This dissertation aims to use theory and numerical simulations to quantify the propagation of fronts, which consist of autocatalytic reaction fronts, fronts with feedback and pattern forming fronts in Rayleigh-Bénard convection. The velocity and geometry of fronts are quantified for fronts traveling through straight parallel convection rolls, spatiotemporally chaotic rolls, and weakly turbulent rolls. The front velocity is found to be dependent on the competing influence of the orientation of the convection rolls and the geometry of the wrinkled front interface which is quantified as a fractal having a non-integer box-counting dimension. Front induced solutal and thermal feedback to the convective flow field is then studied by solving an exothermic autocatalytic reaction where the products and the reactants can vary in density. A single self-organized fluid roll propagating with the front is created by the solutal feedback while a pair of propagating counterrotating convection rolls are formed due to heat release from the reaction. Depending on the relative change in density induced by the solutal and thermal feedback, cooperative and antagonistic feedback scenarios are quantified. It is found that front induced feedback enhances the front velocity and reactive mixing length and induces spatiotemporal oscillations in the front and fluid dynamics. Using perturbation expansions, a transition in symmetry and scaling behavior of the front and fluid dynamics for larger values of feedback is studied. The front velocity, flow structure, front geometry and reactive mixing length scales for a range of solutal and thermal feedback are quantified. Lastly, pattern forming fronts of convection rolls are studied and the wavelength and velocity selected by the front near the onset of convective instability are investigated. This research was partially supported by DARPA Grant No. HR0011-16-2-0033. The numerical computations were done using the resources of the Advanced Research Computing center at Virginia Tech. / Doctor of Philosophy / Quantification of transport of reacting species in the presence of a flow field is important in many problems of engineering and science. A front is described as a moving interface between two different states of a system such as between the products and reactants in a chemical reaction. An example is a line of wildfire which separates burnt and fresh vegetation and propagates until all the fresh vegetation is consumed. In this dissertation the propagation of reacting fronts in the presence of convective flow fields of varying complexity is studied. It is found that the spatial variations in a convective flow field affects the burning and propagation of fronts by reorienting the geometry of the front interface. The velocity of the propagating fronts and its dependence on the spatial variation of the flow field is quantified. In certain scenarios the propagating front feeds back to the flow by inducing a local flow that interacts with the background convection. The rich and emergent dynamics resulting from this front induced feedback is quantified and it is found that feedback enhances the burning and propagation of fronts. Finally, the properties of pattern forming fronts are studied for fronts which leave a trail of spatial structures behind as they propagate for example in dendritic solidification and crystal growth. Pattern forming fronts of convection rolls are studied and the velocity of the front and spatial distribution of the patterns left behind by the front is quantified. This research was partially supported by DARPA Grant No. HR0011-16-2-0033. The numerical computations were done using the resources of the Advanced Research Computing center at Virginia Tech.

Several problems in physics and engineering are modeled by reaction-advection-diffusion (RAD) equations. However, when the diffusive terms are small compared with the other ones, these problems can become difficult to solve numerically. Besides, formulating the unsteady version of these models in a semi-discrete fashion, it can be interpreted that the overall diffusivity gets smaller as the time step decreases. To overcome these drawbacks, this thesis considers the development of Galerkin (or Petrov-Galerkin) finite element methods based on approximation spaces enriched by residual-free bubbles (RFB) or multiscale functions. Beginning with the unsteady reaction-diffusion problem, new methods using multiscale functions are presented which improve the solutions in the reaction-dominated regime and/or when small time steps are adopted. They also give rise to a general concept of stabilizing unsteady problems differently along the time. In the following, it is shown that switching RFB by suitable multiscale functions in the elements connected to the outflow boundaries of the domain increases the accuracy of the solutions in this region for RAD problems with advection. Next, this methodology is further studied for systems of RAD equations. In a final contribution, an extension of the RFB method is introduced for the shallow waters equations. All these methods are tested through benchmark problems and compared with stabilized methods presenting stable and accurate results. / A modelagem de vários problemas físicos e de engenharia envolve a solução de problemas de transporte do tipo reação-advecção-difusão (RAD), porém, estes podem tornar-se singularmente perturbados quando os termos difusivos são pequenos comparados aos demais. Além disso, ao adotar formulações semi-discretas em problemas transientes, observa-se que diminuir o passo de tempo tem um efeito de redução da componente difusiva. Para superar estas dificuldades, esta tese considera o desenvolvimento de métodos de elementos finitos de Galerkin (ou Petrov-Galerkin) baseados em espaços de aproximação enriquecidos por funções bolhas livres do resíduo (RFB) ou funções multiescala. Começando pelo problema de reação-difusão transiente, novos métodos utilizando funções multiescala são apresentados, os quais melhoram as soluções no regime reativo-dominante e/ou quando pequenos passos de tempo são adotados. Com estes métodos, discute-se também o conceito de estabilização variável ao longo do tempo para problemas transientes. Na seqüência, verifica-se que utilizar funções multiescala nos elementos conectados às fronteiras de saída de fluxo do domínio e RFB nos demais elementos aumenta a precisão das soluções nesta região em problemas de RAD com advecção dominante. A seguir, esta metodologia é estudada para sistemas de RAD. Como contribuição final, estende-se o método RFB para o modelo de águas rasas. Todos estes métodos são submetidos a testes de robustez e comparados com métodos estabilizados, apresentando resultados estáveis e precisos.

Made available in DSpace on 2015-03-04T18:50:39Z (GMT). No. of bitstreams: 1 Apresentacao.pdf: 200775 bytes, checksum: 317576b779951158daadb5222c59a464 (MD5) Previous issue date: 2005-12-20 / Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior / Several problems in physics and engineering are modeled by reaction-advection-diffusion (RAD) equations. However, when the diffusive terms are small compared with the other ones, these problems can become difficult to solve numerically. Besides, formulating the unsteady version of these models in a semi-discrete fashion, it can be interpreted that the overall diffusivity gets smaller as the time step decreases. To overcome these drawbacks, this thesis considers the development of Galerkin (or Petrov-Galerkin) finite element methods based on approximation spaces enriched by residual-free bubbles (RFB) or multiscale functions. Beginning with the unsteady reaction-diffusion problem, new methods using multiscale functions are presented which improve the solutions in the reaction-dominated regime and/or when small time steps are adopted. They also give rise to a general concept of stabilizing unsteady problems differently along the time. In the following, it is shown that switching RFB by suitable multiscale functions in the elements connected to the outflow boundaries of the domain increases the accuracy of the solutions in this region for RAD problems with advection. Next, this methodology is further studied for systems of RAD equations. In a final contribution, an extension of the RFB method is introduced for the shallow waters equations. All these methods are tested through benchmark problems and compared with stabilized methods presenting stable and accurate results. / A modelagem de vários problemas físicos e de engenharia envolve a solução de problemas de transporte do tipo reação-advecção-difusão (RAD), porém, estes podem tornar-se singularmente perturbados quando os termos difusivos são pequenos comparados aos demais. Além disso, ao adotar formulações semi-discretas em problemas transientes, observa-se que diminuir o passo de tempo tem um efeito de redução da componente difusiva. Para superar estas dificuldades, esta tese considera o desenvolvimento de métodos de elementos finitos de Galerkin (ou Petrov-Galerkin) baseados em espaços de aproximação enriquecidos por funções bolhas livres do resíduo (RFB) ou funções multiescala. Começando pelo problema de reação-difusão transiente, novos métodos utilizando funções multiescala são apresentados, os quais melhoram as soluções no regime reativo-dominante e/ou quando pequenos passos de tempo são adotados. Com estes métodos, discute-se também o conceito de estabilização variável ao longo do tempo para problemas transientes. Na seqüência, verifica-se que utilizar funções multiescala nos elementos conectados às fronteiras de saída de fluxo do domínio e RFB nos demais elementos aumenta a precisão das soluções nesta região em problemas de RAD com advecção dominante. A seguir, esta metodologia é estudada para sistemas de RAD. Como contribuição final, estende-se o método RFB para o modelo de águas rasas. Todos estes métodos são submetidos a testes de robustez e comparados com métodos estabilizados, apresentando resultados estáveis e precisos.

Les besoins grandissants de prévisions robustes pour des systèmes complexes conduisent à introduire des modèles mathématiques considérant un nombre croissant de paramètres. Au temps s'ajoutent l'espace, l'aléa, les échelles de dynamiques, donnant lieu à des modèles stochastiques multi-échelles avec dépendance spatiale (modèles spatiaux). Cependant, l'explosion du temps de simulation de tels modèles complique leur utilisation. Leur analyse difficile a néanmoins permis, pour les modèles à une échelle, de développer des outils puissants: loi des grands nombres (LGN), théorème central limite (TCL), ..., puis d'en dériver des modèles simplifiés et algorithmes accélérés. Dans le processus de dérivation, des modèles et algorithmes dits hybrides ont vu le jour dans le cas multi-échelle, mais sans analyse rigoureuse préalable, soulevant ainsi la question d'approximation hybride dont la consistance constitue l'une des motivations principales de cette thèse.En 2012, Crudu, Debussche, Muller et Radulescu établissent des critères d'approximation hybride pour des modèles homogènes en espace de réseaux de régulation de gènes. Le but de cette thèse est de compléter leur travail et le généraliser à un cadre spatial.Nous avons développé et simplifié différents modèles, tous des processus de Markov de sauts pures à temps continu. La démarche met en avant, d'une part, des conditions d'approximations déterministes par des solutions d'équations d'évolution (type réaction-advection-diffusion), et, d'autre part, des conditions d'approximations hybrides par des processus stochastiques hybrides. Dans le cadre des réseaux de réactions biochimiques, un TCL est établi. Il correspond à une approximation hybride d'un modèle homogène simplifié à deux échelles de temps (suivant Crudu et al.). Puis, une LGN est obtenue pour un modèle spatial à deux échelles de temps. Ensuite, une approximation hybride est établie pour un modèle spatial à deux échelles de dynamique en temps et en espace. Enfin, des comportements asymptotiques en grandes populations et en temps long sont présentés pour un modèle d'épidémie de choléra, via une LGN suivie d'une borne supérieure pour les sous-ensembles compacts, dans le cadre d'un principe de grande déviation (PGD) correspondant.À l'avenir, il serait intéressant, entre autres, de varier la géométrie spatiale, de généraliser le TCL, de compléter les estimations du PGD, et d'explorer des systèmes complexes issus d'autres domaines. / The growing needs of precise predictions for complex systems lead to introducing stronger mathematical models, taking into account an increasing number of parameters added to time: space, stochasticity, scales of dynamics. Combining these parameters gives rise to spatial --or spatially inhomogeneous-- multiscale stochastic models. However, such models are difficult to study and their simulation is extremely time consuming, making their use not easy. Still, their analysis has allowed one to develop powerful tools for one scale models, among which are the law of large numbers (LLN) and the central limit theorem (CLT), and, afterward, to derive simpler models and accelrated algorithms. In that deduction process, the so-called hybrid models and algorithms have arisen in the multiscale case, but without any prior rigorous analysis. The question of hybrid approximation then shows up, and its consistency is a particularly important motivation of this PhD thesis.In 2012, criteria for hybrid approximations of some homogeneous regulation gene network models were established by Crudu, Debussche, Muller and Radulescu. The aim of this PhD thesis is to complete their work and generalize it afterward to a spatial framework.We have developed and simplified different models. They all are time continuous pure jump Markov processes. The approach points out the conditions allowing on the the one hand deterministic approximations by solutions of evolution equations of type reaction-advection-diffusion, and, on the other hand, hybrid approximations by hybrid stochastic processes. In the field of biochemical reaction networks, we establish a CLT. It corresponds to a hybrid approximation of a simplified homogeneous model (due to Crudu et al.). Then a LLN is obtained for a spatial model with two time scales. Afterward, a hybrid approximation is established, for a two time-space scales spatial model. Finally, the asymptotic behaviour in large population and long time are respectively presented for a model of cholera epidemic, through a LLN followed by the upper bound for compact sets, in the context of a corresponding large deviation principle (LDP).Interesting future works would be, among others, to study other spatial geometries, to generalize the CLT, to complete the LDP estimates, and to study complex systems from other fields.