Granular Streams Research Articles

Direct observation of particle clustering and gas breakdown in charged granular streams

The dust layer formed by charged particles deposition significantly limit the collection efficiency and stable application of electrostatic precipitators (ESPs). However, the deposition characteristics and re-entrainment mechanism of charged particles in electric field still remain unclear. In this study, a transparent ESP was designed to investigate the dynamic process of charged particle deposition, clustering and gas breakdown in electric field. The results showed that deposited particles will aggregate and gradually grow in chains in high-voltage electric field. The length of chain agglomerates can exceed 10 mm. And interelectrode gas breakdown could suddenly occur, which significantly changed the particle charging characteristics and increased particle emission at the outlet. In addition, a perforated plate was proposed to inhibit the gas breakdown and particle re-entrainment. The mass concentrations of particle re-entrainment decreased by 85.2 % and 79.1 % for the spike electrode and the arista electrode at the applied voltage of 30 kV, respectively.

Granular Flow–Obstacle Interaction and Granular Dam Break Using the S-H Model with the TVD-MacCormack Scheme

An accurate second-order spatial and temporal finite-difference scheme is applied to solve the dynamics model of a depth-averaged avalanche. Within the framework of the MacCormack scheme, a total variation diminishing term supplements the corrector step to suppress large oscillations in domains with steep gradients. The greatest strength of the scheme lies in its high computational efficiency while maintaining satisfactory accuracy. The performance of the scheme is tested on a granular flume flow–obstacle interaction scenario and a granular dam breaking scenario. In the former, the flume flow splits into two granular streams when an obstacle is encountered. The opening between the two granular streams widens when the side length of the obstacle increases. In the simulation, shock waves with a fan-shaped configuration are captured, and successive waves in the tail of the avalanche between the two streams are observed. In the latter scenario, the average values and the fluctuations in the flow rate and velocity (at relatively steady state) decrease with the width of the breach. The capture of complex and typical granular-flow phenomena indicates the applicability and effectiveness of combining the TVD-MacCormack Scheme and S-H model to simulate dam breaking and inclined flow–obstacle interaction cases. In this study, the dense granular flow strikes on a rigid obstacle that is described by a wall boundary, rather than a topographic feature with a finite slope. This shows that the TVD-MacCormack scheme has a shock-capturing ability. The results of granular dam break simulations also revealed that the boundary conditions (open or closed) affect the collapse of the granular pile, i.e., the grains evenly breached out under closed boundary conditions, whereas the granules breaching out of the opening were mostly grains adjacent to the boundaries under open boundary conditions.

Detachment of strong shocks in confined granular flows

Granular flows are highly dissipative due to frictional resistance and inelasticity in collisions among grains. They are known to exhibit shock waves at velocities that are easily achieved in industrial and nature-driven flows such as avalanches and landslides. This experimental work investigates the formation of strong shock waves on triangular obstacles placed in a dry rapid granular stream in a confined two-dimensional set-up. Oblique attached shock waves are formed for mild turning angles and higher flow velocities, whereas strong bow shock waves are formed for higher turning angles and slower granular streams. A shadowgraph imaging technique elucidates interesting characteristics of the shock waves, especially in the vicinity of shock detachment. Velocity distributions in the form of scatter plots and probability distribution functions are calculated from the flow field data obtained by particle imaging velocimetry. The flow field around the granular shock wave region represents a bimodal distribution of velocities with two distinct peaks, one representing the supersonic flow within the free stream, and the other corresponding to the subsonic faction downstream of a shock wave. Connecting the two is a population that does not directly belong to either of the modes, constituting the non-equilibrium shock wave region. The effect of grain size and scaling, for fixed free-stream conditions and fixed channel width, on the shock detachment is presented. The mechanisms of the static heap formation and the shock detachment process in a confined environment are discussed.

A Mathematical, Graphical and Visual Approach to Granular Synthesis Composition

We show a method for Granular Synthesis Composition based on a mathematical modeling for the musical gesture. Each gesture is drawn as a curve generated from a particular mathematical model (or function) and coded as a MATLAB script. The gestures can be deterministic through defining mathematical time functions, hand free drawn, or even randomly generated. This parametric information of gestures is interpreted through OSC messages by a granular synthesizer (Granular Streamer). The musical composition is then realized with the models (scripts) written in MATLAB and exported to a graphical score (Granular Score). The method is amenable to allow statistical analysis of the granular sound streams and the final music composition. We also offer a way to create granular streams based on correlated pair of grains parameters.

Packing fraction of clusters formed in free-falling granular streams based on flash x-ray radiography

We study the packing fraction of clusters in free-falling streams of spherical and irregularly shaped particles using flash x-ray radiography. The estimated packing fraction of clusters is low enough to correspond to coordination numbers less than 6. Such coordination numbers in numerical simulations correspond to aggregates that collide and grow without bouncing. Moreover, the streams of irregular particles evolved faster and formed clusters of larger sizes with lower packing fraction. This result on granular streams suggests that particle shape has a significant effect on the agglomeration process of granular materials.

Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification

As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.

Observations of the Agglomeration Process of Granular Streams Using a Flash X-ray Radiography Technique

Observations of the Agglomeration Process of Granular Streams Using a Flash X-ray Radiography Technique

Prediction Intervals for Granular Data Streams Based on Evolving Type-2 Fuzzy Granular Neural Network Dynamic Ensemble

Granular data streams (GDSs) are a class of high-level abstract multitime scale description of data streams. Prediction intervals (PIs) for GDSs that provide estimated values as well as their corresponding reliability play an important role for assisting on-site workers to perceive the nonstationary environment in real time. However, constructing reliable PIs for GDSs constitutes a significant challenge. To provide a solution to the problem, an interval type-2 (IT2) fuzzy granular neural network (FGNN) dynamic ensemble approach (IT2FGNNDEnsemble) is proposed in this article. To fully reflect the uncertainty of GDSs, an interval value learning algorithm based IT2FGNN is developed, which can automatically generate, prune, merge, and realize recall in a single-pass learning mode. In addition, an evolving dynamic ensemble method is presented by providing an adaptive structure that considers a tradeoff between coverage and width of PIs, which can dynamically generate and prune the element of an ensemble according to current data tendency. A number of synthetic and industrial data streams experimentally validate the performance of the proposed IT2FGNNDEnsemble by using the state-of-the-art comparative methods. It is demonstrated that the proposed approach exhibits a good performance on PIs for practical applications.

Shock–shock interactions in granular flows

Shock–shock interaction structures and a newly discovered dynamic instability in granular streams resulting from such interactions are presented. Shock waves are generated by placing two similar triangular wedges in a gravity-driven granular stream. When the shock waves interact, grains collapse near the centre region of the wedges and a slow-moving concentrated diamond-shaped streak of grains is formed that grows as the inclination of the channel is increased. The diamond streak, under certain geometric conditions, is found to become unstable and start oscillating in the direction transverse to the mainstream. When the wedges are placed too close to each other, the granular flux of the incoming stream is unable to pass through the small gap, resulting in the formation of a single bow shock enveloping both the wedges. Experiments are performed for a wide range of flow speeds, wedge angles and wedge separations to investigate the interaction zone. We discuss a possible mechanism for the formation of the central streak and the associated dynamic instability observed for specific physical parameters.

Laboratory experiments on agglomeration of particles in a granular stream

Inelastic collisions occur among regolith particles, such as those in the ejecta curtain from a crater, and may cause clustering or agglomeration of particles and thus produce discrete patterns of ejecta deposits around a crater. Previous studies have shown that clusters, and even agglomerates, are formed via mutual, inelastic collisions of spherical particles due to adhering forces between particles in granular streams. To investigate the condition of agglomerate formation in granular streams, we conducted laboratory experiments of granular streams using both spherical and irregular, non-spherical particles. Measurements of particle adhesion in this study were performed using a centrifugal separation method, in contrast to the previous study in which atomic force microscopy (AFM) was used. This enabled simultaneous measurements of multiple particles of various shapes for a statistical analysis of the results. With similar relative velocities and adhesion values, irregular particles were found to form agglomerates much more easily than spherical particles. The axial ratio of the agglomerates of spherical particles and irregular particles was similar and was in accordance with those observed in previous laboratory studies, whereas the size of the agglomerates of irregular particles was larger than the size of spherical particles. The degree of agglomeration and the size of agglomerates can be used as an indicator of the shape or adhesive force of the particles in granular stream. Our findings on agglomeration in granular streams could provide new insights into the origin of rays on airless bodies and grooves on Phobos.

Direct observation of particle interactions and clustering in charged granular streams

By eliminating the effects of gravity with a free-falling camera, high-resolution imaging of charged grains reveals Keplerian orbits and electrostatically stable clusters—with implications for astrophysical and industrial cluster formation.

Stability of Freely Falling Granular Streams

A freely falling stream of weakly cohesive granular particles is modeled and analyzed with the help of event driven simulations and continuum hydrodynamics. The former show a breakup of the stream into droplets, whose size is measured as a function of cohesive energy. Extensional flow is an exact solution of the one-dimensional Navier-Stokes equation, corresponding to a strain rate, decaying like t(-1) from its initial value, γ[over ˙](0). Expanding around this basic state, we show that the flow is stable for short times, γ[over ˙](0)t<<1, whereas for long times, γ[over ˙](0)t>>1, perturbations of all wavelengths grow. The growth rate of a given wavelength depends on the instant of time when the fluctuation occurs, so that the observable patterns can vary considerably.

Droplet and cluster formation in freely falling granular streams

Particle beams are important tools for probing atomic and molecular interactions. Here we demonstrate that particle beams also offer a unique opportunity to investigate interactions in macroscopic systems, such as granular media. Motivated by recent experiments on streams of grains that exhibit liquid-like breakup into droplets, we use molecular dynamics simulations to investigate the evolution of a dense stream of macroscopic spheres accelerating out of an opening at the bottom of a reservoir. We show how nanoscale details associated with energy dissipation during collisions modify the stream's macroscopic behavior. We find that inelastic collisions collimate the stream, while the presence of short-range attractive interactions drives structure formation. Parameterizing the collision dynamics by the coefficient of restitution (i.e., the ratio of relative velocities before and after impact) and the strength of the cohesive interaction, we map out a spectrum of behaviors that ranges from gaslike jets in which all grains drift apart to liquid-like streams that break into large droplets containing hundreds of grains. We also find a new, intermediate regime in which small aggregates form by capture from the gas phase, similar to what can be observed in molecular beams. Our results show that nearly all aspects of stream behavior are closely related to the velocity gradient associated with vertical free fall. Led by this observation, we propose a simple energy balance model to explain the droplet formation process. The qualitative as well as many quantitative features of the simulations and the model compare well with available experimental data and provide a first quantitative measure of the role of attractions in freely cooling granular streams.

Rupture and clustering in granular streams

Rupture and clustering in granular streams

High-speed tracking of rupture and clustering in freely falling granular streams

Thin streams of liquid commonly break up into characteristic droplet patterns owing to the surface-tension-driven Plateau-Rayleigh instability. Very similar patterns are observed when initially uniform streams of dry granular material break up into clusters of grains, even though flows of macroscopic particles are considered to lack surface tension. Recent studies on freely falling granular streams tracked fluctuations in the stream profile, but the clustering mechanism remained unresolved because the full evolution of the instability could not be observed. Here we demonstrate that the cluster formation is driven by minute, nanoNewton cohesive forces that arise from a combination of van der Waals interactions and capillary bridges between nanometre-scale surface asperities. Our experiments involve high-speed video imaging of the granular stream in the co-moving frame, control over the properties of the grain surfaces and the use of atomic force microscopy to measure grain-grain interactions. The cohesive forces that we measure correspond to an equivalent surface tension five orders of magnitude below that of ordinary liquids. We find that the shapes of these weakly cohesive, non-thermal clusters of macroscopic particles closely resemble droplets resulting from thermally induced rupture of liquid nanojets.

Granular streams rheology and mechanics

In the present paper we describe a model of the transition from a quasi static to dynamic regime in a granular stream. The model was developed using the results of experiments carried out on a rotating drum partially filled with sand grains or glass beads; the experiments provide information about rheology through grain velocity profiles and through the grain velocity covariance tensor. The model relies on several assumptions: we express the frictional stress component, due to prolonged contacts between particles, with a Coulomb law, assuming that the friction angle is equal to the true friction angle between the particle surfaces at contact plus the angle between the mean contact plane and the shearing plane. The difficulties involved in measuring the volume concentration of the grains with the necessary precision and the substantial impossibility of checking the results, suggest a closure based on the contact angle. We assume that the average contact angle in the frictional regime is the same as the average collisional angle in the collisional regime. The collisional contribution to the global stress is expressed as a function of the mean concentration, the local grain velocity gradient and the average contact angle between shearing layers (we implicitly assume that collisions between particles are binary and that multiple contacts between particles in movement generate friction); the kinetic contribution is not taken into account because of its minor relevance at high concentration.The numerical model gives a satisfactory reproduction of the experimental grain velocity profiles.