Smoother than smooth: increasing the flow conveyance of an open-channel flow by using drag reduction methods

International audienceThe drag reduction method using polymer additives is a common strategy to minimize friction losses when carrying fluids (water, oil, or slurries) in pipes over long distances. Previous studies showed that the interactions between the polymer and turbulent structures of the flow tend to modify the streamwise velocity profile close to the walls by adding a so-called elastic sublayer between the classical viscous and log layers. The gain in linear head losses can reach up to 80% depending on the roughness of the walls and the concentration of polymers. The application of this technique to sewers and the subsequent gain in discharge capacity motivated this work to quantitatively measure the drag reduction in classical open-channel flows. Three measurement campaigns were performed in a dedicated long flume for several water discharges and several polymer concentrations: backwater curves over smooth and rough channel walls (including velocity and turbulent shear-stress profiles) and flows around emerging obstacles. The addition of polymers, even in limited concentrations, allowed a high friction decrease with the typical Darcy-Weisbach coefficient reduced by factors of 2 and 1.5, respectively, in smooth and rough walls configurations without obstacles, but without strong modifications of the nondimensional velocity profiles. In contrast, when adding emerging obstacles, the flow was unaffected by the inclusion of polymers, in agreement with the prediction of the literature. The drag reduction method by addition of small concentrations of polymers thus appears to be a promising technique to increase flow conveyance in open-channel flows

Active and thermal imaging performance under bad weather conditions

Thermal imaging cameras are widely used in military contexts for their night vision capabilities and their observation range; there are based on passive infrared sensors (e.g. MWIR or LWIR range). Under bad weather conditions or when the target is partially hidden (e.g. foliage, military camouflage) they are more and more complemented by active imaging systems, a key technology to perform target identification at long range. The 2D flash imaging technique is based on a high powered pulsed laser source that illuminates the entire scene and a fast gated camera as the imaging system. Both technologies are well experienced under clear meteorological conditions; models including atmospheric effects such as turbulence are able to predict accurately their performances. However, under bad weather conditions such as rain, haze or snow, these models are not relevant. This paper introduces new models to predict performances under bad weather conditions for both active and infrared imaging systems. We first establish an enumeration of these “bad” atmospheric conditions, depending on their occurrence rate. Then we develop physical models to describe their intrinsic characteristics and their impact on the imaging system performances. Finally, we approximate these models to have a “first order” model easy to deploy for industrial applications. This theoretical work will be validated on real active and infrared data

Valeurs bromatologiques de 150 aliments de l'Ouest Africain

Un certain nombre de produits originaires des pays de l'Afrique de l'Ouest francophones, susceptibles d'être utilisés en alimentation animale, ont été analysés et étudiés en considérant les points de vue technique, économique et social de leur utilisation. Ces produits appartiennent aux catégories suivantes: grains et dérivés, autres végétaux essentiellement glucidiques, graines oléagineuses et leurs sous-produits, produits végétaux divers, produits d'origine animale. Cette étude doit permettre un calcul plus précis des ration

Experiments and Models of Active and Thermal Imaging Under Bad Weather Conditions

Thermal imaging cameras are widely used in military contexts for their night vision capabilities and their observation range; there are based on passive infrared sensors (e.g. MWIR or LWIR range). Under bad weather conditions or when the target is partially hidden (e.g. foliage, military camouflage) they are more and more complemented by active imaging systems, a key technology to perform target identification at long range. The 2D flash imaging technique is based on a high powered pulsed laser source that illuminates the entire scene and a fast gated camera as the imaging system. Both technologies are well experienced under clear meteorological conditions; models including atmospheric effects such as turbulence are able to predict accurately their performances. However, under bad weather conditions such as rain, haze or snow, these models are not relevant. This paper introduces new models to predict performances under bad weather conditions for both active and infrared imaging systems. We point out their effects on controlled physical parameters (extinction, transmission, spatial resolution, thermal background, speckle, turbulence). Then we develop physical models to describe their intrinsic characteristics and their impact on the imaging system performances. Finally, we approximate these models to have a “first order” model easy to deploy for industrial applications. This theoretical work will be validated on real active and infrared data