Christophe Cuvier

Characterisation of a high Reynolds number boundary layer subject to pressure gradient and separation

The flow over a ramp model is characterised in detail in the present study. In the selected configuration, a turbulent boundary layer developed on a flat plate is first accelerated in a curved contraction. It is then submitted to a mild adverse pressure gradient on a flat plate followed by a separation above a flap. Inlet boundary conditions and pressure distribution are provided to allow numerical simulations. The flow in the mild adverse pressure gradient region is characterised with hot-wire anemometry. In this region, the boundary layer thickness is of the order of 20 cm, the momentum Reynolds number is about 11,000 and the Clauser pressure gradient parameter β in the stabilised region is about 0.4. Particular emphasis is laid on the separation to provide quantitative information to evaluate turbulence models. This is achieved through a large streamwise two-dimensional two-component particle image velocimetry (2D2C PIV) plane which contains all the separation bubble and part of the flow upstream and downstream of it. The separation border is detected using the backflow coefficient, resulting in a separation length of about 3.49 Hs (with Hs the flap step height) and a maximum height of about 0.17 Hs. The Reynolds stresses and their main production terms are also determined. A region of high turbulence intensity develops above the separation border for all the measured components. The production of dominates the production of turbulent kinetic energy which implies a redistribution from to to explain the increase observed. The production term drives the production of in the first part of the flap which is not the case for zero pressure gradient boundary layers. Finally, a high similarity is observed between and as the production of the latter is dominated by . This flow appears as a challenging test case for Reynolds averaged Navier–Stokes (RANS) and large eddy simulation (LES) validation.

Extensive characterisation of a high Reynolds number decelerating boundary layer using advanced optical metrology

ABSTRACT Over the last years, the observation of large-scale structures in turbulent boundary layer flows has stimulated intense experimental and numerical investigations. Nevertheless, partly due to the lack of comprehensive experimental data at sufficiently high Reynolds number, our understanding of turbulence near walls, especially in decelerating situations, is still quite limited. The aim of the present contribution is to combine the equipment and skills of several teams to perform a detailed characterisation of a large-scale turbulent boundary layer under adverse pressure gradient. Extensive particle image velocimetry (PIV) measurements are performed, including a set-up with 16 sCMOS cameras allowing the characterisation of the boundary layer on 3.5 m, stereo PIV and high resolution near wall measurements. In this paper, detailed statistics are presented and discussed, boundary conditions are carefully characterised, making this experiment a challenging test case for numerical simulation.

Closed-loop control of experimental shear flows using machine learning

We propose a novel closed-loop control strategy of turbulent flows using machine learning methods in a model-free manner. This strategy, called Machine Learning Control (MLC), allows – for the first time – to detect and exploit all enabling nonlinear actuation mechanisms in an un-supervised automatic manner. In this communication, we focus on MLC applications for in-time control of experimental shear flows and demonstrate how it outperforms state-of-the-art control. In particular, MLC is applied to three different experimental closed-loop control setups: (1) the TUCOROM mixing layer tunnel, (2) the Gortler PMMH water tunnel with a backward facing step, and (3) the LML Boundary Layer wind tunnel with a separating turbulent boundary layer. In all three cases, MLC finds a control which yields a significantly better performance with respect to the given cost functional as compared to the best previously tested open-loop actuation. We foresee numerous potential applications to most nonlinear multiple-input multiple-output (MIMO) flow control problems, particularly in experiments. In particular, the model-free architecture of MLC enables its application to a large class of complex nonlinear systems in all areas of science.

3D Spatial Correlation Tensor from an L-Shaped SPIV Experiment in the Near Wall Region

Understanding the turbulence organization near a wall is necessary to help improving turbulence models. From the experimental point of view, many researchers have worked on this subject since the fifties. Recently, Foucaut et al. (Exp. Fluids 50(4, Sp. Iss. SI):839–846, 2011) [16] have proposed a new idea to compute the 3D correlation tensor from two normal velocity fields when there are two homogeneous directions in the flow. The idea of the present contribution is to propose a specific SPIV experiment which allows the computation of the full 3D spatial correlation tensor in the near wall region of the TBL. This experiment composed of two Stereoscopic PIV planes normal to the wall which were simultaneously recorded was performed in the LML wind tunnel. The 3D correlation is then computed from the two velocity planes in order to give some information about the near wall turbulence organization. Conditioning the average by specific events allows us to improve the analysis of the organisation. It can evidence the link between the events.