15h20 - 15h45 : Study of a Lagrangian denoising method for the exploration of Eulerian and Lagrangian Irreversibility in an experimental Von Karman flow

The understanding of two-particle dispersion in turbulence is crucial for the estimation of aerosol transport and airborne virus exposure. At long times, the famous Richardson prediction states that the particle dispersion behaves as a t^3 power law. At short times, the two-particle dispersion quantifies the flow irreversibility as the time-symmetry breaking shows that ‘particle separate slower backwards than forward [Jucha 2014]’. Furthermore, it is now understood that in the limit of large Reynolds number, the deterministic particle trajectories are expected to become non-unique. This phenomenon termed spontaneous stochasticity [Eyink 2014-20] states that in a turbulent flow, two particles separate independently of their initial separation length. The experimental study of such phenomena is extremely difficult since it requires long-time highly dense Lagrangian tracking. New 4D-PTV methods [Schanz 2016] seem to enable us to access such fields. However, the particle trajectories are strongly affected by measurement noise that deteriorate the measurement of short time velocity evolution as well as high order statistics. The subject of this talk is twofold : - First, we study a Lagrangian trajectory denoising method based on regularized B-spline [Gesemann 2016]. The aim is to find systematic criteria for optimization of algorithms used in 4D-PTV in order to optimize the quality of 4D-PTV measurements of turbulent flows as well as high-order of turbulence statistics. We introduce and adapt to this context two innovative tuning strategies which are commonly used in the Tikhonov regularization of inverse problems based on L-curve shape and Normalized Cumulative Periodogram (NCP). - Secondly, thanks to the denoising technique, we will show a first experimental exploration of both Eulerian and Lagrangian Irreversibility in a Von Karman flow at a resol