Over the last three decades, CFD simulations have become commonplace as a tool in the engineering and design of high-speed aircraft. Experiments are often complemented by computational simulations, and CFD technologies have proved very useful in both the reduction of aircraft development cycles, and in the simulation of conditions difficult to reproduce experimentally. Great advances have been made in the field since its introduction, especially in areas of meshing, computer architecture, and solution strategies. Despite this, there still exist many computational limitations in existing CFD methods; in particular, reliable higher order and hp-adaptive methods for the Navier-Stokes equations that govern viscous compressible flow. Solutions to the equations of viscous flow can display shocks and boundary layers, which are characterized by localized regions of rapid change and high gradients. The use of adaptive meshes is crucial in such settings -- good resolution for such problems under uniform meshes is computationally prohibitive and impractical for most physical regimes of interest. However, the construction of "good" meshes is a difficult task, usually requiring a-priori knowledge of the form of the solution. An alternative to such is the construction of automatically adaptive schemes; such methods begin with a coarse mesh and refine based on the minimization of error. However, this task is difficult, as the convergence of numerical methods for problems in CFD is notoriously sensitive to mesh quality. Additionally, the use of adaptivity becomes more difficult in the context of higher order and hp methods. Many of the above issues are tied to the notion of robustness, which we define loosely for CFD applications as the degradation of the quality of numerical solutions on a coarse mesh with respect to the Reynolds number, or nondimensional viscosity. For typical physical conditions of interest for the compressible Navier-Stokes equations, the Reynolds number dictates the scale of shock and boundary layer phenomena, and can be extremely high -- on the order of 10⁷ in a unit domain. For an under-resolved mesh, the Galerkin finite element method develops large oscillations which prevent convergence and pollute the solution. The issue of robustness for finite element methods was addressed early on by Brooks and Hughes in the SUPG method, which introduced the idea of residual-based stabilization to combat such oscillations. Residual-based stabilizations can alternatively be viewed as modifying the standard finite element test space, and consequently the norm in which the finite element method converges. Demkowicz and Gopalakrishnan generalized this idea in 2009 by introducing the Discontinous Petrov-Galerkin (DPG) method with optimal test functions, where test functions are determined such that they minimize the discrete linear residual in a dual space. Under the ultra-weak variational formulation, these test functions can be computed locally to yield a symmetric, positive-definite system. The main theoretical thrust of this research is to develop a DPG method that is provably robust for singular perturbation problems in CFD, but does not suffer from discretization error in the approximation of test functions. Such a method is developed for the prototypical singular perturbation problem of convection-diffusion, where it is demonstrated that the method does not suffer from error in the approximation of test functions, and that the L² error is robustly bounded by the energy error in which DPG is optimal -- in other words, as the energy error decreases, the L² error of the solution is guaranteed to decrease as well. The method is then extended to the linearized Navier-Stokes equations, and applied to the solution of the nonlinear compressible Navier-Stokes equations. The numerical work in this dissertation has focused on the development of a 2D compressible flow code under the Camellia library, developed and maintained by Nathan Roberts at ICES. In particular, we have developed a framework allowing for rapid implementation of problems and the easy application of higher order and hp-adaptive schemes based on a natural error representation function that stems from the DPG residual. Finally, the DPG method is applied to several convection diffusion problems which mimic difficult problems in compressible flow simulations, including problems exhibiting both boundary layers and singularities in stresses. A viscous Burgers' equation is solved as an extension of DPG to nonlinear problems, and the effectiveness of DPG as a numerical method for compressible flow is assessed with the application of DPG to two benchmark problems in supersonic flow. In particular, DPG is used to solve the Carter flat plate problem and the Holden compression corner problem over a range of Mach numbers and laminar Reynolds numbers using automatically adaptive schemes, beginning with very under-resolved/coarse initial meshes. / text

This thesis demonstrates the use of numerical simulation in predicting the behavior of proteins in a flow environment. A novel convection-diffusion-reaction computational model is first introduced to simulate fibroblast growth factor (FGF-2) binding to its receptor (FGFR) on cell surfaces and regulated by heparan sulfate proteoglycan (HSPG) under flow in a bioreactor. The model includes three parts: (1) the flow of medium using incompressible Navier-Stokes equations; (2) the mass transport of FGF-2 using convection-diffusion equations; and (3) the cell surface binding using chemical kinetics. The model consists of a set of coupled nonlinear partial differential equations (PDEs) for flow and mass transport, and a set of coupled nonlinear ordinary differential equations (ODEs) for binding kinetics. To handle pulsatile flow, several assumptions are made including neglecting the entrance effects and an approximate analytical solution for axial velocity within the fibers is obtained. To solve the time-dependent mass transport PDEs, the second order implicit Euler method by finite volume discretization is used. The binding kinetics ODEs are stiff and solved by an ODE solver (CVODE) using Newton’s backward differencing formula. To obtain a reasonable accuracy of the biochemical reactions on cell surfaces, a uniform mesh is used. This basic model can be used to simulate any growth factor-receptor binding on cell surfaces on the wall of fibers in a bioreactor, simply by replacing binding kinetics ODEs. Circulation is an important delivery method for natural and synthetic molecules, but microenvironment interactions, regulated by endothelial cells and critical to the molecule’s fate, are difficult to interpret using traditional approaches. Growth factor capture under flow is analyzed and predicted using computer modeling mentioned above and a three-dimensional experimental approach that includes pertinent circulation characteristics such as pulsatile flow, competing binding interactions, and limited bioavailability. An understanding of the controlling features of this process is desired. The experimental module consists of a bioreactor with synthetic endotheliallined hollow fibers under flow. The physical design of the system is incorporated into the model parameters. FGF-2 is used for both the experiments and simulations. The computational model is based on the flow and reactions within a single hollow fiber and is scaled linearly by the total number of fibers for comparison with experimental results. The model predicts, and experiments confirm, that removal of heparan sulfate (HS) from the system will result in a dramatic loss of binding by heparin-binding proteins, but not by proteins that do not bind heparin. The model further predicts a significant loss of bound protein at flow rates only slightly higher than average capillary flow rates, corroborated experimentally, suggesting that the probability of capture in a single pass at high flow rates is extremely low. Several other key parameters are investigated with the coupling between receptors and proteoglycans shown to have a critical impact on successful capture. The combined system offers opportunities to examine circulation capture in a straightforward quantitative manner that should prove advantageous for biological or drug delivery investigations. For some complicated binding systems, where there are more growth factors or proteins with competing binding among them moving through hollow fibers of a bioreactor coupled with biochemical reactions on cell surfaces on the wall of fibers, a complex model is deduced from the basic model mentioned above. The fluid flow is also modeled by incompressible Navier-Stokes equations as mentioned in the basic model, the biochemical reactions in the fluid and on the cell surfaces are modeled by two distinctive sets of coupled nonlinear ordinary differential equations, and the mass transports of different growth factors or complexes are modeled separately by different sets of coupled nonlinear partial differential equations. To solve this computationally intensive system, parallel algorithms are devised, in which all the numerical computations are solved in parallel, including the discretization of mass transport equations and the linear system solver Stone’s Implicit Procedure (SIP). A parallel SIP solver is designed, in which pipeline technique is used for LU factorization and an overlapped Jacobi iteration technique is chosen for forward and backward substitutions. For solving binding equations ODEs in the fluid and on cell surfaces, a parallel scheme combined with a sequential CVODE solver is used. The simulation results are obtained to demonstrate the computational efficiency of the algorithms and further experiments need to be conducted to verify the predictions.

The main objective of this project is to develop a "black-box" multigrid preconditioner for the iterative solution of finite element discretisations of the convection-diffusion equation with dominant convection. This equation can be considered a stand alone scalar problem or as part of a more complex system of partial differential equations, such as the Navier-Stokes equations. The project will focus on the stand alone scalar problem. Multigrid is considered an optimal preconditioner for scalar elliptic problems. This strategy can also be used for convection-diffusion problems, however an appropriate robust smoother needs to be developed to achieve mesh-independent convergence. The focus of the thesis is on the development of such a smoother. In this context a novel smoother is developed referred to as truncated incomplete factorisation (tILU) smoother. In terms of computational complexity and memory requirements, the smoother is considerably less expensive than the standard ILU(0) smoother. At the same time, it exhibits the same robustness as ILU(0) with respect to the problem and discretisation parameters. The new smoother significantly outperforms the standard damped Jacobi smoother and is a competitor to the Gauss-Seidel smoother (and in a number of important cases tILU outperforms the Gauss-Seidel smoother). The new smoother depends on a single parameter (the truncation ratio). The project obtains a default value for this parameter and demonstrated the robust performance of the smoother on a broad range of problems. Therefore, the new smoothing method can be regarded as "black-box". Furthermore, the new smoother does not require any particular ordering of the nodes, which is a prerequisite for many robust smoothers developed for convection-dominated convection-diffusion problems. To test the effectiveness of the preconditioning methodology, we consider a number of model problems (in both 2D and 3D) including uniform and complex (recirculating) convection fields discretised by uniform, stretched and adaptively refined grids. The new multigrid preconditioner within block preconditioning of the Navier-Stokes equations was also tested. The numerical results gained during the investigation confirm that tILU is a scalable, robust smoother for both geometric and algebraic multigrid. Also, comprehensive tests show that the tILU smoother is a competitive method.

Les déplacements cellulaires, collectifs ou individuels, sont essentiels pour assurer des fonctions fondamentales de l'organisme (réponse immunitaire, morphogenèse), mais jouent également un rôle crucial dans le développement de certaines pathologies (invasion métastatique).Les processus cellulaires à l'origine du déplacement forment une activité complexe, auto-organisée et fortement multi-échelle en temps mais aussi en espace. Mettre en évidence des principes généraux de la migration est donc un enjeu majeur. Dans cette thèse, nous nous intéressons à la construction de modèles de migration individuelle qui prennent en compte ce caractère multi-échelle de manière minimale.Dans une première partie, nous nous intéressons à des modèles particulaires. Nous décrivons des processus intracellulaires clés de la migration de manière discrète au moyen de processus de population. Puis, par une renormalisation en grand nombre d'individus, taille infinitésimale et dynamique accélérée, nous obtenons des équations de dynamique continue et stochastique, permettant de faire le lien entre la dynamique intracellulaire et le déplacement macroscopique.Nous nous confrontons d'abord à la situation d'un leucocyte se déplaçant dans une artère, et développant des liaisons de différentes natures avec les molécules de la paroi, jusqu'à éventuellement s'arrêter. La dynamique de formation de liaisons est décrite par un processus stochastique de type Naissance et Mort avec Immigration. Ces liaisons correspondent à des forces de résistance au mouvement. Nous obtenons explicitement le temps d'arrêt moyen de la cellule.Puis, nous nous intéressons à la reptation cellulaire, qui se produit grâce à la formation d'excroissances au bord de la cellule, appelées protrusions, qui avancent sur le substrat et exercent des forces de traction. Nous modélisons cette dynamique au moyen d'un processus de population structurée par l'orientation de la protrusion. Le modèle continu limite obtenu peut être étudié pour la migration 1D, et donne lieu à une équation de Fokker-Planck sur la distribution de probabilité de la population de protrusion. L'étude d'une configuration stationnaire permet de mettre en avant une dichotomie entre un état non motile et un état de déplacement directionnel.Dans une seconde partie, nous construisons un modèle déterministe minimal de migration dans un domaine discoïdal non déformable. Nous nous basons sur l'idée selon laquelle les structures responsables de la migration renforcent la polarisation de la cellule, ce qui favorise en retour un déplacement directionnel. Cette boucle positive passe par le transport d'un marqueur moléculaire dont la répartition inhomogène caractérise un état polarisé.Le modèle comporte un problème de convection-diffusion sur la concentration en marqueur, où le champs d'advection correspond à la vitesse d'un fluide de Darcy modélisant le cytosquelette. Son caractère actif est porté par des termes de bord, ce qui fait l'originalité du modèle.Du point de vue analytique, le modèle 1D présente une dichotomie face à une masse critique. Dans les cas sous-critique et critique, il est possible de montrer l'existence globale de solutions faibles, ainsi que la convergence à taux explicite vers l'unique état stationnaire correspondant à un état non polarisé. Au delà de la masse critique et pour des masses intermédiaires, nous mettons en évidence deux états stationnaires supplémentaires correspondant à des profils polarisés. De plus, pour des conditions initiales assez asymétrique, nous démontrons l'apparition d'un blow-up en temps fini.Du point de vue numérique, des tests numériques en 2D sont effectués en volumes finis (Matlab) et éléments finis (FreeFem++). Ils permettent de mettre en évidence à nouveau des états motiles et non motiles. L'effet de perturbations stochastiques est étudié, permettant d'aborder des cas de réponse à des signaux extérieurs chimique (chimiotactisme) ou mécanique (obstacle). / Collective or individual cell displacements are essential in fundamental physiological processes (immune response, embryogenesis) as well as in pathological developments (tumor metastasis). The intracellular processes responsible for cell motion have a complex self-organized activity spanning different time and space scales. Highlighting general principles of migration is therefore a challenging task.In a first part, we build stochastic particular models of migration. To do so, we describe key intracellular processes as discrete in space by using stochastic population models. Then, by a renormalization in large population, infinitesimal size and accelerated dynamics, we obtain continuous stochastic equations for the dynamics of interest, allowing a relation between the intracellular dynamics and the macroscopic displacement.First, we study the case of a leukocyte carried by the blood flow and developing adhesive bonds with the artery wall, until an eventual stop. The binding dynamics is described by a stochastic Birth and Death with Immigration process. These bonds correspond to resistive forces to the motion. We obtain explicitly the mean stopping time of the cell.Then, we study the case of cell crawling, that happens by the formation of protrusions on the cell edge, that grow on the substrate and exert traction forces. We describe this dynamics by a structured population process, where the structure comes from the protrusions' orientations. The limiting continuous model can be analytically studied in the 1D migration case, and gives rise to a Fokker-Planck equation on the probability distribution for the protrusion density. For a stationary profile, we can show the existence of a dichotomy between a non motile state and a directional displacement state.In a second part, we build a deterministic minimal migration model in a discoïdal cell domain. We base our work on the idea such that the structures responsible for migration also reinforce cell polarisation, which favors in return a directional displacement. This positive feedback loop involves the convection of a molecular marker, whose inhomogeneous spatial repartition is characteristic of a polarised state.The model writes as a convection-diffusion problem for the marker's concentration, where the advection field is the velocity field of the Darcy fluid that describes the cytoskeleton. Its active character is carried by boundary terms, which makes the originality of the model.From the analytical point of vue, the 1D model shows a dichotomy depending on a critical mass for the marker. In the subcritical and critical cases, it is possible to show global existence of weak solutions, as well as a rate-explicit convergence of the solution towards the unique stationary profile, corresponding to a non-motile state. Above the critical mass, for intermediate values, we show the existence of two additional stationary solutions corresponding to polarised motile profiles. Moreover, for asymmetric enough initial profiles, we show the finite time apparition of a blowup.Studying a more complex model involving activation of the marker at the cell membrane permits to get rid of this singularity.From the numerical point of vue, numerical experiments are led in 2D either in finite volumes (Matlab) or finite elements (FreeFem++) discretizations. They allow to show both motile and non motile profiles. The effect of stochastic fluctuations in time and space are studied, leading to numerical simulations of cases of responses to an external signal, either chemical (chemotaxis) or mechanical (obstacles).

A singularly perturbed convection-diffusion problem in two and three space dimensions is discretized using the streamline upwind Petrov Galerkin (SUPG) variant of the finite element method. The dominant convection frequently gives rise to solutions with layers; hence anisotropic finite elements can be applied advantageously. The main focus is on a posteriori energy norm error estimation that is robust in the perturbation parameter and with respect to the mesh anisotropy. A residual error estimator and a local problem error estimator are proposed and investigated. The analysis reveals that the upper error bound depends on the alignment of the anisotropies of the mesh and of the solution. Hence reliable error estimation is possible for suitable anisotropic meshes. The lower error bound depends on the problem data via a local mesh Peclet number. Thus efficient error estimation is achieved for small mesh Peclet numbers. Altogether, error estimation approaches for isotropic meshes are successfully extended to anisotropic elements. Several numerical experiments support the analysis.

L'objectif de ce travail est de développer des algorithmes de décomposition de domaine pour des équations de l'océanographie. Les méthodes de décomposition de domaine consistent à décomposer un domaine de calcul de grand taille en plusieurs sous-domaines plus petits. Elles s'appliquaient jusqu'à présent à des problèmes stationnaires, nous généralisons ici ce type de méthodes aux problèmes en temps ('Schwarz Waveform Relaxation Methods'). Le principal but de cette nouvelle approche est de simuler des problèmes multiphysiques pour lesquels il est intéressant d'avoir une discrétisation temporelle différente dans chaque sous-domaine. Nous généralisons aux équations d'évolution une méthode récente qui consiste à écrire les conditions transparentes (Conditions aux Limites Absorbantes) puis les approche par des opérateurs différentiels d'ordre 1 dans la direction normale à l'interface et d'ordre 0 ou 1 dans la direction tangentielle. Nous développons cette méthode premièrement pour l'équation de convection diffusion qui traduit notamment l'advection des traceurs (température, salinité, traceurs passifs) dans l'océan. Nous approchons les opérateurs exacts par développement de Taylor, ou par optimisation du taux de convergence. Nous démontrons que les problèmes aux limites introduits sont bien posés. Puis nous montrons la convergence des algorithmes correspondants. Des résultats numériques sont implémentés dans le cas avec ou sans recouvrement et mettent en évidence la réelle efficacité des méthodes optimisées. Nous faisons ensuite un premier pas vers le couplage d'équations en implémentant un algorithme de couplage de l'équation de convection avec l'équation de convection diffusion. Ensuite nous traitons les équations de Saint Venant, moyennes verticales des équations de Navier-Stokes en milieu tournant. Nous introduisons pour ce système un algorithme de décomposition de domaine avec des conditions d'interface qui s'obtiennent par des considérations physiques. Nous montrons que cet algorithme est bien posé puis nous en démontrons la convergence. Des résultats numériques concluants sont également exposés.

Ce travail a pour objet le développement et l'étude d'une méthode de décomposition de domaine, la méthode Optimisée d'Ordre 2 (OO2), pour la résolution de l'équation de convection-diffusion. Son atout principal est de permettre d'utiliser un découpage quelconque du domaine, sans savoir à l'avance où sont situés les phénomènes physiques tels que les couches limites ou les zones de recirculation. La méthode OO2 est une méthode de décomposition de domaine sans recouvrement, itérative, parallélisable. Le domaine de calcul est divisé en sous-domaines, et on résout le problème de départ dans chaque sous-domaine, avec des conditions de raccord spécifiques sur les interfaces des sous-domaines. Ce sont des conditions différentielles d'ordre 1 dans la direction normale et d'ordre 2 dans la direction tangente à l'interface qui approchent, par une procédure d'optimisation, les Conditions aux Limites Artificielles (CLA). L'utilisation des CLA en décomposition de domaine permet de définir des algorithmes stables. Une reformulation de la méthode de Schwarz conduit à un problème d'interface. Celui-ci est résolu par une méthode itérative de type Krylov (BICG-STAB, GMRES, GCR). La méthode est appliquée à un schéma aux différences finies décentré, puis à un schéma volumes finis. Un préconditionneur ``basses fréquences'' est ensuite introduit et étudié, dans le but d'avoir une convergence indépendante du nombre de sous-domaines. Ce préconditionneur est une extension aux problèmes non-symétriques d'un préconditionneur utilisé pour des problèmes symétriques. Enfin, l'utilisation de conditions différentielles d'ordre 2 le long de l'interface nécessite d'ajouter des conditions de raccord aux points de croisement des sous-domaines. Une étude est menée a ce sujet, qui permet de montrer que les problèmes dans chaque sous-domaine sont bien posés.

This is a book on numerical methods for singular perturbation problems - in particular stationary convection-dominated convection-diffusion problems. More precisely it is devoted to the construction and analysis of layer-adapted meshes underlying these numerical methods. An early important contribution towards the optimization of numerical methods by means of special meshes was made by N.S. Bakhvalov in 1969. His paper spawned a lively discussion in the literature with a number of further meshes being proposed and applied to various singular perturbation problems. However, in the mid 1980s this development stalled, but was enlivend again by G.I. Shishkin's proposal of piecewise- equidistant meshes in the early 1990s. Because of their very simple structure they are often much easier to analyse than other meshes, although they give numerical approximations that are inferior to solutions on competing meshes. Shishkin meshes for numerous problems and numerical methods have been studied since and they are still very much in vogue. With this contribution we try to counter this development and lay the emphasis on more general meshes that - apart from performing better than piecewise-uniform meshes - provide a much deeper insight in the course of their analysis. In this monograph a classification and a survey are given of layer-adapted meshes for convection-diffusion problems. It tries to give a comprehensive review of state-of-the art techniques used in the convergence analysis for various numerical methods: finite differences, finite elements and finite volumes. While for finite difference schemes applied to one-dimensional problems a rather complete convergence theory for arbitrary meshes is developed, the theory is more fragmentary for other methods and problems and still requires the restriction to certain classes of meshes.

The central theme of this thesis is the design of optimal balanced black-box stopping criteria in iterative solvers of symmetric positive-definite, symmetric indefinite, and nonsymmetric linear systems arising from finite element approximation of stochastic (parametric) partial differential equations. For a given stochastic and spatial approximation, it is known that iteratively solving the corresponding linear(ized) system(s) of equations to too tight algebraic error tolerance results in a wastage of computational resources without decreasing the usually unknown approximation error. In order to stop optimally-by avoiding unnecessary computations and premature stopping-algebraic error and a posteriori approximation error estimate must be balanced at the optimal stopping iteration. Efficient and reliable a posteriori error estimators do exist for close estimation of the approximation error in a finite element setting. But the algebraic error is generally unknown since the exact algebraic solution is not usually available. Obtaining tractable upper and lower bounds on the algebraic error in terms of a readily computable and monotonically decreasing quantity (if any) of the chosen iterative solver is the distinctive feature of the designed optimal balanced stopping strategy. Moreover, this work states the exact constants, that is, there are no user-defined parameters in the optimal balanced stopping tests. Hence, an iterative solver incorporating the optimal balanced stopping methodology that is presented here will be a black-box iterative solver. Typically, employing such a stopping methodology would lead to huge computational savings and in any case would definitely rule out premature stopping. The constants in the devised optimal balanced black-box stopping tests in MINRES solver for solving symmetric positive-definite and symmetric indefinite linear systems can be estimated cheaply on-the- fly. The contribution of this thesis goes one step further for the nonsymmetric case in the sense that it not only provides an optimal balanced black-box stopping test in a memory-expensive Krylov solver like GMRES but it also presents an optimal balanced black-box stopping test in memory-inexpensive Krylov solvers such as BICGSTAB(L), TFQMR etc. Currently, little convergence theory exists for the memory-inexpensive Krylov solvers and hence devising stopping criteria for them is an active field of research. Also, an optimal balanced black-box stopping criterion is proposed for nonlinear (Picard or Newton) iterative method that is used for solving the finite dimensional Navier-Stokes equations. The optimal balanced black-box stopping methodology presented in this thesis can be generalized for any iterative solver of a linear(ized) system arising from numerical approximation of a partial differential equation. The only prerequisites for this purpose are the existence of a cheap and tight a posteriori error estimator for the approximation error along with cheap and tractable bounds on the algebraic error.

Dans cette thèse, nous nous intéressons à des méthodes numériques pour un modèle d'écoulements incompressibles et miscibles ayant des application dans l'hydrogéologie et l'ingénierie pétrolière. Nous étudions et analysons un schéma numérique combinant une méthode d'éléments finis mixtes (EFM) et une méthode des volumes finis (VF) pour approcher le système couplé entre une équation elliptique (pression-vitesse) et une équation de convection-diffusion-réaction (concentration). Le schéma VF considérée est de type "vertex centred" semi-implicite en temps : explicite pour la convection et implicite pour la diffusion. On utilise un schéma de Godunov pour approcher le terme convectif et une approximation élément fini P1 pour le terme de diffusion. Nous montrons des résultats de stabilité L≂ estimations BV et le principe du maximum discret sous une condition CFL appropriée. Ensuite, nous montrons la convergence de la solution approchée obtenue par le schéma combiné EFM-VF vers la solution du problème couplé. La démonstration de la convergence se fait en plusieurs étapes : premièrement, on déduit la convergence forte de la solution approchée de la concentration dans L2(Q), en utilisant la stabilité L≂, les estimations BV et des arguments de compacité. Dans l'étape suivante, on étudie le schéma découplé EFM, en donnant des résultats de convergence pour la pression et la vitesse. Enfin, le processus de convergence de la solution approchée du schéma combiné EFM-VF vers la solution exacte est obtenu par passage à la limite et par unicité de solution pour le problème continu. Des simulations numériques académiques et réalistes pour des problèmes bidimensionnels confirment la stabilité et l'efficacité du schéma combiné. Enfin, nous étudions des estimateurs d'erreur a posteriori de type résiduel pour une équation de convection-diffusion-réaction discrétisée par un schéma VF "vertex centred" semi-implicite en temps. Nous introduisons deux sortes d'indicateurs. Le premier est local en temps et en espace et constitue un outil efficace pour l'adaptation du maillage à chaque pas de temps. Le second est global en espace mais local en temps et peut être utilisé pour l'adaptation en temps. Nous montrons que l'estimateur est une borne supérieure de l'erreur. Des résultats numériques d'adaptations de maillage sont présentés et montrent l'efficacité de la méthode. La partie logiciels de ce travail porte sur deux volets. Le premier a permis de réaliser un code de calcul 2D, MFlow, écrit en C++, pour la résolution du système des écoulements miscibles considérés dans cette thèse. Le second volet concerne la collaboration avec un groupe de chercheurs pour l'élaboration de la plate-forme Homogenizer++ réalisée dans le cadre du GDR MoMaS (http://momas.univ-lyon1.fr/).