Self-similar prior and wavelet bases for hidden incompressible turbulent motion

This work is concerned with the ill-posed inverse problem of estimating turbulent flows from the observation of an image sequence. From a Bayesian perspective, a divergence-free isotropic fractional Brownian motion (fBm) is chosen as a prior model for instantaneous turbulent velocity fields. This self-similar prior characterizes accurately second-order statistics of velocity fields in incompressible isotropic turbulence. Nevertheless, the associated maximum a posteriori involves a fractional Laplacian operator which is delicate to implement in practice. To deal with this issue, we propose to decompose the divergent-free fBm on well-chosen wavelet bases. As a first alternative, we propose to design wavelets as whitening filters. We show that these filters are fractional Laplacian wavelets composed with the Leray projector. As a second alternative, we use a divergence-free wavelet basis, which takes implicitly into account the incompressibility constraint arising from physics. Although the latter decomposition involves correlated wavelet coefficients, we are able to handle this dependence in practice. Based on these two wavelet decompositions, we finally provide effective and efficient algorithms to approach the maximum a posteriori. An intensive numerical evaluation proves the relevance of the proposed wavelet-based self-similar priors.Comment: SIAM Journal on Imaging Sciences, 201

Helmholtz-Hodge Decomposition on [0, 1] d by Divergence-free and Curl-free Wavelets

Abstract. This paper deals with the Helmholtz-Hodge decomposition of a vector field in bounded domain. We present a practical algorithm to compute this decomposition in the context of divergence-free and curl-free wavelets satisfying suitable boundary conditions. The method requires the inversion of divergence-free and curl-free wavelet Gram matrices. We propose an optimal preconditioning which allows to solve the systems with a small number of iterations. Finally, numerical examples prove the accuracy and the efficiency of the method

A wavelet based numerical simulation of Navier-Stokes equations under uncertainty

In this work we explore the numerical simulation of Navier-Stokes equations representation incorporating an uncertainty component on the fluid flow velocity. The uncertainty considered is formalized through a random field uncorrelated in time but correlated in space. This model enables the constitution of large scale dynamical models of the flows in which emerges an anisotropic subgrid tensor reminiscent to the Reynolds stress tensor. This subgrid model is directly related to the uncertainty variance tensor. This property allows us to propose simple models of this stress tensor that are computed directly on the resolved component. These models are here assessed on a standard Green-Taylor vortex at Reynolds 1600 and on a Crow instability at Reynolds 3200. We also describe in this paper an efficient divergence free wavelet scheme for the numerical simulation of this model. The stability condition of the divergence-free wavelet based numerical scheme we used in this study is also discussed

Effective Wavelet-Based Regularization of Divergence-free Fractional Brownian Motion

This paper presents a method for regularization of inverse problems. The vectorial bi-dimensional unknown is assumed to be the realization of an isotropic divergence-free fractional Brownian Motion (fBm). The method is based on fractional Laplacian and divergence-free wavelet bases. The main advantage of these bases is to enable an easy formalization in a Bayesian framework of fBm priors, by simply sampling wavelet coe cients according to Gaussian white noise. Fractional Laplacians and the divergence-free projector can naturally be implemented in the Fourier domain. An interesting alternative is to remain in the spatial domain. This is achieved by the analytical computation of the connection coefficients of divergence-free fractional Laplacian wavelets, which enables to easily rotate this simple prior in any sufficiently "regular" wavelet basis. Taking advantage of the tensorial structure of a separable fractional wavelet basis approximation, isotropic regularization is then computed in the spatial domain by low-dimensional matrix products. The method is successfully applied to fractal image restoration and turbulent optic-flow estimation

Divergence-free Wavelets and High Order Regularization

International audienceExpanding on a wavelet basis the solution of an inverse problem provides several advantages. Wavelet bases yield a natural and efficient multiresolution analysis. The continuous representation of the solution with wavelets enables analytical calculation of regularization integrals over the spatial domain. By choosing differentiable wavelets, high-order derivative regularizers can be designed, either taking advantage of the wavelet differentiation properties or via the basis's mass and stiffness matrices. Moreover, differential constraints on vector solutions, such as the divergence-free constraint in physics, can be handled with biorthogonal wavelet bases. This paper illustrates these advantages in the particular case of fluid flows motion estimation. Numerical results on synthetic and real images of incompressible turbulence show that divergence-free wavelets and high-order regularizers are particularly relevant in this context

Ondelettes à divergence nulle et à rotationnel nul sur le carré. Application à Navier-Stokes

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Ondelettes pour la prise en compte de conditions aux limites en turbulence incompressible

This work concerns wavelet numerical methods for the simulation of incompressible turbulent flow. The main objective of this work is to take into account physical boundary conditions in the resolution of Navier-Stokes equations on wavelet basis. Unlike previous work where the vorticity field was decomposed in term of classical wavelet bases, the point of view adopted here is to compute the velocity field of the flow in its divergence-free wavelet series. We are then in the context of velocity-pressure formulation of the incompressible Navier-Stokes equations, for which the boundary conditions are written explicitly on the velocity field, which differs from the velocity-vorticity formulation. The principle of the method implemented is to incorporate directly the boundary conditions on the wavelet basis . This work extends the work of the thesis of E. Deriaz realized in the periodic case. The first part of this work highlights the definition and the construction of new divergence-free and curl-free wavelet bases on $[0,1]^n$, which can take into account boundary conditions, from original works of P. G. Lemarie-Rieusset, K. Urban, E. Deriaz and V. Perrier. In the second part, efficient numerical methods using these new wavelets are proposed to solve various classical problem: heat equation, Stokes problem and Helmholtz-Hodge decomposition in the non-periodic case. The existence of fast algorithms makes the associated methods more competitive. The last part is devoted to the definition of two new numerical schemes for the resolution of the incompressible Navier-Stokes equations into wavelets, using the above ingredients. Numerical experiments conducted for the simulation of driven cavity flow in two dimensions or the issue of reconnection of vortex tubes in three dimensions show the strong potential of the developed algorithms.Ce travail de thèse concerne les méthodes numériques à base d'ondelettes pour la simulation de la turbulence incompressible. L'objectif principal est la prise en compte de conditions aux limites physiques dans la résolution des équations de Navier-Stokes. Contrairement aux travaux précédents où la vorticité était décomposée sur base d'ondelettes classiques, le point de vue qui est adopté ici vise à calculer le champ de vitesse de l'écoulement sous la forme d'une série d'ondelettes à divergence nulle. On est alors dans le cadre des équations de Navier-Stokes incompressibles en formulation vitesse-pression, pour lesquelles les conditions aux limites sur la vitesse s'écrivent explicitement, ce qui diffère de la formulation vitesse-tourbillon. Le principe de la méthode développée dans cette thèse consiste à injecter directement les conditions aux limites sur la base d'ondelettes. Ce travail prolonge la thèse de E. Deriaz réalisée dans le cas périodique. La première partie de ce travail a donc été la définition et la mise en œuvre de nouvelles bases d'ondelettes à divergence nulle ou à rotationnel nul sur $[0,1]^n$, permettant la prise en compte de conditions aux limites, à partir des travaux originaux de P. G. Lemarié-Rieusset, K. Urban, E. Deriaz et V. Perrier. Dans une deuxième partie, des méthodes numériques efficaces utilisant ces nouvelles ondelettes sont proposées pour résoudre différents problèmes classiques : équation de la chaleur, problème de Stokes et calcul de la décomposition de Helmholtz-Hodge en non périodique. L'existence d'algorithmes rapides associés rend les méthodes compétitives. La dernière partie est consacrée à la définition de deux nouveaux schémas de résolution des équations de Navier-Stokes incompressibles par ondelettes, qui utilisent les ingrédients précédents. Des expériences numériques menées pour la simulation d'écoulement en cavité entraînée en dimension deux ou le problème de la reconnection de tubes de vortex en dimension trois montrent le fort potentiel des algorithmes développés

Utilisation des ondelettes à divergence nulle en mécanique des fluides

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Effective construction of divergence-free wavelets on the square

International audienceWe present an effective construction of divergence-free wavelets on the square, with suitable boundary conditions. Since 2D divergence-free vector functions are the curl of scalar stream-functions, we simply derive divergence-free multiresolution spaces and wavelets by considering the curl of standard biorthogonal multiresolution analyses (BMRAs) on the square. The key point of the theory is that the derivative of a 1D BMRA is also a BMRA, as established by Jouini and Lemari'e-Rieusset [11]. We propose such construction in the context of generic compactly supported wavelets, which allows fast algorithms. Examples illustrate the practicality of the method