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The aim of this study is to improve the prediction of near-wall mean streamwise velocity profile $U^+$ by using a simple method. The $U^+$ profile is obtained by solving the momentum equation which is written as an ordinary differential equation. An eddy viscosity formulation based on a near-wall turbulent kinetic energy $k^+$ function (R. Absi, Analytical solutions for the modeled $k$-equation, ASME J. Appl. Mech. \textbf{75}, 044501, 2008) and the van Driest mixing length equation (E.R. van Driest, On turbulent flow near a wall, J. Aero. Sci. \textbf{23}, 1007, 1956) is used. The parameters obtained from the $k^+$ profiles are used for the computation of $U^+$ (variables with the superscript of + are those nondimensionalized by the wall friction velocity $u_\tau$ and the kinematic viscosity $\nu$). Comparisons with DNS data of fully-developed turbulent channel flows for $109 < Re_{\tau} < 2003$ show good agreement (where $Re_{\tau}$ denotes the friction Reynolds number defined by $u_\tau$, $\nu$ and the channel half-width $\delta$).

Sediment transport over wave-induced ripples is a very complex phenomenon where available models fail to provide accurate predictions. For coastal engineering applications, the 1-DV advection-diffusion equation could be used with an additional parameter {\alpha} related to the process of vortex shedding above ripples (Absi, 2010). The aim of this study is to provide simple practical analytical tools. An analytical eddy viscosity profile was validated by DNS data of turbulent channel flows (Absi et al., 2011). In this study, we will show that: (1) the period-averaged eddy viscosity in oscillatory boundary layers could be described by this simple analytical formulation; (2) The shape of the vertical profile is validated by period-averaged eddy viscosity of baseline (BSL) k-{\omega} model (Suntoyo and Tanaka, 2009) for sinusoidal and asymmetric waves; (3) The vertical eddy viscosity profile depends on the wave non-linearity parameter and requires therefore a specific calibration.

An ordinary differential equation for velocity distribution in open channel flows is presented based on an analysis of the Reynolds-Averaged Navier-Stokes equations and a log-wake modified eddy viscosity distribution. This proposed equation allows to predict the velocity-dip-phenomenon, i.e. the maximum velocity below the free surface. Two different degrees of approximations are presented, a semi-analytical solution of the proposed ordinary differential equation, i.e. the full dip-modified-log-wake law and a simple dip-modified-log-wake law. Velocity profiles of the two laws and the numerical solution of the ordinary differential equation are compared with experimental data. This study shows that the dip correction is not efficient for a small Coles' parameter, accurate predictions require larger values. The simple dip-modified-log-wake law shows reasonable agreement and seems to be an interesting tool of intermediate accuracy. The full dip-modified-log-wake law, with a parameter for dip-correction obtained from an estimation of dip positions, provides accurate velocity profiles.

EBI is a further education establishment which provides education in applied industrial biology at level of MSc engineering degree. Fluid mechanics at EBI was considered by students as difficult who seemed somewhat unmotivated. In order to motivate them, we applied a new play-based pedagogy. Students were asked to draw inspiration from everyday life situations to find applications of fluid mechanics and to do experiments to verify and validate some theoretical results obtained in course. In this paper, we present an innovative teaching/learning pedagogy which includes the concept of learning through play and its implications in fluid mechanics for engineering. Examples of atypical experiments in fluid mechanics made by students are presented. Based on teaching evaluation by students, it is possible to know how students feel the course. The effectiveness of this approach to motivate students is presented through an analysis of students' teaching assessment. Learning through play proved a great success in fluid mechanics where course evaluations increased substantially. Fluid mechanics has been progressively perceived as interesting, useful, pleasant and easy to assimilate. It is shown that this pedagogy which includes educational gaming presents benefits for students. These experiments seem therefore to be a very effective tool for improving teaching/learning activities in higher education.

The aim of our study is to improve the description of suspended sediment transport over wave ripples. We will first show the importance of sediment diffusivity with convective transfer (hereafter called) which is different from the sediment diffusivity associated to turbulent flux . It is possible to interpret concentration profiles, in semi-log plots, thanks to a relation between second derivative of the logarithm of concentration and derivative of (Absi, 2010). An analytical profile for will be presented and validated by experimental data obtained by Thorne et al. (2009) for medium sand. The proposed profile allows a good description of suspended sediment concentrations for fine and coarse sediments.

For the calculation of turbulent mixing in the bottom boundary layer, we present simple analytical tools for the mixing velocity wm and the mixing length lm. Based on observations of turbulence intensity measurements, the mixing velocity wm is represented by an exponential function decaying with z. We suggest two theoretical functions for the mixing length, a first lm1 obtained from the k-equation written as a constant modeled fluctuating kinetic energy flux and a second lm2 based on von K\'arm\'an's similarity hypothesis. These analytical tools were used in the finite-mixing-length model of Nielsen and Teakle (2004). The modeling of time-mean sediment concentration profiles C(z) over wave ripples shows that at the opposite of the second equation lm2 which increases the upward convexity of C(z), the first equation lm1 increases the upward concavity of C(z) and is able to reproduce the shape of the measured concentrations for coarse sand., Comment: Coastal Dynamics 2005 - Fifth Coastal Dynamics International Conference, Barcelona : Espagne (2005)

We propose a new mixing length profile, based on an extension of von K\'arm\'an similarity hypothesis, as well as the associated mixing velocity profile. This profile was compared with other profiles and was tested on three academic and experimental cases. The validation of the model was made from a set of reference examples and concerns the erosion of a bottom of sand in a uniform flow, and the filling of an extraction pit resulting from the tests presented in the European project SANDPIT. -- On propose un nouveau profil de longueur de m\'elange lm (z), bas\'e sur une extension de l'hypoth\`ese de similitude de von K\'arm\'an, ainsi que le profil de vitesse de m\'elange associ\'e. Ce profil a \'et\'e compar\'e \`a d'autres profils et test\'e sur trois cas test acad\'emiques et exp\'erimentaux. La validation du mod\`ele a \'et\'e effectu\'ee \`a partir d'un ensemble d'exemples de r\'ef\'erences et concernent l'\'erosion d'un fond de sable \'erodable dans un \'ecoulement uniforme en canal, et le remplissage d'une fosse d'extraction issus des essais pr\'esent\'es dans le projet Europ\'een SANDPIT.

Field and laboratory measurements of suspended sediments over wave ripples show, for time-averaged concentration profiles in semi-log plots, a contrast between upward convex profiles for fine sand and upward concave profiles for coarse sand. Careful examination of experimental data for coarse sand shows a near-bed upward convex profile beneath the main upward concave profile. Available models fail to predict these two profiles for coarse sediments. The 1-DV gradient diffusion model predicts the main upward concave profile for coarse sediments thanks to a suitable $\beta$(y)-function (where $\beta$ is the inverse of the turbulent Schmidt number and y is the distance from the bed). In order to predict the near-bed upward convex profile, an additional parameter {\alpha} is needed. This parameter could be related to settling velocity ($\alpha$ equal to inverse of dimensionless settling velocity) or to convective sediment entrainment process. The profiles are interpreted by a relation between second derivative of the logarithm of concentration and derivative of the product between sediment diffusivity and $\alpha$.

Airflow simulation results depend on a good prediction of near wall turbulence. In this paper a comparative study between different near wall treatments is presented. It is applied to two test cases: (1) the first concerns the fully developed plane channel flow (i.e. the flow between two infinitely large plates). Simulation results are compared to direct numerical simulation (DNS) data of Moser et al. (1999) for $Re\tau$ = 590 (where $Re\tau$ denotes the friction Reynolds number defined by friction velocity $u\tau$, kinematics viscosity $v$ and the channel half-width $\delta$); (2) the second case is a benchmark test for room air distribution (Nielsen, 1990). Simulation results are compared to experimental data obtained with laser-doppler anemometry. Simulations were performed with the aid of the commercial CFD code Fluent (2005). Near wall treatments available in Fluent were tested: Standard Wall Functions, Non Equilibrium Wall Function and Enhanced Wall Treatment. In each case, suitable meshes with adequate position for the first near-wall node are needed. Results of near-wall mean streamwise velocity U+ and turbulent kinetic energy k+ profiles are presented, variables with the superscript of + are those non dimensional by the wall friction velocity $u\tau$ and the kinematic viscosity {\nu}.

Reynolds-averaged Navier-Stokes (RANS) turbulence models (such as k-\epsilon models) are still widely used for engineering applications because of their relatively simplicity and robustness. In fully developed plane channel flow (i.e. the flow between two infinitely large plates), even if available models and near-wall treatments provide adequate mean flow velocities, they fail to predict suitable turbulent kinetic energy "TKE" profiles near walls. TKE is involved in determination of eddy viscosity/diffusivity and could therefore provide inaccurate concentrations and temperatures. In order to improve TKE a User Define Function "UDF" based on an analytical profile for TKE was developed and implemented in Fluent. Mean streamwise velocity and turbulent kinetic energy "TKE" profiles were compared to DNS data for friction Reynolds number Re\tau = 150. Simulation results for TKE show accurate profiles. Simulation results for horizontal heated channel flows obtained with Fluent are presented. Numerical results are validated by DNS data for Re\tau = 150.