Beating the teapot effect

We investigate the dripping of liquids around solid surfaces in the regime of inertial flows, a situation commonly encountered with the so-called "teapot effect". We demonstrate that surface wettability is an unexpected key factor in controlling flow separation and dripping, the latter being completely suppressed in the limit of superhydrophobic substrates. This unforeseen coupling is rationalized in terms of a novel hydro-capillary adhesion framework, which couples inertial flows to surface wettability effects. This description of flow separation successfully captures the observed dependence on the various experimental parameters - wettability, flow velocity, solid surface edge curvature-. As a further illustration of this coupling, a real-time control of dripping is demonstrated using electro-wetting for contact angle actuation.Comment: 4 pages; movies at http://lpmcn.univ-lyon1.fr/~lbocque

Space-time estimation of a particle system model

13 pagesLet X be a discrete time contact process (CP) on the discrete bidimensional lattice as define by Durett - Levin (1994) . We study estimation of the model based on space-time evolution on a finite subset of sites. For this, we make use of a marginal pseudo-likelihood. The estimator obtained is consistent and asymptoticaly normal for non-vanishing supercritical CP. Numerical studies confirm these results

Efficient simulation of non-crossing fibers and chains in a hydrodynamic solvent

An efficient simulation method is presented for Brownian fiber suspensions, which includes both uncrossability of the fibers and hydrodynamic interactions between the fibers mediated by a mesoscopic solvent. To conserve hydrodynamics, collisions between the fibers are treated such that momentum and energy are conserved locally. The choice of simulation parameters is rationalised on the basis of dimensionless numbers expressing the relative strength of different physical processes. The method is applied to suspensions of semiflexible fibers with a contour length equal to the persistence length, and a mesh size to contour length ratio ranging from 0.055 to 0.32. For such fibers the effects of hydrodynamic interactions are observable, but relatively small. The non-crossing constraint, on the other hand, is very important and leads to hindered displacements of the fibers, with an effective tube diameter in agreement with recent theoretical predictions. The simulation technique opens the way to study the effect of viscous effects and hydrodynamic interactions in microrheology experiments where the response of an actively driven probe bead in a fiber suspension is measured.Comment: 12 pages, 2 tables, 5 figure

Zenithal bistability in a nematic liquid crystal device with a monostable surface condition

The ground-state director configurations in a grating-aligned, zenithally bistable nematic device are calculated in two dimensions using a Q tensor approach. The director profiles generated are well described by a one-dimensional variation of the director across the width of the device, with the distorted region near the grating replaced by an effective surface anchoring energy. This work shows that device bistability can in fact be achieved by using a monostable surface term in the one-dimensional model. This implies that is should be possible to construct a device showing zenithal bistability without the need for a micropatterned surface

Personalized Pancreatic Tumor Growth Prediction via Group Learning

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method

Adaptive optics in high-contrast imaging

The development of adaptive optics (AO) played a major role in modern astronomy over the last three decades. By compensating for the atmospheric turbulence, these systems enable to reach the diffraction limit on large telescopes. In this review, we will focus on high contrast applications of adaptive optics, namely, imaging the close vicinity of bright stellar objects and revealing regions otherwise hidden within the turbulent halo of the atmosphere to look for objects with a contrast ratio lower than 10^-4 with respect to the central star. Such high-contrast AO-corrected observations have led to fundamental results in our current understanding of planetary formation and evolution as well as stellar evolution. AO systems equipped three generations of instruments, from the first pioneering experiments in the nineties, to the first wave of instruments on 8m-class telescopes in the years 2000, and finally to the extreme AO systems that have recently started operations. Along with high-contrast techniques, AO enables to reveal the circumstellar environment: massive protoplanetary disks featuring spiral arms, gaps or other asymmetries hinting at on-going planet formation, young giant planets shining in thermal emission, or tenuous debris disks and micron-sized dust leftover from collisions in massive asteroid-belt analogs. After introducing the science case and technical requirements, we will review the architecture of standard and extreme AO systems, before presenting a few selected science highlights obtained with recent AO instruments.Comment: 24 pages, 14 figure

Influence of flow confinement on the drag force on a static cylinder

The influence of confinement on the drag force $F$ on a static cylinder in a viscous flow inside a rectangular slit of aperture $h_0$ has been investigated from experimental measurements and numerical simulations. At low enough Reynolds numbers, $F$ varies linearly with the mean velocity and the viscosity, allowing for the precise determination of drag coefficients $\lambda_{||}$ and $\lambda_{\bot}$ corresponding respectively to a mean flow parallel and perpendicular to the cylinder length $L$. In the parallel configuration, the variation of $\lambda_{||}$ with the normalized diameter $\beta = d/h_0$ of the cylinder is close to that for a 2D flow invariant in the direction of the cylinder axis and does not diverge when $\beta = 1$. The variation of $\lambda_{||}$ with the distance from the midplane of the model reflects the parabolic Poiseuille profile between the plates for $\beta \ll 1$ while it remains almost constant for $\beta \sim 1$. In the perpendicular configuration, the value of $\lambda_{\bot}$ is close to that corresponding to a 2D system only if $\beta \ll 1$ and/or if the clearance between the ends of the cylinder and the side walls is very small: in that latter case, $\lambda_{\bot}$ diverges as $\beta \to 1$ due to the blockage of the flow. In other cases, the side flow between the ends of the cylinder and the side walls plays an important part to reduce $\lambda_{\bot}$: a full 3D description of the flow is needed to account for these effects