How historical information can improve estimation and prediction of extreme coastal water levels: application to the Xynthia event at La Rochelle (France)

International audienceThe knowledge of extreme coastal water levels is useful for coastal flooding studies or the design of coastal defences. While deriving such extremes with standard analyses using tide-gauge measurements, one often needs to deal with limited effective duration of observation which can result in large statistical uncertainties. This is even truer when one faces the issue of outliers, those particularly extreme values distant from the others which increase the uncertainty on the results. In this study, we investigate how historical information , even partial, of past events reported in archives can reduce statistical uncertainties and relativise such outlying observations. A Bayesian Markov chain Monte Carlo method is developed to tackle this issue. We apply this method to the site of La Rochelle (France), where the storm Xynthia in 2010 generated a water level considered so far as an outlier. Based on 30 years of tide-gauge measurements and 8 historical events, the analysis shows that (1) integrating historical information in the analysis greatly reduces statistical uncertainties on return levels (2) Xynthia's water level no longer appears as an outlier, (3) we could have reasonably predicted the annual exceedance probability of that level beforehand (predictive probability for 2010 based on data until the end of 2009 of the same order of magnitude as the standard es-timative probability using data until the end of 2010). Such results illustrate the usefulness of historical information in extreme value analyses of coastal water levels, as well as the relevance of the proposed method to integrate heterogeneous data in such analyses

Using Surface Evolver to measure pressures and energies of real 2D foams submitted to quasi-static deformations

International audienceStatic 2D foams have the interesting property that their energy is measurable by summing up the length of their films, so that a simple optical picture of a 2D foam should enable measurement of its energy and other quantities such as its bubbles’ pressures. This operation is of course unrealizable in most experiments since the optical resolution limits the accuracy of length measurements. Here we show that, using image analysis tools alongside an iterative procedure based on the Surface Evolver (Brakke, 1992) to analyze optical images of a 2D foam, we are able to measure accurately its energy and its bubbles’ pressures up to a single multiplying factor. We determine this factor, and validate this procedure, by comparing experimental measurements of the pressure and the work done on a 2D foam experiencing a quasi-static localized deformation with the energy and pressures computed using our procedure

Experimental growth law for bubbles in a "wet" 3D liquid foam

We used X-ray tomography to characterize the geometry of all bubbles in a liquid foam of average liquid fraction $\phi_l\approx 17$ and to follow their evolution, measuring the normalized growth rate $\mathcal{G}=V^{-{1/3}}\frac{dV} {dt}$ for 7000 bubbles. While $\mathcal{G}$ does not depend only on the number of faces of a bubble, its average over $f-$faced bubbles scales as $G_f\sim f-f_0$ for large $f$s at all times. We discuss the dispersion of $\mathcal{G}$ and the influence of $V$ on $\mathcal{G}$.Comment: 10 pages, submitted to PR

Quantitative 3D Characterization of Cellular Materials: Segmentation and Morphology of Foam

International audienceWood, trabecular bone, coral, liquid foams, grains in polycrystals, igneous rock, and even many types of food share many structural similarities and belong to the general class called cellular materials. The visualization of these materials in 3D has been made possible in the last decades through a variety of imaging techniques including magnetic resonance imaging (MRI), micro-computed X-ray tomography ($\mu$CT), and confocal microscopy. Recent advances in synchrotron-based ultra fast tomography have enabled measurements in liquid foams with thousands of bubbles and time resolutions down to 0.5 seconds. Post-processing techniques have, however, not kept pace and extracting useful physical metrics from such measurements is far from trivial. In this manuscript we present and validate a new, fully-automated method for segmenting and labeling the void space in cellular materials where the walls between cells are not visible or present. The individual cell labeling is based on a new tool, the Gradient Guided Watershed, which, while computationally simple, can be robustly scaled to large data-sets. Specifically we demonstrate the utility of this new method on several liquid foams (with varying liquid fraction and polydispersity) composed of thousands of bubbles, and the subsequent quantitative 3D structural characterization of those foams

Deploying the ATLAS Metadata Interface (AMI) stack in a Docker Compose or Kubernetes environment

ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. This paper describes how a renewed architecture and integration with modern technologies ease the usage and deployment of a complete AMI stack. It describes how to deploy AMI in a Docker Compose or Kubernetes environment, with a particular emphasis on the registration of existing databases, the addition of more metadata sources, and the generation of high level Web search interfaces using dedicated wizards

Using MQTT and Node-RED to monitor the ATLAS Meta-data Interface (AMI) stack and define metadata aggregation tasks in a pipelined way

ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. Each sub-system of the stack has recently been improved in order to acquire messaging/telemetry capabilities. This paper describes the whole stack monitoring with the Message Queuing Telemetry Transport (MQTT) protocol and Node-RED, a tool for wiring together hardware/software devices. Finally, this paper shows how Node-RED is used to graphically define metadata aggregation tasks, in a pipelined way, without introducing any single point of failure