Contribution à la prévision du méandrement du sillage des éoliennes à axe horizontal

La production éolienne industrielle s’organise sous la forme de centrales regroupant de quelques unités à la centaine d’éoliennes. Ces centrales sont disséminées sur le territoire terrestre ou maritime en des lieux soigneusement sélectionnés sur la base de nombreux critères, dont le potentiel éolien. Cela concerne la qualité des vents incidents, l’influence de la complexité géométrique du site mais aussi les interactions aérodynamiques entre éoliennes qui pourraient dégrader la qualité du vent incident et donc la production énergétique. En fonction de la direction des vents, la proximité des éoliennes organisées en parc induit très fréquemment des interactions de sillages entre deux ou plusieurs machines alignées. Le sillage généré par une éolienne se caractérise principalement par un déficit de vitesse du vent et une augmentation de la turbulence sur plusieurs centaines de mètres en aval. L’interaction du sillage d’une éolienne sur une seconde se traduit par une diminution du rendement ainsi qu’une augmentation des charges aérodynamiques fatiguant prématurément les matériaux. De plus, les grandes échelles de la turbulence atmosphérique ont tendance à modifier la trajectoire des sillages éoliens avec un caractère instationnaire, appelé méandrement (« meandering »). Ce phénomène mal quantifié jusqu’à maintenant doit être caractérisé pour être correctement modélisés dans les calculs de prévision d’effets de sillage. La modélisation physique en soufflerie est l’approche utilisée par le laboratoire PRISME pour étudier cette problématique. Cela consiste à reproduire à échelle réduite (de l’ordre du 1/500ème) les propriétés aérodynamiques (moyennes et turbulentes) de la basse couche atmosphérique. Le rotor est modélisé, sur la base du concept de disque actuateur, par un disque poreux et les propriétés stationnaires et instationnaires du sillage lointain sont étudiées par l’intermédiaire de mesures de vitesse par anémométrie fil chaud multi-points, PIV et LDV. Par le biais de ces expériences en soufflerie, des réponses sont apportées sur les causes du méandrement, son amplitude et sa prévision

Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: application to the multi-scale dynamics of glycine receptors in the neuronal membrane

Protein mobility is conventionally analyzed in terms of an effective diffusion. Yet, this description often fails to properly distinguish and evaluate the physical parameters (such as the membrane friction) and the biochemical interactions governing the motion. Here, we present a method combining high-density single-molecule imaging and statistical inference to separately map the diffusion and energy landscapes of membrane proteins across the cell surface at ~100 nm resolution (with acquisition of a few minutes). When applying these analytical tools to glycine neurotransmitter receptors (GlyRs) at inhibitory synapses, we find that gephyrin scaffolds act as shallow energy traps (~3 kBT) for GlyRs, with a depth modulated by the biochemical properties of the receptor-gephyrin interaction loop. In turn, the inferred maps can be used to simulate the dynamics of proteins in the membrane, from the level of individual receptors to that of the population, and thereby, to model the stochastic fluctuations of physiological parameters (such as the number of receptors at synapses). Overall, our approach provides a powerful and comprehensive framework with which to analyze biochemical interactions in living cells and to decipher the multi-scale dynamics of biomolecules in complex cellular environments.Comment: 23 pages, 4 figure

Compression-based inference of network motif sets

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred as ``building blocks of complex networks''. Their well-defined structures and small sizes also enables the testing of their functions in synthetic and natural biological experiments. The statistical inference of network motifs is however fraught with difficulties, from defining and sampling the right null model to accounting for the large number of possible motifs and their potential correlations in statistical testing. Here we develop a framework for motif mining based on lossless network compression using subgraph contractions. The minimum description length principle allows us to select the most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. The approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. This provides an alternative definition of motif significance which guarantees more robust statistical inference. Our approach overcomes the common problems in classic testing-based motif analysis. We apply our methodology to perform comparative connectomics by evaluating the compressibility and the circuit motifs of a range of synaptic-resolution neural connectomes

QALC - the Question-Answering program of the Language and Cognition group at LIMSI-CNRS

International audienc

Variational inference of fractional Brownian motion with linear computational complexity

We introduce a simulation-based, amortised Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learnt summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cram{\'e}r-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes well to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories

Approximate information maximization for bandit games

Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with the information bottleneck principle (Tishby et al., 2000), and navigation in random environments using information maximization (Vergassola et al., 2007). Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system. To this end, we develop an approximated analytical physics-based representation of an entropy to forecast the information gain of each action and greedily choose the one with the largest information gain. This method yields strong performances in classical bandit settings. Motivated by its empirical success, we prove its asymptotic optimality for the two-armed bandit problem with Gaussian rewards. Owing to its ability to encompass the system's properties in a global physical functional, this approach can be efficiently adapted to more complex bandit settings, calling for further investigation of information maximization approaches for multi-armed bandit problems

A geomorphological investigation of lateral spreading and translational sliding within the Storegga Slide

Lateral spreading and translational sliding are two of the most prevalent types of slope failures within the Storegga Slide. This has been concluded from a thorough analysis of three acoustic data sets from the Storegga Slide complex – high-resolution multibeam bathymetry, TOBI sidescan sonar imagery and 3D seismic data.We have applied quantitative geomorphometric techniques to the bathymetry data set and analysed the texture of sidescan sonar images using Grey-Level Occurrence Matrices (GLCMs). Both techniques have been shown to improve the geological interpretation of submarine environments (e.g. Micallef et al., 2006), and allowed an objective characterisation of the slide surface to be carried out. These results were then combined with the interpretation of the seismic data set and all the geological information currently available for Storegga in the literature. In this way we were able to define the types and boundaries of the different styles of mass movements, and represent them on a geomorphological map. Further insight is provided into the origin and the mode of failure of lateral spreading and translational sliding. Finally we attempt to describe the characteristic morphology of lateral spreading and demonstrate that it is a very common slope failure process in the Norwegian margin.peer-reviewe

Appropriate dynamic-stall models for performance predictions of VAWTs with NLF blades

This paper illustrates the relative merits of using Natural Laminar Flow (NLF) airfoils in the design of Vertical Axis Wind Turbines (VAWT). This is achieved by the application of the double-multiple-streamtube model of Paraschivoiu to the performance predictions of VAWTs equipped with conventional and NLF blades. Furthermore, in order to clearly illustrate the potential benefit of reducing the drag, the individual contributions of lift and drag to power are presented. The dynamic-stall phenomena are modelled using the method of Gormont as modified by several researchers. Among the various implementations of this dynamic-stall model available in the literature, the most appropriate and general for NLF applications has been identified through detailed comparisons between predicted performances and experimental data. This selection process is presented in the paper. It has been demonstrated that the use of NLF airfoils in VAWT applications can lead to significant improvements with respect to conventional design only in a very low wind speed range, the extent of which is negligible with respect to the VAWT operational wind speeds