristics. We found that particle shape and surface roughness in terms of
rolling resistance and interparticle sliding friction as well as particle
cohesion all have a major (sometimes surprising) influence on the powder layer
quality: more irregular shaped particles, rougher particle surfaces and/or
higher interfacial cohesion usually, but not always, lead to worse
spreadability. Our findings illustrate that there is a trade-off between
material properties and process parameters. Increasing the spreading speed
decreases layer quality for non- and weakly cohesive powders, but improves it
for strongly cohesive ones. Using a counter-clockwise rotating roller as a
spreading tool improves the powder layer quality compared to spreading with a
blade. Finally, for both geometries, a unique correlation between the quality
criteria uniformity and mass fraction is reported and some of the findings are
related to size-segregation during spreading.Comment: 38 pages, 24 figures, 2 table
Abundance and Distribution of Transposable Elements in Two Drosophila QTL Mapping Resources
Here we present computational machinery to efficiently and accurately identify transposable element (TE) insertions in 146 next-generation sequenced inbred strains of Drosophila melanogaster. The panel of lines we use in our study is composed of strains from a pair of genetic mapping resources: the Drosophila Genetic Reference Panel (DGRP) and the Drosophila Synthetic Population Resource (DSPR). We identified 23,087 TE insertions in these lines, of which 83.3% are found in only one line. There are marked differences in the distribution of elements over the genome, with TEs found at higher densities on the X chromosome, and in regions of low recombination. We also identified many more TEs per base pair of intronic sequence and fewer TEs per base pair of exonic sequence than expected if TEs are located at random locations in the euchromatic genome. There was substantial variation in TE load across genes. For example, the paralogs derailed and derailed-2 show a significant difference in the number of TE insertions, potentially reflecting differences in the selection acting on these loci. When considering TE families, we find a very weak effect of gene family size on TE insertions per gene, indicating that as gene family size increases the number of TE insertions in a given gene within that family also increases. TEs are known to be associated with certain phenotypes, and our data will allow investigators using the DGRP and DSPR to assess the functional role of TE insertions in complex trait variation more generally. Notably, because most TEs are very rare and often private to a single line, causative TEs resulting in phenotypic differences among individuals may typically fail to replicate across mapping panels since individual elements are unlikely to segregate in both panels. Our data suggest that “burden tests” that test for the effect of TEs as a class may be more fruitful
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar
In this work, we first describe a framework for the application of
Reinforcement Learning (RL) control to a radar system that operates in a
congested spectral setting. We then compare the utility of several RL
algorithms through a discussion of experiments performed on Commercial
off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of
convergence, radar detection performance achieved in a congested spectral
environment, and the ability to share 100MHz spectrum with an uncooperative
communications system. We examine policy iteration, which solves an environment
posed as a Markov Decision Process (MDP) by directly solving for a stochastic
mapping between environmental states and radar waveforms, as well as Deep RL
techniques, which utilize a form of Q-Learning to approximate a parameterized
function that is used by the radar to select optimal actions. We show that RL
techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the
conditions under which each approach is most effective.Comment: Accepted for publication at IEEE Intl. Radar Conference, Washington
DC, Apr. 2020. This is the author's version of the wor
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