Métrologie et modélisation des écoulements à forte pente autour d'obstacles : application au dimensionnement des passes naturelles

Cette thèse est une partie du projet ONEMA pour le dimensionnement des passes à poissons et pour l’amélioration de la continuité écologique des cours d’eau. Ce travail s’est concentré sur les passes à poissons naturelles qui présentent des avantages de coût et paysager. Il s’agit d’un écoulement à forte pente autour des blocs (macro-rugosités) régulièrement repartis en quinconce avec des grands nombres de Froude. Les conditions hydrodynamiques sont alors très diverses, et peuvent être franchissables par un nombre élargi d’espèces de poisson. Ce mémoire présente les travaux réalisés à l’Institut de Mécanique des Fluides de Toulouse (IMFT). Afin d’étudier l’écoulement dans ces passes, on va mener des expériences sur des canaux réduits ainsi que des simulations numériques à l’aide du modèle Telemac 2D. L’objectif est de mieux connaître la structure de l’écoulement en fonction des conditions hydrauliques et géométriques comme le nombre, la forme et la taille des macro-rugosités. Plus particulièrement, la compréhension de l’interaction de phénomènes physiques généralement étudiés séparément, tels que le passage en régime torrentiel, l’interaction de sillage ou l’écoulement autour de macro-rugosités, a été recherchée. Des relations hauteur-débit ont été établies permettant une aider au dimensionnement des passes naturelles. Elles fournissent des critères de franchissement comme les vitesses maximales, la puissance dissipée ou la hauteur d’eau minimale. Pour atteindre une description plus locale de l’écoulement, des mesures de Vélocimétrie Acoustiques Doppler ont été conduites. Elles ont aussi permis de définir la plage de validité du modèle numérique 2D (Telemac). Ce modèle a alors était utilisé pour extrapoler les critères de franchissement pour des configurations non testées expérimentalement. Finalement, les connaissances sur l’écoulement ont été synthétisées pour définir des préconisations générales de dimensionnement. La précision des relations établies en laboratoire a pu aussi être vérifiée sur des passes réelles. L’hydrodynamique de ces passes est maintenant suffisamment connue pour savoir si un poisson peut remonter le courant et se reposer. Il restera à s’assurer que leur attractivité soit bonne et que des phénomènes liés aux échelles de longueurs de la turbulence ne présentent un obstacle au franchissement

Advanced Control of the dynamic voltage restorer for mitigating voltage sags in power systems

The paper presents a vector control with two cascaded loops to improve the properties of Dynamic Voltage Restorer (DVR) to minimize Voltage Sags on the grid. Thereby, a vector controlled structure was built on the rotating dq-coordinate system with the combination of voltage control and the current control. The proposed DVR control method is modelled using MATLAB-Simulink. It is tested using balanced/ unbalanced voltage sags as well as fluctuant and distorted voltages. As a result, by using this controlling method, the dynamic characteristics of the system have been improved significantly. The system performed with higher accuracy, faster response and lower distortion in the voltage sags compensation. The paper presents real time experimental results to verify the performance of the proposed method in real environments

Experimental evidence of the double-porosity effects in geomaterials

International audienceDouble-porosity is an important characteristic of microstructure in a large range of geomaterials. It designs porous media with connected fissures/fractures or aggregated soils. The origin of double-porosity can be natural or/and it can result from mechanical, chemical or biological damage. The presence of double-porosity can significantly affect the behaviour of geomaterials. In this paper we provide an experimental evidence of the double-porosity effects by performing laboratory experiments. Series of tracer dispersion experiments (in saturated and unsaturated steady-state water flow conditions) in a physical model of double-porosity geomaterial were carried out. For the comparative purposes , experiments of the same type were also performed in a single-porosity model medium. The results clearly showed that the double-porosity microstructure leads to the non-Fickian behaviour of the tracer (early breakthrough and long tail) in both saturated and unsaturated cases

The Roles of Agricultual Cooperatives in Linking Small Farmers to Set up Large Paddy Fields in Mekong Delta

Agricultural cooperatives play a significant role in linking small farmers to set up large paddy fields in the Mekong Delta. Agricultural cooperatives’ association for establishment of big paddy fields allows firms to purchase secured raw materials in mass quantities, ensuring quality while reducing related costs. Farmers are able to cut down the cost of production, sell products more effectively by gaining economy of scale and have higher position and influence over the businesses. In order to enhance the role of agricultural cooperatives in linking small farmers to establish large paddy fields in the Mekong Delta, it is necessary to synchronously implement the following solutions such as (i) improving mechanisms and policies for agricultural cooperatives to promote their role in farmer linkage to set up large paddy fields; (ii) strengthening the capacity of agricultural cooperatives management staff; iii) strengthening the role of agricultural cooperatives’ representing member farmer households in carrying out the association with firms in setting up big paddy fields. Keywords: Role of agricultural cooperatives, association, large paddy field, Mekong Delta DOI: 10.7176/JESD/10-2-1

Extension of uncertainty propagation to dynamics MFCCs for noise robust

ABSTRACT Uncertainty propagation has been successfully employed for speech recognition in nonstationary noise environments. The uncertainty about the features is typically represented as a diagonal covariance matrix for static features only. We present a framework for estimating the uncertainty over both static and dynamic features as a full covariance matrix. The estimated covariance matrix is then multiplied by scaling coefficients optimized on development data. We achieve 21% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding without uncertainty, that is five times more than the reduction achieved with diagonal uncertainty covariance for static features only

Session B5: 2D Modelling of Nature-Like Fish Passes

Abstract: The passability of nature-like fishpass depends on water depth, velocity and kinetic turbulent energy. To estimate these values the 2D Saint Venant code can be an interesting tool because it can simulate a large range of geometrical configuration and also take into account the deformation of the free surface. However the flow in a fish pass can reach the limit of the model assumptions as the hydrostatic pressure. As a consequence a series of experiments on down-scale are conducted to validate the numerical results. Velocity and turbulent fields are measured by Acoustic Doppler Velocimeter (ADV) for five combinations of discharge and slope. The comparison between experiments and model shows that the numerical results are relevant for a moderate Froude number which corresponds to fishpass application. Moreover, some crucial data related to passability are extracted from the computation as the maximum velocity location

Hydraulic Resistance of Emergent Macroroughness at Large Froude Numbers: Design of Nature-Like Fishpasses

The mean flow in a nature-like fishpass can be highly modified by the Froude number. It is important to understand this evolution to correctly design the structure. The studied configuration is an emergent staggered arrangement of obstacles. The hydraulic resistance of a fishpass is experimentally investigated that depends on several geometric parameters: block shape, ramp slope, block density, and bed roughness. An analytical model based on the balance momentum allows one to quantify the influence of each hydraulic parameter. The bed roughness has a weak influence, whereas the block shape and the Froude number are significant. The variation of the drag coefficient was analyzed to improve the stage-discharge relationship. To this end, a correlation with the block diameter and water level is proposed. The maximal velocity reached in the fishpass can also be estimated. These results have to be compared with the fish swimming ability to assess the fishpass passability

Fusion of Multiple Uncertainty Estimators and Propagators for Noise Robust ASR

International audienceUncertainty decoding has been successfully used for speech recognition in highly nonstationary noise environments. Yet, accurate estimation of the uncertainty on the denoised signals and propagation to the features remain difficult. In this work, we propose to fuse the uncertainty estimates obtained from different uncertainty estimators and propagators by linear combination. The fusion coefficients are optimized by minimizing a measure of divergence with oracle estimates on development data. Using the Kullback-Leibler divergence, we obtain 18\% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding, that is about twice as much as the reduction achieved by the best single uncertainty estimator and propagator

Extension of uncertainty propagation to dynamic MFCCs for noise robust ASR

International audienceUncertainty propagation has been successfully employed for speech recognition in nonstationary noise environments. The uncertainty about the features is typically represented as a diagonal covariance matrix for static features only. We present a framework for estimating the uncertainty over both static and dynamic features as a full covariance matrix. The estimated covariance matrix is then multiplied by scaling coefficients optimized on development data. We achieve 21\% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding without uncertainty, that is five times more than the reduction achieved with diagonal uncertainty covariance for static features only

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure