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