Direct simulation of interface dynamics: linking capillary pressure, interfacial area and surface energy

We perform direct numerical simulations of drainage by solving Navier- Stokes equations in the pore space and employing the Volume Of Fluid (VOF) method to track the evolution of the fluid-fluid interface. After demonstrating that the method is able to deal with large viscosity contrasts and to model the transition from stable flow to viscous fingering, we focus on the definition of macroscopic capillary pressure. When the fluids are at rest, the difference between inlet and outlet pressures and the difference between the intrinsic phase average pressure coincide with the capillary pressure. However, when the fluids are in motion these quantities are dominated by viscous forces. In this case, only a definition based on the variation of the interfacial energy provides an accurate measure of the macroscopic capillary pressure and allows separating the viscous from the capillary pressure components.

Neutron structure of type-III antifreeze protein allows the reconstruction of AFP-ice interface.

Antifreeze proteins (AFPs) inhibit ice growth at sub-zero temperatures. The prototypical type-III AFPs have been extensively studied, notably by X-ray crystallography, solid-state and solution NMR, and mutagenesis, leading to the identification of a compound ice-binding surface (IBS) composed of two adjacent ice-binding sections, each which binds to particular lattice planes of ice crystals, poisoning their growth. This surface, including many hydrophobic and some hydrophilic residues, has been extensively used to model the interaction of AFP with ice. Experimentally observed water molecules facing the IBS have been used in an attempt to validate these models. However, these trials have been hindered by the limited capability of X-ray crystallography to reliably identify all water molecules of the hydration layer. Due to the strong diffraction signal from both the oxygen and deuterium atoms, neutron diffraction provides a more effective way to determine the water molecule positions (as D(2) O). Here we report the successful structure determination at 293 K of fully perdeuterated type-III AFP by joint X-ray and neutron diffraction providing a very detailed description of the protein and its solvent structure. X-ray data were collected to a resolution of 1.05 Å, and neutron Laue data to a resolution of 1.85 Å with a "radically small" crystal volume of 0.13 mm(3). The identification of a tetrahedral water cluster in nuclear scattering density maps has allowed the reconstruction of the IBS-bound ice crystal primary prismatic face. Analysis of the interactions between the IBS and the bound ice crystal primary prismatic face indicates the role of the hydrophobic residues, which are found to bind inside the holes of the ice surface, thus explaining the specificity of AFPs for ice versus water.

Simulation of interface dynamics across pore discontinuities

We study the dynamics of a water-oil meniscus moving from a smaller to a larger pore. The process is characterised by an abrupt change in the configuration, yielding a sudden energy release. A theoretic study for static conditions provides analytical solutions of the surface energy content of the system. Although the configuration after the sudden energy release is energetically more convenient, an energy barrier must be overcome before the process can happen spontaneously. The energy barrier depends on the system geometry and on the flow parameters. The analytical results are compared to numerical simulations that solve the full Navier-Stokes equation in the pore space and employ the Volume Of Fluid (VOF) method to track the evolution of the interface. First, the numerical simulations of a quasi-static process are validated by comparison with the analytical solutions for a static meniscus, then numerical simulations with varying injection velocity are used to investigate dynamic effects on the configuration change. During the sudden energy jump the system exhibits an oscillatory behaviour. Extension to more complex geometries might elucidate the mechanisms leading to a dynamic capillary pressure and to bifurcations in final distributions of fluid phases in porous

Direct numerical simulations of interface dynamics to link capillary pressure and total surface energy

The flow of two immiscible fluids through a porous medium depends on the complex interplay between gravity, capillarity, and viscous forces. The interaction between these forces and the geometry of the medium gives rise to a variety of complex flow regimes that are difficult to describe using continuum models. Although a number of pore-scale models have been employed, a careful investigation of the macroscopic effects of pore-scale processes requires methods based on conservation principles in order to reduce the number of modeling assumptions. In this work we perform direct numerical simulations of drainage by solving Navier-Stokes equations in the pore space and employing the Volume Of Fluid (VOF) method to track the evolution of the fluid-fluid interface. After demonstrating that the method is able to deal with large viscosity contrasts and model the transition from stable flow to viscous fingering, we focus on the macroscopic capillary pressure and we compare different definitions of this quantity under quasi-static and dynamic conditions. We show that the difference between the intrinsic phase-average pressures, which is commonly used as definition of Darcy-scale capillary pressure, is subject to several limitations and it is not accurate in presence of viscous effects or trapping. In contrast, a definition based on the variation of the total surface energy provides an accurate estimate of the macroscopic capillary pressure. This definition, which links the capillary pressure to its physical origin, allows a better separation of viscous effects and does not depend on the presence of trapped fluid clusters.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Développement de l'interprétation quantitative de la sismique réflexion lacustre : application de l'AVO aux sédiments quaternaires dans le lac Léman

Résumé Des développements antérieurs, au sein de l'Institut de Géophysique de Lausanne, ont permis de développer des techniques d'acquisition sismique et de réaliser l'interprétation des données sismique 2D et 3D pour étudier la géologie de la région et notamment les différentes séquences sédimentaires du Lac Léman. Pour permettre un interprétation quantitative de la sismique en déterminant des paramètres physiques des sédiments la méthode AVO (Amplitude Versus Offset) a été appliquée. Deux campagnes sismiques lacustres, 2D et 3D, ont été acquises afin de tester la méthode AVO dans le Grand Lac sur les deltas des rivières. La géométrie d'acquisition a été repensée afin de pouvoir enregistrer les données à grands déports. Les flûtes sismiques, mises bout à bout, ont permis d'atteindre des angles d'incidence d'environ 40˚ . Des récepteurs GPS spécialement développés à cet effet, et disposés le long de la flûte, ont permis, après post-traitement des données, de déterminer la position de la flûte avec précision (± 0.5 m). L'étalonnage de nos hydrophones, réalisé dans une chambre anéchoïque, a permis de connaître leur réponse en amplitude en fonction de la fréquence. Une variation maximale de 10 dB a été mis en évidence entre les capteurs des flûtes et le signal de référence. Un traitement sismique dont l'amplitude a été conservée a été appliqué sur les données du lac. L'utilisation de l'algorithme en surface en consistante a permis de corriger les variations d'amplitude des tirs du canon à air. Les sections interceptes et gradients obtenues sur les deltas de l'Aubonne et de la Dranse ont permis de produire des cross-plots. Cette représentation permet de classer les anomalies d'amplitude en fonction du type de sédiments et de leur contenu potentiel en gaz. L'un des attributs qui peut être extrait des données 3D, est l'amplitude de la réflectivité d'une interface sismique. Ceci ajoute une composante quantitative à l'interprétation géologique d'une interface. Le fond d'eau sur le delta de l'Aubonne présente des anomalies en amplitude qui caractérisent les chenaux. L'inversion de l'équation de Zoeppritz par l'algorithme de Levenberg-Marquardt a été programmée afin d'extraire les paramètres physiques des sédiments sur ce delta. Une étude statistique des résultats de l'inversion permet de simuler la variation de l'amplitude en fonction du déport. On a obtenu un modèle dont la première couche est l'eau et dont la seconde est une couche pour laquelle V P = 1461 m∕s, ρ = 1182 kg∕m3 et V S = 383 m∕s. Abstract A system to record very high resolution (VHR) seismic data on lakes in 2D and 3D was developed at the Institute of Geophysics, University of Lausanne. Several seismic surveys carried out on Lake Geneva helped us to better understand the geology of the area and to identify sedimentary sequences. However, more sophisticated analysis of the data such as the AVO (Amplitude Versus Offset) method provides means of deciphering the detailed structure of the complex Quaternary sedimentary fill of the Lake Geneva trough. To study the physical parameters we applied the AVO method at some selected places of sediments. These areas are the Aubonne and Dranse River deltas where the configurations of the strata are relatively smooth and the discontinuities between them easy to pick. A specific layout was developed to acquire large incidence angle. 2D and 3D seismic data were acquired with streamers, deployed end to end, providing incidence angle up to 40˚ . One or more GPS antennas attached to the streamer enabled us to calculate individual hydrophone positions with an accuracy of 50 cm after post-processing of the navigation data. To ensure that our system provides correct amplitude information, our streamer sensors were calibrated in an anechoic chamber using a loudspeaker as a source. Amplitude variations between the each hydrophone were of the order of 10 dB. An amplitude correction for each hydrophone was computed and applied before processing. Amplitude preserving processing was then carried out. Intercept vs. gradient cross-plots enable us to determine that both geological discontinuities (lacustrine sediments/moraine and moraine/molasse) have well defined trends. A 3D volume collected on the Aubonne river delta was processed in order ro obtain AVO attributes. Quantitative interpretation using amplitude maps were produced and amplitude maps revealed high reflectivity in channels. Inversion of the water bottom of the Zoeppritz equation using the Levenberg-Marquadt algorithm was carried out to estimate V P , V S and ρ of sediments immediately under the lake bottom. Real-data inversion gave, under the water layer, a mud layer with V P = 1461 m∕s, ρ = 1182 kg∕m3 et V S = 383 m∕s.

Diving capacity and foraging behaviour of the water shrew (Neomys fodiens)

With regard to semi-aquatic mammals, Schröpfer & Stubbe (1992) distinguished three riparian guilds: the herbivores with the water vole and the beaver; the megacarnivores with the mink and the otter; and the macrocarnivores with water shrews and desmans. Among water shrews, the evolution of aquatic foraging behaviour occurred several times: Nectogale and Chimarrogale in Asia, several species of the genus Sorex in America, and Neomys in Eurasia (Churchfield, 1990). The fairly common European water shrew N. fodiens is the best known. However, the reports on the degree of adaptation to the water habitat are conflicting. Therefore some important findings from the literature are reviewed in this introduction, whereas new data are presented in the following sections. The swimming locomotion of water shrews was analysed by Ruthardt & Schröpfer (1985) and Köhler (1991), and the related morphological adaptation were reviewed by Hutterer (1985) and Churchfield (this volume pp. 49-51). They obviously present a compromise between the requirements for activity on land and in the water. Thermoregulation is a major problem for semi-aquatic mammals, because heat conductance in water is 25-fold greater than in air (Calder, 1969). According to this author, the body temperature of immersed American Sorex palustris dropped by a rate of 2.8 °C per min. However, this may be an experimental artefact, because Neomys fodiens can maintain its body temperature at 37 °C during an immersion of 6 min (Vogel, 1990).

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-