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Soft gripping provides the potential for high performance in challenging tasks through morphological computing; however, design explorations are limited by a combination of a difficulty in generating useful models and use of laborious fabrication techniques. We focus on a class of grippers based on granular jamming that are particularly difficult to model and introduce a "one shot" technique that exploits multimaterial three-dimensional (3D) printing to create entire grippers, including membrane and grains, in a single print run. This technique fully supports the de facto physical generate-and-test methodology used for this class of grippers, as entire design iterations can be fitted onto a single print bed and fabricated from Computer-Aided Design (CAD) files in a matter of hours. Initial results demonstrate the approach by rapidly prototyping in materio solutions for two challenging problems in unconventional design spaces; a twisting gripper that uses programmed deformations to reliably pick a coin, and a multifunctional legged robot paw that offers the ability for compliant locomotion over rough terrains, as well as being able to pick objects in cluttered natural environments. The technique also allows us to easily characterize the design space of multimaterial printed jamming grippers and provide some useful design rules. The simplicity of our technique encourages and facilitates creativity and innovation. As such, we see our approach as an enabling tool to make informed principled forays into unconventional design spaces and support the creation of a new breed of novel soft actuators.

In metal additive manufacturing (AM), powder bed fusion technologies such as selective laser melting and binder jetting rely on the spreading of fine metal powder to build up the layers of a part. This work studies the complex interaction between metal powder particles and the recoater. We apply the Discrete Element Method (DEM) to a calibrated cohesive Ti-6Al-4V powder model, matching the particle properties to experimental measurements of size, shape and inter-particle forces. We simulate powder bed fusion recoating at varying speeds and layer thicknesses with two different recoater geometries: a toothed rake and a solid blade. Our results demonstrate that the recoating mechanism induces significant granular convection, with the recoater velocity and geometry strongly influencing the degree of particle circulation and size segregation. Notably a toothed rake recoater improved particle circulation – while the solid blade recoater increases size segregation within the heap. Furthermore the recoater was found to filter fine and coarse particles, allowing smaller particles to flow underneath the recoater, and larger particles through its teeth. Our results demonstrate the key mechanisms which drive granular convection during recoating and how the fine details of recoater geometry impacts surface layer roughness and final part quality.

Landslides may be triggered by a variety of external factors and can lead to dramatic human and economic consequences. Systematically investigating all possible parameters that could potentially influence the extent of a landslide is costly and generally not practical. In this paper, a methodology to numerically assess the impact of potential landslides is proposed. In this approach the landslide simulations were carried out using finite element model estimates of potential failure volume as an input into a discrete element model which is used to assess run-out and consequences. A statistical design of experiments was implemented to determine the most important factors influencing the landslide extent thereby significantly reducing the number of simulations required. The method is illustrated on a case study of potential failure of an overburden dump at a coal mine. Over the parameter ranges considered, the particle–particle (rock block to rock block) friction coefficient and the volume of failing material were determined to be the most important factors influencing the extent of the landslide. A systematic analysis of varying the particle–particle friction coefficient was then undertaken to better understand the dynamics of the collapse and different types of collapses were identified between low and high inter-particle friction coefficients. The methodology proposed in this paper should be of interest to practitioners as a way to thoroughly yet efficiently identify and assess the main factors influencing a potential landslide on sites at risk.

Accurate modeling of rebound kinematics in particle-substrate collisions is essential in a wide range of applications like milling and mixing. To accurately model these real-world collisions the forces arising due to elastoplastic deformation and friction need to be modelled accurately. In this study the use of frictional boundary conditions in modeling elastic collisions with Smooth Particle Hydrodynamics (SPH) is explored. The collision dynamics for an oblique impact of a 3D spherical granule on an elastic substrate is assessed for SPH (a) without any special treatment for friction and (b) using Coulomb's friction model, and it is identified that these approaches are inaccurate in some instances. To resolve this, spring-based contact models are incorporated in SPH to develop a new method for improved contact modeling. We call this method Collisional SPH. This Collisional SPH method predicts both rebound kinematics and surface deformation accurately. This method opens new avenues for further development in modeling collisional deformations and collision dynamics in granular systems using SPH.

Rationale: There are at least four key pathophysiological endotypes that contribute to obstructive sleep apnea (OSA) pathophysiology. These include 1) upper-airway collapsibility (Pcrit); 2) arousal threshold; 3) loop gain; and 4) pharyngeal muscle responsiveness. However, an easily interpretable model to examine the different ways and the extent to which these OSA endotypes contribute to conventional polysomnography-defined OSA severity (i.e., the apnea–hypopnea index) has not been investigated. In addition, clinically deployable approaches to estimate OSA endotypes to advance knowledge on OSA pathogenesis and targeted therapy at scale are not currently available. Objectives: Develop an interpretable data-driven model to 1) determine the different ways and the extent to which the four key OSA endotypes contribute to polysomnography-defined OSA severity and 2) gain insight into how standard polysomnographic and clinical variables contribute to OSA endotypes and whether they can be used to predict OSA endotypes. Methods: Age, body mass index, and eight polysomnography parameters from a standard diagnostic study were collected. OSA endotypes were also quantified in 52 participants (43 participants with OSA and nine control subjects) using gold-standard physiologic methodology on a separate night. Unsupervised multivariate principal component analyses and data-driven supervised machine learning (decision tree learner) were used to develop a predictive algorithm to address the study objectives. Results: Maximum predictive performance accuracy of the trained model to identify standard polysomnography-defined OSA severity levels (no OSA, mild to moderate, or severe) using the using the four OSA endotypes was approximately twice that of chance. Similarly, performance accuracy to predict OSA endotype categories (“good,” “moderate,” or “bad”) from standard polysomnographic and clinical variables was approximately twice that of chance for Pcrit and slightly lower for arousal threshold. Conclusions: This novel approach provides new insights into the different ways in which OSA endotypes can contribute to polysomnography-defined OSA severity. Although further validation work is required, these findings also highlight the potential for routine sleep study and clinical data to estimate at least two of the key OSA endotypes using data-driven predictive analysis methodology as part of a clinical decision support system to inform scalable research studies to advance OSA pathophysiology and targeted therapy for OSA.

Metal additive manufacturing based on powder bed fusion processes is increasingly important. However, highly transient physical phenomena that occur in these processes at different length scales are difficult to observe. Challenging and costly experiments are usually needed to obtain data for process understanding and improvement. Computational modelling of powder-bed fusion processes is therefore important from several points of view. These include better process understanding, optimisation of process parameters and component designs, prediction of component properties, qualification of components and to assist process control. Several physical processes have to be treated to develop a complete model, namely the raking of the powder bed surface, the transfer of energy from the laser or electron beam to the metal, the melting and solidification of the powder, the flow of liquid metal in the melt pool, the heat transfer from the melt pool to the surrounding powder and solid metal, the evolution of the microstructure, and the residual stress and deformation of the component. These processes occur at very different scales, and have to be treated using several different computational techniques. In addition, the interdependency of some of the processes has to be accounted for. This paper discusses the rationale for developing a complete model, progress in developing sub-models of the different physical processes, and the framework that is envisaged to combine the sub-models into a predictive model of the additive manufacturing process.

This second part paper explores rock breakage mechanisms, the life cycle of rocks in mills and the strong influence of end walls on charge motion within mills. We present recent advances in particle-based modelling of mills for comminution focused around wear and the effect of slurry and slurry phase grinding. Three mill scenarios are considered: 1. Media flow and the resulting wear evolution of the belly and end wall liners and the resulting change in mill performance for a full industrial scale dry ball mill (modelled using DEM) 2. Axial slurry transport and mixing in a wet overflow industrial scale ball mill (modelled using fully coupled DEM and SPH) 3. Effect of mill speed on slurry and solid charge motion and the resulting grinding of fine particles in a 1.8 m diameter wet Hardinge pilot mill (modelled using fully coupled DEM and SPH with advection-diffusion-population balance equations solved for the slurry size distribution for each SPH particle) These demonstrate the nature and level of fidelity that is now possible to include in particle-scale comminution models. They provide insights into the critical importance of curtain flows generated by the end walls of tumbling mills, on wear behaviour on liners, on the structure of slurry pools and mill discharge and on the adverse effect on grinding of increasing mill speed.

Particle scale modelling of comminution processes can provide significant insight into the flow of particles, their breakage, the effect of slurry, wear and energy utilisation within these machines. The ability to use such models to assist in faster and lower cost design of new comminution devices and in the improvement of existing ones will be critical to the ability of industry to respond to the substantive challenges facing mineral processing in the next decade. These challenges are reviewed and drivers for change are discussed. Understanding individual unit process performance needs to be in the context of the flowsheets in which they are used so this is also reviewed. Advances in particle based comminution modelling are presented with this work divided into two parts. This first part focuses on recent advances in particle based modelling of crushing. Three crusher types are used to demonstrate these capabilities: 1. Twin roll crusher 2. Cone crusher 3. Vertical Shaft Impactor (VSI) These show the nature and level of fidelity that is now possible to include in particle scale crusher models including breakage of non-spherical particles and prediction of the product size distribution and throughput.

Granular jamming has recently become popular in soft robotics with widespread applications including industrial gripping, surgical robotics and haptics. Previous work has investigated the use of various techniques that exploit the nature of granular physics to improve jamming performance, however this is generally underrepresented in the literature compared to its potential impact. We present the first research that exploits vibration-based fluidisation actively (e.g., during a grip) to elicit bespoke performance from granular jamming grippers. We augment a conventional universal gripper with a computer-controllled audio exciter, which is attached to the gripper via a 3D printed mount, and build an automated test rig to allow large-scale data collection to explore the effects of active vibration. We show that vibration in soft jamming grippers can improve holding strength. In a series of studies, we show that frequency and amplitude of the waveforms are key determinants to performance, and that jamming performance is also dependent on temporal properties of the induced waveform. We hope to encourage further study focused on active vibrational control of jamming in soft robotics to improve performance and increase diversity of potential applications., Comment: arXiv admin note: substantial text overlap with arXiv:2109.10496

Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacuum pump and function by pressing the un-jammed gripper against a target object and evacuating the air to transition the material to a jammed (solid) state, gripping the target object. However, due to the complex interactions between grain morphology and target object shape, much uncertainty still remains regarding optimal constituent grain shapes for specific gripping applications. We address this challenge by utilising a modern Evolutionary Algorithm, NSGA-III, combined with a Discrete Element Method soft robot model to perform a multi-objective optimisation of grain morphology for use in jamming grippers for a range of target object sizes. Our approach optimises the microscopic properties of the system to elicit bespoke functional granular material performance driven by the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Results establish the important contribution of grain morphology to gripper performance and the critical role of local surface curvature and the length scale governed by the relative sizes of the grains and target object.

An emerging and increasingly pervasive school of thought is that large, long-lived and largely molten magma chambers are transient to non-existent in Earth’s history1–13. These ideas attempt to supplant the classical paradigm of the ‘big magma tank’ chambers in which the melt differentiates, is replenished, and occasionally feeds the overlying volcanoes14–23. The stratiform chromitites in the Bushveld Complex – the largest magmatic body in the Earth’s crust24 – however, offers strong contest to this shifting concept. Several chromitites in this complex occur as layers up to 2 metres in thickness and more than 400 kilometres in lateral extent, implying that chromitite-forming events were chamber-wide phenomena24–27. Field relations and microtextural data, specifically the relationship of 3D coordination number and grain size, indicate that the chromitites grew as a 3D framework of touching chromite grains directly at the chamber floor from a melt saturated in chromite only28–30. Mass-balance estimates dictate that a 1 to 4 km thick column of this melt26,31,32 is required to form each of these chromitite layers. Therefore, an enormous volume of melt (>1,00,000 km3)24,25 must have been involved in the generation of all the Bushveld chromitite layers, with half of this melt being expelled from the magma chamber24,26. We therefore argue that the very existence of thick and laterally extensive chromitite layers in the Bushveld and other layered intrusions strongly buttress the classical paradigm of ‘big magma tank’ chambers.

A new multi-stage three-dimensional transient computational model to simulate powder bed fusion (L-PBF) additive manufacturing (AM) processes is presented. The model uses the discrete element method (DEM) for powder flow simulation, an extended smoothed particle hydrodynamics (SPH) for melt pool dynamics and a semi-empirical microstructure evolution strategy to simulate the evolving temperature and microstructure of non-spherical Ti-6Al-4V powder grains undergoing L-PBF. The highly novel use of both DEM and SPH means that varied physics such as collisions between non-spherical powder grains during the coating process and heat transfer, melting, solidification and microstructure evolution during the laser fusion process can be simulated. The new capability is demonstrated by applying a complex representative laser scan pattern to a single-layer Ti-6Al-4V powder bed. It is found that the fast cooling rate primarily leads to a transition between the β and α martensitic phases. A minimal production of the α Widmanstatten phase at the outer edge of the laser is also noted due to an in situ heat treatment effect of the martensitic grains near the laser. This work demonstrates the potential of the coupled DEM/SPH computational model as a realistic tool to investigate the effect of process parameters such as powder morphology, laser scan speed and power characteristics on the Ti-6Al-4V powder bed microstructure.

The accuracy of SPH in predicting rebound kinematics in granular flow applications involving non-spherical granules is investigated. For this, the rebound behavior of an ellipsoidal granule impacting an elastic substrate is analyzed for different impact angles and for different initial orientations of the granule. The friction modeling capabilities of SPH are investigated by comparing the rebound spin predicted by the SPH simulations (a) without any special friction model and (b) using Coulomb’s friction model, with DEM results. This study presents the potential of using SPH in analysing granular flow applications and brings to light the limitations associated with the existing friction modeling capabilities in SPH.

Jamming is a popular and versatile soft robotic mechanism, enabling new systems to be developed that can achieve high stiffness variation with minimal volume variation. Numerous applications have been reported, including deep-sea sampling, industrial gripping, and use as paws for legged locomotion. This review explores the state-of-the-art for the three classes of jamming actuator: granular, layer and fibre jamming. We highlight the strengths and weaknesses of these soft robotic systems and propose opportunities for further development. We describe a number of trends, promising avenues for innovative research, and several technology gaps that could push the field forwards if addressed, including the lack of standardization for evaluating the performance of jamming systems. We conclude with perspectives for future studies in soft jamming robotics research, particularly elucidating how emerging technologies, including multi-material 3D printing, can enable the design and creation of increasingly diverse and high-performance soft robotic mechanisms for a myriad of new application areas.

Granular materials, such as sands, grains, soils and powders are central to many industrial processes from mining and food production to pharmaceuticals and construction. These materials display many interesting properties, including their ability to flow like a liquid at low densities and jam in to a solid state at high densities. However, controlling the microscopic properties of granular media to elicit bespoke functional granular systems remains challenging due to the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Here, we investigate the use of evolution to explore the functional landscapes of granular systems. We employ a superellipsoid representation of the particle shape, and examine a range of bi-disperse systems of prolate particles. Results show the ability to determine optimal particle morphologies and trade-offs for density and two different structural order measures. This represents an important further step towards the creation of bespoke jammed systems with a range of practical applications across broad swathes of industry.

Coupled DEM+SPH models are able to predict the motion of the coarse particulates and the slurry phase in a SAG mill. An extension of this model is proposed in which breakage of coarser particles (which are resolved in the DEM sub-model) in accordance with the incremental damage theory is included. This allows direct prediction of the resident size distribution of the rock component of the charge. It also allows the finer unresolved progeny from the breakage to be added to the slurry whose rheological properties then become a dynamic prediction of the model via the evolving local solids density of the slurry. It also allows transport of the fine material within and from the mill to be predicted. This model is demonstrated for an industry standard 1.8 m diameter by 0.6 m long pilot SAG mill. It allows the nature of the damage accumulation by the rock particles and its linkage to the flow structure of the charge to be explored. A flux of particles rebounding from the liner in the impact zone of the mill colliding with cataracting material produces the strongest incremental damage. Damage is also generated by cascading material arriving in the toe region. The presence of a slurry pool restricts the opportunity for incremental damage from the rebounding particles in the impact zone by damping rebound in places where particles fall directly into slurry. Finally, the coupled DEM+breakage+SPH model enables a mechanistic linkage between the slurry properties and the rock breakage.

Applying DEM to prediction of tumbling mill performance is challenging because several different modes of breakage are active in the process. Here we use measured data from a well characterised ore in a well instrumented, 1.2 m diameter pilot scale mill to validate direct DEM prediction of particle size reduction. The key comminution mechanisms involved for a SAG mill are: (1) incremental breakage where parent particles break into progeny based on the cumulative energy absorption above the elastic damage threshold, (2) abrasion, and (3) chipping/rounding due to preferential contact and breakage of corners and edges of non-round particles. In this paper, a method for including incremental damage breakage in DEM is presented. The inclusion of all the size reduction mechanisms in the same DEM framework allows direct prediction of the evolution of the resident rock particle size and shape distributions and the product throughput rate. The surface mass loss mechanisms are shown to be critical for reducing the particle size to the point where the accumulation of incremental damage becomes significant leading to body breakage of these damaged particles. The energy split between ball and rock is also important for exceeding the elastic threshold and creating damage. Comparison of the predicted particle sizes at the completion of ten minutes of grinding operation with the measured experimental values from the pilot mill provides quantitative validation of the breakage predictions of this DEM breakage model.

Discrete Element Method (DEM) simulation with non-round particles and including breakage has been used to understand the breakage behaviour and operating performance of an industrial scale cone crusher using a representative ore. The breakage model uses a replacement strategy and impact energy specific progeny size data from a Drop Weight Test (DWT). There is a strong variation in the breakage behaviour with height in the compression region as the differing profiles of the concave and mantle create five different regions with monotonically decreasing width and differing degrees of convergence between the surfaces. These control the rate of motion and the ability to load and break the particles, and determine whether high forces are generated via multi-particle stress chains or as single particle loading directly from the liner surfaces. The larger feed particles jam in the compression zone prior to breakage and cause observable obstruction to the flow of finer material and strong non-uniformity in the flow of product down the lower part of the mantle. Trends in the coarseness of product and changes in steady state throughput are identified with changes in material properties (rock breakage energy and friction coefficient) and crusher operating parameters (Closed Side Setting and crusher rotation rate).

The notion of stationary Apollonian packings in the d-dimensional Euclidean space is introduced as a mathematical formalization of so-called random Apollonian packings and rotational random Apollonian packings, which constitute popular grain packing models in physics. Apart from dealing with issues of existence and uniqueness in the entire Euclidean space, asymptotic results are provided for the growth durations and it is shown that the packing is space-filling with probability 1, in the sense that the Lebesgue measure of its complement is zero. Finally, the phenomenon is studied that grains arrange in clusters and properties related to percolation are investigated.

Predictions of particle flow and compression breakage of non-round rock passing through an industrial scale cone crusher are presented. The DEM (Discrete Element Method) particle breakage model is generalised to allow non-round particles to be broken into non-round progeny. Particles are broken in this DEM model when the elastic energy of a contact is sufficiently high to initiate fracture. Progeny size distribution data from JKMRC Drop Weight Test (JKDWT) or JKMRC rotary breakage test (JKRBT) is used to generate the specific daughter fragments from each breakage event. This DEM model is able to predict the production of both coarser progeny which are resolved in the DEM model and finer progeny which are not. This allows the prediction of product down to very small sizes, limited only by the fineness of the fragments measured in the breakage characterisation. The predicted flow of material through the crusher, product size distribution and liner wear are discussed. The generalised breakage model demonstrated here is suitable for modelling all forms of crushers.

We explore sets of jammed packings of frictional spheres and ellipsoids generated using the Discrete Element Method. A novel preparation method is presented and analysed, where the internal structure of the system is modified by iteratively removing grains to generate a continuous variation in packing fraction from the densest to the loosest mechanically stable systems. We present a number of measures quantifying the variation in the structure of the system as grains are removed, including the variation in packing fraction, distribution of grain contact numbers, degree of orientational ordering and the degree of displacements of the grains due to rearrangements to achieve mechanical stability after a grain is removed.

It is well recognized that there are gaps in knowledge on the strongly intertwined process–microstructure–property–performance relationships inherent in the metallic additive manufacturing processes. Computational modeling can assist with filling in some of these gaps by increasing in-depth understanding of these relationships and highlighting cause-and-effect. Additionally, it can capture the knowledge of materials scientists and engineers and apply established physics-based rules to simulate the processes and thus predict the final outcomes. Modeling can also help optimize processes. Some even predict that future generations of additive manufacturing machines will employ ‘model-assisted feed forward algorithms’ that would leapfrog feedback control methods. In the current article the authors describe the several computational efforts sponsored by CSIRO’s ‘Lab 22—Australia’s Centre for Additive Innovation’ aimed at modeling-assisted tailored design. The models in development, e.g. microstructure prediction (both fundamental and empirical), powder bed raking, and residual stress predictions, are described in some detail, and representative results are presented.

A set of computationally generated granular packings of frictionless grains is statistically analyzed using tools from stochastic geometry. We consider both the graph of the solid phase (formed using the particle mid-points) and the pore-phase. Structural characteristics rooted in the analysis of random point processes are seen to yield valuable insights into the underlying structure of granular systems. The graph of the solid phase is analyzed using traditional measures such as edge length and coordination number, as well as more instructive measures of the overall transport properties such as geometric tortuosity, where significant differences are observed in the windedness of paths through the different particle graphs considered. In contrast, the distributions of pore-phase characteristics have a similar shape for all considered granular packings. Interestingly, it is found that prolate and oblate ellipsoid packings show a striking similarity between their solid-phase graphs as well as between their pore-phase graphs.

Several approaches to quantitative local structure characterization for particulate assemblies, such as structural glasses or jammed packings, use the partition of space provided by the Voronoi diagram. The conventional construction for spherical mono-disperse particles, by which the Voronoi cell of a particle is that of its centre point, cannot be applied to configurations of aspherical or polydisperse particles. Here, we discuss the construction of a Set Voronoi diagram for configurations of aspherical particles in three-dimensional space. The Set Voronoi cell of a given particle is composed of all points in space that are closer to the surface (as opposed to the centre) of the given particle than to the surface of any other; this definition reduces to the conventional Voronoi diagram for the case of mono-disperse spheres. An algorithm for the computation of the Set Voronoi diagram for convex particles is described, as a special case of a Voronoi-based medial axis algorithm, based on a triangula...

Applying DEM to prediction of tumbling mill performance is challenging because several different modes of breakage are active in the process. As our knowledge of breakage and computational capacity improve, it is worth revisiting previous simulation tasks which did not produce a satisfactorily realistic description of a measured process. At SAG 2006, we reported the simulated and measured outcomes of treating a well characterised ore in a 1.2 m diameter mill. This well instrumented, pilot scale mill at the University of KwaZulu-Natal has been combined with some new approaches to ore testing to allow different modes of breakage to be tested. The mill design allows the rate of generation of fine material to be measured in close to real time for autogenous and SAG mill charges. The updated simulation model estimates mass loss with particle evolution and embeds the cumulative damage and breakage. To more clearly delineate the effects of different types of breakage, surface wear has not been included in these simulations. The simulated results are compared with the measured results. The cumulative damage model is more realistic for SAG operation but is inadequate for AG. Hence, any realistic model must incorporate several breakage mechanisms. Issues such as relating modelling inputs to particle breakage characterisation data and the accuracy of the predictions of the models are discussed.

We investigate the flow of a granular material over a vibrated horizontal screen. We perform a direct quantitative comparison, across a range of operating conditions, between laboratory scale experiments and simulations using the discrete element method (DEM). We test the extent to which the commonly employed DEM approximation of particles being spherical affects the ability of the model to realistically reproduce the behaviour of industrial screening systems where the particles are generally non-spherical in shape. The simulation geometry and input particle size distribution are set up to exactly match the experimental system, which consists of a horizontal screen with a wire mesh cloth onto which quarry rock is fed at a series of input flow rates. The screen is vibrated, causing the granular bed to flow over the deck and vertically stratify with finer material passing through the screen, where it is collected in a series of bins located along the length of the screen. The size distribution of the material flowing through each section of the screen is found by analyzing the contents of each collection bin. The best agreement is found for very low flow rates, where the vast majority of the below aperture size material is rapidly captured just after it enters the screen in both the simulation and experiment. At higher flow rates, significant quantitative errors are found with the over-prediction of the flow rate through the screen for near grate sized particles. This is attributed to the higher rate of percolation through the bed and the easier capture by the screen surface of the spherical shaped material. The near aperture sized spherical particles also show a very strong tendency to peg the screen, becoming trapped in the screen openings and limiting further flow through those parts the screen. The use of spherical particles in the DEM simulation of vibrating screens is therefore found to be inadequate for modelling realistic flow and separation of particles that are not actually spherical.

Monodisperse liquid foams consisting of equal-sized bubbles with diameters of a few hundred microns exhibit crystalline order, with a predominance of the fcc structure. This is inferred from observations at both bottom (foam–water) and top (foam–air) interfaces, together with supporting ray-tracing simulations. Diffusion of gas from the top layer into the atmosphere results in the formation of an ordered bidisperse top layer within a few minutes. In foam samples with a height of about 100 bubbles we have found a transition from mainly fcc packing at the (wet) bottom to the (bcc) Kelvin structure at the (dry) top. The issue of templating for the ordering in wet foams is addressed in soft sphere computer simulations.

In particulate systems with short-range interactions, such as granular matter or simple fluids, local structure plays a pivotal role in determining the macroscopic physical properties. Here, we analyse local structure metrics derived from the Voronoi diagram of configurations of oblate ellipsoids, for various aspect ratios $\alpha$ and global volume fractions $\phi_g$. We focus on jammed static configurations of frictional ellipsoids, obtained by tomographic imaging and by discrete element method simulations. In particular, we consider the local packing fraction $\phi_l$, defined as the particle's volume divided by its Voronoi cell volume. We find that the probability $P(\phi_l)$ for a Voronoi cell to have a given local packing fraction shows the same scaling behaviour as function of $\phi_g$ as observed for random sphere packs. Surprisingly, this scaling behaviour is further found to be independent of the particle aspect ratio. By contrast, the typical Voronoi cell shape, quantified by the Minkowski tensor anisotropy index $\beta=\beta_0^{2,0}$, points towards a significant difference between random packings of spheres and those of oblate ellipsoids. While the average cell shape $\beta$ of all cells with a given value of $\phi_l$ is very similar in dense and loose jammed sphere packings, the structure of dense and loose ellipsoid packings differs substantially such that this does not hold true. This non-universality has implications for our understanding of jamming of aspherical particles., Comment: 6 pages, 5 figures

Microfluidics is a rapidly developing field of innovation, which provides the chemical, medical and (bio)pharmaceutical industries with a much more efficient substitute for the use of well plates, syringes, etc., to perform multiple manipulations and analysis of small samples. While conventional, continuous microfluidics and the manipulation of individual nano-droplets in “Digital Microfluidics” are highly advanced, a major new dimension is added by our work on the flow of ordered foam and emulsion structures in specifically designed channel geometries. We show experimentally and computationally, utilizing both the quasi-static and Viscous Froth models, how the interplay between channel geometry and ordered foam/emulsion structures can be used to process tiny amounts of gases or liquids in a highly reliable and efficient way. We have termed this method “Discrete Microfluidics” and will introduce its various aspects in this paper.

In textbook descriptions of Newton’s cradle, it is generally claimed that displacing one ball will result in a collision that leads to another ball being ejected from the line, with all others remaining motionless. Hermann and Schmalzle, Hinch and Saint-Jean, and others have shown that a realistic description is more subtle. We present a simulation of Newton’s cradle that reproduces the break-up of the line of balls at the first collision, the eventual movement of all the balls in phase, and is in good agreement with our experimentally obtained data. The first effect is due to the finite elastic response of the balls, and the second is a result of viscoelastic dissipation in the impacts. We also analyze a dissipation-free ideal Newton’s cradle which displays complex dynamics.

In particulate soft matter systems the average number of contacts $Z$ of a particle is an important predictor of the mechanical properties of the system. Using X-ray tomography, we analyze packings of frictional, oblate ellipsoids of various aspect ratios $\alpha$, prepared at different global volume fractions $\phi_g$. We find that $Z$ is a monotonously increasing function of $\phi_g$ for all $\alpha$. We demonstrate that this functional dependence can be explained by a local analysis where each particle is described by its local volume fraction $\phi_l$ computed from a Voronoi tessellation. $Z$ can be expressed as an integral over all values of $\phi_l$: $Z(\phi_g, \alpha, X) = \int Z_l (\phi_l, \alpha, X) \; P(\phi_l | \phi_g) \; d\phi_l$. The local contact number function $ Z_l (\phi_l, \alpha, X)$ describes the relevant physics in term of locally defined variables only, including possible higher order terms $X$. The conditional probability $P(\phi_l | \phi_g)$ to find a specific value of $\phi_l$ given a global packing fraction $\phi_g$ is found to be independent of $\alpha$ and $X$. Our results demonstrate that for frictional particles a local approach is not only a theoretical requirement but also feasible., Comment: more revisions, accepted for publication in Physical Review Letters