Section: New Results

Fluid motion estimation

Stochastic uncertainty models for motion estimation

Participants : Musaab Khalid Osman Mohammed, Etienne Mémin.

This work is concerned with the design of motion estimation technique for image-based river velocimetry. The method proposed is based on an advection diffusion equation associated to the transport of large-scale quantity with a model of the unresolved small-scale contributions. Additionally, since there is no ground truth data for such type of image sequences, a new evaluation method to assess the results has been developed. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods used in hydrology [24]. This study has been performed within the PhD thesis of Musaab Khalid [18] and through a collaboration with the Irstea Lyon hydrology research group (HHLY).

Development of an image-based measurement method for large-scale characterization of indoor airflows

Participants : Dominique Heitz, Etienne Mémin, Romain Schuster.

The goal is to design a new image-based flow measurement method for large-scale industrial applications. From this point of view, providing in situ measurement technique requires: (i) the development of precise models relating the large-scale flow observations to the velocity; (ii) appropriate large-scale regularization strategies; and (iii) adapted seeding and lighting systems, like Hellium Filled Soap Bubles (HFSB) and led ramp lighting. This work conducted within the PhD of Romain Schuster in collaboration with the compagny ITGA has started in february 2016. The first step has been to evaluate the performances of a stochastic uncertainty motion estimator when using large scale scalar images, like those obtained when seeding a flow with smoke. The PIV characterization of flows on large fields of view requires an adaptation of the motion estimation method from image sequences. The backward shift of the camera coupled to a dense scalar seeding involves a large scale observation of the flow, thereby producing uncertainty about the observed phenomena. By introducing a stochastic term related to this uncertainty into the observation term, we obtained a significant improvement of the estimated velocity field accuracy. The technique was validated on a mixing layer in a wind tunnel for HFSB and smoke tracers [40] and applied on a laboratory fume-hood [39]. This study demonstrated the feasibility of conducting on-site large-scale image-based measurements for indoor airflows characterization.

3D flows reconstruction from image data

Participants : Dominique Heitz, Etienne Mémin.

Our work focuses on the design of new tools for the estimation of 3D turbulent flow motion in the experimental setup of Tomo-PIV. This task includes both the study of physically-sound models on the observations and the fluid motion, and the design of low-complexity and accurate estimation algorithms. This year, we continued our investigation on the problem of efficient volume reconstruction. We have developed an ensemble assimilation methods for the fast reconstruction of 3D tomo-PIV motion field. The approach relies on a simplified dynamics of the flow and is a generalization of one of the popular emerging flow reconstruction technique of the PIV community referred to as "Shake the box » (STB). The study developed within an Irstea post-doctoral funding introduced a novel approach originated from the data assimilation technique comprising a sampling-based optimal estimation algorithm, namely a group of ensemble-based filtering variational schemes. We found that employing such an ensemble-based optimal estimation method helped tackling the problems associated with STB: the inaccurate predictor and/or the robustness of the optimization procedure. The proposed method (ENS) was quantitatively evaluated with synthetic particle image data built by transporting virtual particles in a turbulent cylinder wake-flow at Reynolds number equal to 3900 [43]. The study will be continued within an Irstea doctoral funding.