Effect of a base cavity on the wake of the squareback Ahmed body at various ground clearances and application to drag reduction

Recently, Evrard et al. (2016, Journal of Fluids and Structures, 61) achieved drag reduction by almost 9 % by means of a base cavity on a three-dimensional bluff body, the squareback Ahmed body. The authors associate drag reduction with the suppression of the static asymmetric modes of the wake identified by Grandemange et al. (2013, Journal of Fluid Mechanics, 722) leading to its symmetrization. The beneficial effect of a base cavity on the drag has been known for decades on axisymmetric bluff bodies in the context of aerospace engineering (Morel, 1979, Aeronautical Quarterly, 30) but the phenomenon has not been fully elucidated yet. The present work aims at showing experimentally that the decrease of the asymmetry of the near wake flow is responsible for drag reduction regardless of the ground clearance. With this aim in mind, we do two parametric studies of the ground clearance of a squareback Ahmed body in an industrial wind-tunnel; one without and one with a base cavity. We want to compare the two bifurcations of the wake operated by the ground clearance (Grandemange et al., 2013, Physics of Fluids, 25) depending on the rear geometry. Far enough from the ground i.e. after the bifurcation, the modes indeed disappear in agreement with the work of Evrard et al. the flow is symmetrized and the base pressure increases by 24 %. In the vicinity of the ground however, two new results are reported in this paper. The modes governing the wake are not the same as at higher ground clearances but are rather vertical modes due to the presence of the ground. Besides, even if the cavity does not fully symmetrize the flow in this case, its asymmetry is reduced. Consequently, an important drag reduction of the same order of magnitude as in the far ground regime is observed (7 % associated with a base pressure increase by 20 %). A six-components aerodynamic balance and twenty-one instantaneous base pressure measurements are used to record the data at a high sampling rate to study the dynamics of the changes

Extension de l'aire de distribution du mangabey agile (Cercocebus agilis) et du mandrill (Mandrillus sphinx) dans l'est du Gabon, documentée grâce aux pièges photographiques.

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Boltzmann Machines for signals decomposition. Application to Parkinson's Disease control

International audienceThis article presents a new method of decomposition of signals with an unsupervised training model: the continuous RestrictedBoltzmann Machine (cRBM) based on the structure of the Diffusion Network. An application for the detection of High-Voltage Spindle (HVS)in signals recorded in the brain is also presented.Cet article présente une nouvelle méthode de décomposition des signaux à l'aide d'un modèle d'apprentissage non supervisée: la Machine de Boltzmann Restreinte continue (cRBM) basée sur la structure des réseaux de diffusion. Une application pour la détection des pics d'amplitude de tension enregistrée dans une région profonde du cerveau est également présentée. Abstract-This article presents a new method of decomposition of signals with an unsupervised training model: the continuous Restricted Boltzmann Machine (cRBM) based on the structure of the Diffusion Network. An application for the detection of High-Voltage Spindle (HVS) in signals recorded in the brain is also presented

Boltzmann Machines for signals decomposition. Application to Parkinson's Disease control

International audienceThis article presents a new method of decomposition of signals with an unsupervised training model: the continuous RestrictedBoltzmann Machine (cRBM) based on the structure of the Diffusion Network. An application for the detection of High-Voltage Spindle (HVS)in signals recorded in the brain is also presented.Cet article présente une nouvelle méthode de décomposition des signaux à l'aide d'un modèle d'apprentissage non supervisée: la Machine de Boltzmann Restreinte continue (cRBM) basée sur la structure des réseaux de diffusion. Une application pour la détection des pics d'amplitude de tension enregistrée dans une région profonde du cerveau est également présentée. Abstract-This article presents a new method of decomposition of signals with an unsupervised training model: the continuous Restricted Boltzmann Machine (cRBM) based on the structure of the Diffusion Network. An application for the detection of High-Voltage Spindle (HVS) in signals recorded in the brain is also presented

Special Issue on Latent Variable Analysis and Signal Separation

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Special Issue on Latent Variable Analysis and Signal Separation

International audienc

Latent variable analysis and signal separation

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A review on generative Boltzmann networks applied to dynamic systems

International audienceThe modelling of dynamic system is a challenging problem in a large number of applications like prediction, bio-data modelling, computer vision or time-series processing. To face the complexity and the non-linearity of data, new models are regularly proposed through the literature. Among proposed models artificial neural network (ANN) have benefit of a large interest in the scientist community. The use of latent variables to extract and diffuse complex features in multilayer feedforward neural networks provide usually excellent results. In 1982, Hopfield proposes a generative and deterministic neural network to model a physical system. His work leads to the emergence of a large number of generative neural networks: Boltzmann Machine and its extensions. Different applications lead researchers to propose new extensions for the Boltzmann machine to handle dynamic systems, continuous variables or systems with complex features. In parallel, a new model named the Diffusion Network has emerged, also inspired from Hopfield network but with continuous stochastic properties and designed to solve stochastic differential equations. This paper has the objective to review the evolution of the Boltzmann Machine's family with a synthetic and historical vision and their development for dynamic problem. To write this review, we selected articles from journals/conferences and review articles (1/3 are <7 years) quoted in meta sources (Scopus and Web-of-Sciences). Once a clearly research question was asked – How generative networks model dynamic systems ? – we defined our search terms for papers. Note that not all extensions to Boltzmann machines are presented in this paper. Only models related with dynamic applications and most salient models were retained

Probit latent variables estimation for a gaussian process classifier: Application to the detection of high-voltage spindles

International audienceThe Deep Brain Stimulation (DBS) is a surgical procedure efficient to relieve symptoms of some neurodegenerative disease like the Parkinson’s disease (PD). However, apply permanently the deep brain stimulation due to the lack of possible control lead to several side effects. Recent studies shown the detection of High-Voltage Spindles (HVS) in local field potentials is an interesting way to predict the arrival of symptoms in PD people. The complexity of signals and the short time lag between the apparition of HVS and the arrival of symptoms make it necessary to have a fast and robust model to classify the presence of HVS (Y=1) or not (Y=-1) and to apply the DBS only when needed. In this paper, we focus on a Gaussian process model. It consists to estimate the latent variable f of the probit model: Pr(Y=1|input)= Φ (f(input)) with Φ the distribution function of the standard normal distribution

LNCS 6365 - Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation

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