Prédiction d'une turbulence homogène en présence de la rotation à l'aide du modèle de Gibson-Launder

Dans ce travail, les états d'équilibre des grandeurs adimensionnelles d'une turbulence homogène cisaillée soumise aux effets de la rotation sont prédits à l'aide du modèle de Gibson-Launder. La méthode numérique de Runge-Kutta d'ordre 4 est retenue pour l'intégration numérique des équations d'évolution de l'écoulement après modélisation. Le modèle retenu confirme l'existence de valeurs d'équilibre pour les grandeurs adimensionnelles ciématiques et thermique

On the Effects of Rotation on the Passive Scalar and Kinematic Fields of Homogeneous Sheared Turbulence

In this work, the effect of rotation on the evolution of kinematic and passive scalar fields in two dimensional homogeneous sheared turbulence is studied using two different approaches. The first one is analytical and it consists on the resolution of differential linear equations governing the turbulence at high shear when the non linear effects are neglected. The second one is numerical and it consists on the modeling of governing equations using the most known second order models of turbulence and their numerical integration using the fourth order Runge-kutta method. In this second approach, the classic Launder Reece Rodi model, the Speziale Sarkar Gatski and the Shih Lumley models are retained for the pressure-strain correlation, pressure-scalar gradient correlation and for the time evolution equations of the kinematic and scalar dissipations. The evolution of turbulence is studied according to the dimensionless rotation number R which is varied from -0.75 to 0.5. The obtained results are compared to the recent results of the DNS of Brethouwer. Both methods have confirmed the existence of asymptotic equilibrium states for dimensionless kinematic and scalar parameters

Two Independent Mushroom Body Output Circuits Retrieve the Six Discrete Components of Drosophila Aversive Memory

SummaryUnderstanding how the various memory components are encoded and how they interact to guide behavior requires knowledge of the underlying neural circuits. Currently, aversive olfactory memory in Drosophila is behaviorally subdivided into four discrete phases. Among these, short- and long-term memories rely, respectively, on the γ and α/β Kenyon cells (KCs), two distinct subsets of the ∼2,000 neurons in the mushroom body (MB). Whereas V2 efferent neurons retrieve memory from α/β KCs, the neurons that retrieve short-term memory are unknown. We identified a specific pair of MB efferent neurons, named M6, that retrieve memory from γ KCs. Moreover, our network analysis revealed that six discrete memory phases actually exist, three of which have been conflated in the past. At each time point, two distinct memory components separately recruit either V2 or M6 output pathways. Memory retrieval thus features a dramatic convergence from KCs to MB efferent neurons

Boundary Layer Theory: New Analytical Approximations with Error and Lambert Functions for Flat Plate without/with Suction

In this work, we investigated the problem of the boundary layer suction on a flat plate with null incidence and without pressure gradient. There is an analytical resolution using the Bianchini approximate integral method. This approximation has been achieved by Lambert or Error functions for boundary layer profiles with uniform suction, even in the case without suction. Based on these new laws, we brought out analytical expressions of several boundary layer features. This gives a necessary data to suction effect modeling for boundary layer control. To recommend our theoretical results, we numerically studied the boundary layer suction on a porous flat plate equipped with trailing edge flap deflected to 40°. We showed that this flap moves the stagnation point on the upper surface, resulting to avoid the formation of the laminar bulb of separation. Good agreement was obtained between the new analytical laws, the numerical results (CFD Fluent), and the literature results

Numerical Analysis of Homogeneous and Stratified Turbulence under Horizontal Shear via Lagrangian Stochastic Model: Richardson Number Effect

The present investigation is carried out to reveal Richardson number (Ri) effects on an homogeneous and stratified turbulence under horizontal shear. The problem is simulated via Lagrangian Stochastic model (LSM). Hence, the method of Runge Kutta with fourth order is adopted for the numerical integration of three differential systems under non linear initial conditions of Jacobitz (2002) and Jacobitz et al. (1998). This study is performed for Ri ranging from 0.2 to 3.0. It has been found that computational results by the adopted model (LSM) gave same findings than that of preceding works. It has been shown a global tendency of different parameters governing the problem to equilibrium asymptotic states for various values of Ri. The comparative study between the computations of the present LSM and direct numerical simulation of Jacobitz demonstrates a good agreement for both methods for the ratios of; potential energy Kθ/E and kinetic energy K/E toward the total energy E and the principal component of anisotropy b12 It has been found that Ri is the most important parameter affecting the thermal and dynamic fields of the flow. Hence, increase Ri conduct to increase the uniform stable stratification and decrease for the uniform mean shear S. It can be concluded that Ri is a main non-dimensional parameter which enable us to understand physical phenomenons produced inside stratified shear flows

Rapid aversive and memory trace learning during route navigation in desert ants

The ability of bees and ants to learn long visually guided routes in complex environments is perhaps one of the most spectacular pieces of evidence for the impressive power of their small brains. While flying bees can visit flowers in an optimised sequence over kilometres, walking solitary foraging ants can precisely recapitulate routes of up to a hundred metres in complex environments [1]. It is clear that route following depends largely on learnt visual information and we have a good idea how visual memories can guide individuals along them [2–6], as well as how this is implemented in the insect brain [7,8]. However, little is known about the mechanisms that control route learning and development. Here we show that ants (Melophorus bagoti and Cataglyphis fortis) navigating in their natural environments can actively learn a route detour to avoid a pit-trap. This adaptive flexibility depends on a mechanism of aversive learning based on memory traces of recently encountered stimuli, reflecting the laboratory paradigm of trace conditioning. The views experienced before falling into the trap become associated with the ensuing negative outcome and thus trigger salutary turns on the subsequent trip. This drives the ants to orient away from the goal direction and avoid the trap. If the pit-trap is avoided, the novel views experienced during the detour become positively reinforced and the new route crystallises. We discuss how such an interplay between appetitive and aversive memories might be implemented in insect neural circuitry