Relation between dry granular flow regimes and morphology of deposits: formation of levees in pyroclastic deposits

Experiments on dry granular matter flowing down an inclined plane are performed in order to study the dynamics of dense pyroclastic flows. The plane is rough, and always wider than the flow, focusing this study on the case of laterally unconfined (free boundary) flows.We found that several flow regimes exist depending on the input fluxand on the inclination of the plane. Each flow regime corresponds to a particular morphology of the associated deposit. In one of these regimes, the flow reaches a steady state, and the deposit exhibits a levee/channel morphology similar to those observed on small pyroclastic flow deposits. The levees result from the combination between lateral static zones on each border of the flow and the drainage of the central part of the flow after the supply stops. Particle segregation featuresare created during the flow, corresponding to those observed on the deposits of pyroclastic flows. Moreover, the measurements of the deposit morphology (thickness of the channel, height of the levees, width of the deposit) are quantitatively related to parameters of the dynamics of the flow (flux, velocity, height of the flow), leading to a way of studying the flow dynamics from only measurements of the deposit. Some attempts to make extensions to natural cases are discussed through experiments introducing the polydispersity of the particle sizes and the particle segregation process

Evidence of reverse and intermediate size segregation in dry granular flows down a rough incline

In a dry granular flow, size segregation behave differently for a mixture containing a few large beads with a size ratio (S) above 5 (Thomas, Phys.Rev.E 62,96(2000)). For moderate large S, large beads migrate to an intermediate depth in the bed: this is called intermediate segregation. For the largest S, large beads migrate to the bottom: this is called reverse segregation (in contrast with surface segregation). As the reversal and intermediate depth values depend on the bead fraction, this numerical study mainly uses a single large tracer. Small fractions are also computed showing the link between a tracer behavior and segregation process. For half-filled rotating drum and for rough incline, two and three (3D) dimensional cases are studied. In the tumbler, trajectories of a large tracer show that it reaches a constant depth during the flow. For large S, this depth is intermediate with a progressive sinking when S increases. Largest S correspond to tracers at the bottom of the flow. All 3D simulation are in quantitative agreement with the experiments. In the flow down an incline, a large tracer reaches an equilibrium depth during flow. For large S, its depth is intermediate, inside the bed. For the largest S, its depth is reverse, near the bottom. Results are slightly different for thin or thick flow. For 3D thick flows, the reversal between surface and bottom positions occurs within a short range of S: no tracer stabilizes near mid-height and two reachable intermediate depth layers exist, below the surface and above the bottom. For 3D thin flows, all intermediate depths are reachable, depending on S. The numerical study of larger tracer fractions (5-10%) shows the 3 segregation patterns (surface, intermediate, reverse) corresponding to the 3 types of equilibrium depth. The reversal is smoother than for a single tracer. It happens around S=4.5, in agreement with experiments.Comment: 18 pages, 27 figure

When are Stochastic Transition Systems Tameable?

A decade ago, Abdulla, Ben Henda and Mayr introduced the elegant concept of decisiveness for denumerable Markov chains [1]. Roughly speaking, decisiveness allows one to lift most good properties from finite Markov chains to denumerable ones, and therefore to adapt existing verification algorithms to infinite-state models. Decisive Markov chains however do not encompass stochastic real-time systems, and general stochastic transition systems (STSs for short) are needed. In this article, we provide a framework to perform both the qualitative and the quantitative analysis of STSs. First, we define various notions of decisiveness (inherited from [1]), notions of fairness and of attractors for STSs, and make explicit the relationships between them. Then, we define a notion of abstraction, together with natural concepts of soundness and completeness, and we give general transfer properties, which will be central to several verification algorithms on STSs. We further design a generic construction which will be useful for the analysis of {\omega}-regular properties, when a finite attractor exists, either in the system (if it is denumerable), or in a sound denumerable abstraction of the system. We next provide algorithms for qualitative model-checking, and generic approximation procedures for quantitative model-checking. Finally, we instantiate our framework with stochastic timed automata (STA), generalized semi-Markov processes (GSMPs) and stochastic time Petri nets (STPNs), three models combining dense-time and probabilities. This allows us to derive decidability and approximability results for the verification of these models. Some of these results were known from the literature, but our generic approach permits to view them in a unified framework, and to obtain them with less effort. We also derive interesting new approximability results for STA, GSMPs and STPNs.Comment: 77 page

Influence of Rough and Smooth Walls on Macroscale Flows in Tumblers

Walls in discrete element method simulations of granular flows are sometimes modeled as a closely packed monolayer of fixed particles, resulting in a rough wall rather than a geometrically smooth wall. An implicit assumption is that the resulting rough wall differs from a smooth wall only locally at the particle scale. Here we test this assumption by considering the impact of the wall roughness at the periphery of the flowing layer on the flow of monodisperse particles in a rotating spherical tumbler. We find that varying the wall roughness significantly alters average particle trajectories even far from the wall. Rough walls induce greater poleward axial drift of particles near the flowing layer surface, but decrease the curvature of the trajectories. Increasing the volume fill level in the tumbler has little effect on the axial drift for rough walls, but increases the drift while reducing curvature of the particle trajectories for smooth walls. The mechanism for these effects is related to the degree of local slip at the bounding wall, which alters the flowing layer thickness near the walls, affecting the particle trajectories even far from the walls near the equator of the tumbler. Thus, the proper choice of wall conditions is important in the accurate simulation of granular flows, even far from the bounding wall.Comment: 32 pages, 19 figures, regular article, accepted for publication in Physical Review E 200

Agronomists and accounting. The beginnings of capitalist rationalisation on the farm (1800-1850)

At the dawn of the nineteenth century numerous debates took place on the development of capita-list agriculture and the ways of making as much profit as possible from farm land. Until now this subject has hardly been examined and is unique in that it pertains to the economic history of agriculture, the history of agronomy and the history of managerial thinking. This article aims to highlight the usage of double-entry accounting for agronomic experiments in the first half of the nineteenth century, as well as the significance of the results and the way these were debated. Our aim is to present the authors’ reasons and the role played by bookkeeping in the construction of economically rational knowledge and reasoning. Thus we will bring to light two mechanisms which are common to this accounting quantification drive: data tabulation and the inclusion of data in balance sheets, making it possible to compare inputs and outputs in production processes

A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions