Revisiting the role of friction coefficients in granular collapses: confrontation of 3-D non-smooth simulations with experiments

In this paper, transient granular flows are examined both numerically and experimentally. Simulations are performed using the continuous 3D granular model introduced in Daviet & Bertails-Descoubes (2016), which represents the granular medium as an inelastic and dilatable continuum subject to the Drucker-Prager yield criterion in the dense regime. One notable feature of this numerical model is to resolve such a non-smooth rheology without any regularisation. We show that this non-smooth model, which relies on a constant friction coefficient, is able to reproduce with high fidelity various experimental granular collapses over inclined erodible beds, provided the friction coefficient is set to the avalanche angle - and not to the stop angle, as generally done. In order to better characterise the range of validity of the fully plastic rheology in the context of transient frictional flows, we further revisit scaling laws relating the shape of the final collapse deposit to the initial column aspect ratio, and accurately recover established power-law dependences up to aspect ratios in the order of 10. The influence of sidewall friction is then examined through experimental and simulated collapses with varying channel widths. The effective flow thickness is estimated in relation to the channel width, thereby challenging previously held assumptions on its estimation. Finally, we discuss the potential extension of the constant coefficient model with a hysteretic model to refine predictions of early-stage collapse dynamics, illustrating the impact of such phenomenology on transient flows and paving the way to more elaborate analysis.Comment: 25 figures and 6 movie

Spectral-phase interferometry for direct electric-field reconstruction applied to seeded extreme-ultraviolet free-electron lasers

We present a setup for complete characterization of femtosecond pulses generated by seeded free-electron lasers (FEL's) in the extreme-ultraviolet spectral region. Two delayed and spectrally shifted replicas are produced and used for spectral phase interferometry for direct electric field reconstruction (SPIDER). We show that it can be achieved by a simple arrangement of the seed laser. Temporal shape and phase obtained in FEL simulations are well retrieved by the SPIDER reconstruction, allowing to foresee the implementation of this diagnostic on existing and future sources. This will be a significant step towards an experimental investigation and control of FEL spectral phase

Bridging the gap between particle-scale forces and continuum modelling of size segregation: application to bedload transport

Gravity-driven size segregation is important in mountain streams where a wide range of grain sizes are transported as bedload. More particularly, vertical size segregation is a multi-scale process that originates in interactions at the scale of particles with important morphological consequences on the reach scale. To address this issue, a volume-averaged multi-phase flow model for immersed bidisperse granular flows was developed based on an interparticle segregation force (Guillard et al. 2016) and a granular Stokesian drag force (Tripathi and Khakhar 2013). An advection-diffusion model was derived from this model yielding parametrisations for the advection and diffusion coefficients based on the interparticle interactions. This approach makes it possible to bridge the gap between grain-scale physics and continuum modelling. Both models were successfully tested against existing Discrete Element Model (DEM) simulations of size segregation in bedload transport (Chassagne et al. 2020). Through a detailed investigation of the granular forces, it is demonstrated that the observed scaling of the advection and diffusion coefficients with the inertial number can be explained by the granular drag force dependency on the viscosity. The drag coefficient was shown to be linearly dependent on the small particle concentration. The scaling relationship of the segregation force with the friction coefficient is confirmed and additional non-trivial dependencies including the inertial number and small particle concentration are identified. Lastly, adding a size ratio dependency in the segregation force perfectly reproduces the DEM results for a large range of small particle concentrations and size-ratios

Functionally distinct resident macrophage subsets differentially shape responses to infection in the bladder

International audienceResident macrophages are abundant in the bladder, playing key roles in immunity to uropathogens. Yet, whether they are heterogeneous, where they come from, and how they respond to infection remain largely unknown. We identified two macrophage subsets in mouse bladders, MacM in muscle and MacL in the lamina propria, each with distinct protein expression and transcriptomes. Using a urinary tract infection model, we validated our transcriptomic analyses, finding that MacM macrophages phagocytosed more bacteria and polarized to an anti-inflammatory profile, whereas MacL macrophages died rapidly during infection. During resolution, monocyte-derived cells contributed to tissue-resident macrophage pools and both subsets acquired transcriptional profiles distinct from naïve macrophages. Macrophage depletion resulted in the induction of a type 1-biased immune response to a second urinary tract infection, improving bacterial clearance. Our study uncovers the biology of resident macrophages and their responses to an exceedingly common infection in a largely overlooked organ, the bladder

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio