Simulation des géomatériaux par la méthode des éléments Lagrangiens

Une formule non-traditionnelle de la méthode des différences finies est présentée. Permettant tout type de maillage, elle rend cette méthode aussi souple que celle des éléments finis. Parce qu'elle est explicite et suit donc le comportement des matériaux au cours de leur réponse à une sollicitation, la formulation présentée est particulièrement efficace pour l'étude des sols fortement non- linéaires, avec de grandes déformations et des zones de plastifications importantes

Attentional bias in alcohol drinkers: a systematic review of its link with consumption variables

In severe alcohol use disorder (SAUD), attentional bias refers to the preferential allocation of attentional resources toward alcohol-related cues. Dominant models consider that this bias plays a key role in the emergence and maintenance of SAUD. We evaluate the available experimental support for this assumption through a systematic literature review, providing a critical synthesis of studies exploring the links between alcohol consumption and attentional bias. Using PRISMA guidelines, we explored three databases (PsycINFO, PubMed, Scopus) and extracted 95 papers. We assessed their methodological quality and categorized them based on the population targeted, namely patients with SAUD or subclinical populations with various drinking patterns. We also classified papers according to the measures used (i.e., behavioral or eye-tracking measures). Overall, subclinical populations present an alcohol-related bias, but many studies in SAUD did not find such bias, nor approach/avoidance patterns. Moreover, attentional bias fluctuates alongside motivational states rather than according to alcohol use severity, which questions its stability. We provide recommendations to develop further theoretical knowledge and overcome methodological shortcomings

A spatial clustering approach for stochastic fracture network modelling

Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach followed by a real 3D case study of the Habanero reservoir in the Cooper Basin, Australia. © 2013 Springer-Verlag Wien.S. Seifollahi, P. A. Dowd, C. Xu, A. Y. Fadaka