A particle filter to reconstruct a free-surface flow from a depth camera

We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This data assimilation scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone

Incertitudes de mesures PIV par flot optique

National audienceL'estimation des incertitudes de mesures PIV est un problème difficile. L'accès à cette information est possible pour des techniques de type flot optique basées sur une modélisation des connaissances entre l'image et le champ de vitesse à estimer. Nous montrons dans cette étude que la formulation probabiliste de ces modèles dans un cadre bayésien permet de prendre en compte les sources d'incertitudes et de déterminer une incertitude de mesure. La nouvelle approche fournit une cartographie des incertitudes de mesures et apporte un cadre pour l'analyse des a priori introduits dans les techniques de mesures PIV

Apport des modèles de lois de puissance pour l'estimation du mouvement turbulent en PIV

De manière générales les approches de type PIV nécessitent d'introduire un a priori sur la solution à estimer. Les méthodes dites locales imposent une uniformité du champ de vitesse dans un petit voisinage. Les méthodes globales, s'appuyant sur un formalisme variationnel, permettent d'introduire un terme de régularisation basé sur la dynamique des fluides. Nous montrons dans cette étude que l'introduction d'un a priori de type loi de puissance permet d'améliorer les estimations du champ de vitesse de façon significative

An ensemble filter estimation scheme for Lagrangian trajectory reconstruction

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEE [ADD1_IRSTEA]Sûreté alimentaire [ADD2_IRSTEA]Valoriser les effluents et déchets organiquesInternational audienceParticle tracking velocimetry (PTV), also referred to as Lagrangian particle tracking (LPT) has recently gained considerable revival. The trend started with the Iterative Particle Reconstruction (IPR) method that applied a projection-matchings cheme, to reconstruct 3D particles’ positions rather than voxel-based intensity, like in Tomographic PIV. Recently, IPR has given rise to the Shake-The-Box (STB) method able to tackle densely seeded flows with considerably high accuracies and reasonable computational efforts. However, in most of 3D turbulent flows, image-based experiments can only provide sparse spatiotemporal data, for which STB is not able to track particles. If more robust estimations are possible, something use-ful may be learnt from the coupling between dynamical models and image data. In responding to these problems, we introduce a novel approach originated from the data assimilation technique comprising a sampling-based optimal estimation algo-rithm, 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 robustnessof the optimization procedure. The proposed method (ENS) was quantitatively eval-uated with synthetic particle image data built by transporting virtual particles in aturbulent cylinder wake-flow at Reynolds number equal to 3900. We examined the mean positional error of the reconstructed particles, the fraction of track lost particles as well as the required CPU time/memory. We observed that even at large ppp levels (>0.1), the mean positional error of ensemble method was considerably lower than the one given by the STB method. Besides ENS performed equally well interms of data series of relatively large time separation. These preliminary resultsindicates that the ensemble-based method was indeed effective

Inflow and initial conditions for direct numerical simulation based on adjoint data assimilation

International audienceA method for generating inflow conditions for direct numerical simulations (DNS) of spatially-developing flows is presented. The proposed method is based on variational data assimilation and adjoint-based optimization. The estimation is conducted through an iterative process involving a forward integration of a given dynamical model followed by a backward integration of an adjoint system defined by the adjoint of the discrete scheme associated to the dynamical system. The approach's robustness is evaluated on two synthetic velocity field sequences provided by numerical simulation of a mixing layer and a wake flow behind a cylinder. The performance of the technique is also illustrated in a real world application by using PIV measurements to acquire the database. This method allows to denoise experimental velocity fields and to reconstruct a continuous trajectory of motion fields from discrete and unstable measurements

Variational fluid flow measurements from image sequences: synopsis and perspectives

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEEVariational approaches to image motion segmentation has been an active field of study in image processing and computer vision for two decades. We present a short overview over basic estimation schemes and report in more detail recent modifications and applications to fluid flow estimation. Key properties of these approaches are illustrated by numerical examples. We outline promising research directions and point out the potential of variational techniques in combination with correlation-based PIV methods, for improving the consistency of fluid flow estimation and simulation

Self-similar regularization of optic-flow for turbulent motion estimation

International audienceBased on self-similar models of turbulence, we propose in this paper a multi-scale regularizer in order to provide a closure to the optic-flow estimation problem. Regularization is achieved by constraining motion increments to behave as a self-similar process. The associate constrained minimization problem results in a collection of first-order optic-flow regularizers acting at the different scales. The problem is optimally solved by taking advantage of lagrangian duality. Furthermore, an advantage of using a dual formulation, is that we also infer the regularization parameters. Since, the self-similar model parameters observed in real cases can deviate from theory, we propose to add in the algorithm a bayesian learning stage. The performance of the resulting optic-flow estimator is evaluated on a particle image sequence of a simulated turbulent flow. The self-similar regularizer is also assessed on a meteorological image sequence

Dynamique des fluides : la turbulence par l'image

National audienceDes centaines d'images nous parviennent chaque jour depuis les satellites d'observation de notre biosphère. Les modèles météorologiques et climatiques pourraient tirer grand bénéfice d'une meilleure exploitation de ces images et des informations qu'elles contiennent