Averaged Reynolds Equation for Flows between Rough Surfaces in Sliding Motion

The flow between rough surfaces in sliding motion with contacts between these surfaces, is analyzed through the volume averaging method. Assuming a Reynolds (lubrication) approximation at the roughness scale, an average flow model is obtained combining spatial and time average. Time average, which is often omitted in previous works, is specially discussed. It is shown that the effective transport coefficients, traditionally termed ‘flow factors’ in the lubrication literature, that appear in the average equations can be obtained from the solution to two closure problems. This allows for the numerical determination of flow factors on firmer bases and sheds light on some arguments to the literature. Moreover, fluid flows through fractures form an important subset of problems embodied in the present analysis, for which macroscopisation is given

Quantum boomeranglike effect of wave packets in random media

We unveil an original manifestation of Anderson localization for wave packets launched with a finite average velocity: after an initial ballistic motion, the center of mass of the wave packet experiences a retroreflection and slowly returns to its initial position, an effect that we dub "Quantum Boomerang" and describe numerically and analytically in dimension 1. In dimension 3, we show numerically that the quantum boomerang is a genuine signature of Anderson localization: it exists if and only if the quantum dynamics if localized.Comment: Published versio

Sliding lubricated anisotropic rough surfaces

The object of this paper is to study the effects of lubricant film flow, pressurized and sheared between two parallel rough surfaces in sliding motion. The influence of microscopic surface roughness on lubricant film flow macroscopic behavior is described through five nondimensional parameters called flow factors. These macroscopic transport parameters are related to the local geometry of apertures and surfaces. Short- and long-range-correlated surface roughnesses display very different macroscopic behaviors when surfaces are close to contact. These behaviors are related to underlying surface roughness parameters such as the correlation length and the self-affine Hurst exponent. The problem is numerically studied, and results are compared to some analytical asymptotic results

Using Machine Learning Techniques to Improve Information Systems Development Methods

International audienc

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

Microfluidic synthesis and assembly of reactive polymer beads to form new structured polymer materials

Monodisperse and size-controlled polymer particles were produced without surfactant or wash-coat from O/W monomer emulsions and ‘‘on the fly’’ polymerization under UV irradiation in a very simple needle/tubing system. The effect of the viscosity of the continuous phase on the size of final particles was investigated. The capillary number ratio was found to be relevant to predict the size of the droplets. A relation between dimensionless numbers predicts particle diameter as a function of the needle inner diameter and both velocity and viscosity ratios of continuous and dispersed phases. A functional comonomer was incorporated in the monomer phase so as to obtain polymer microparticles bearing reactive groups on their surface. Polymer beads necklaces were thus formed by linking polymer particles together

Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage

Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where mashups facilitate the combination of data from different sources. Our approach for assessing data believability is based on provenance and lineage, i.e. the origin and subsequent processing history of data. We present the main concepts of our model for representing and storing data provenance, and an ontology of the sub-dimensions of data believability. We then use aggregation operators to compute believability across the sub-dimensions of data believability and the provenance of data. We illustrate our approach with a scenario based on Internet data. Our contribution lies in three main design artifacts (1) the provenance model (2) the ontology of believability subdimensions and (3) the method for computing and aggregating data believability. To our knowledge, this is the first work to operationalize provenance-based assessment of data believability