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In the field of exoplanetary sciences, high contrast imaging is crucial for the direct detection of, and answering questions about habitability of exoplanets. For the direct imaging of habitable exoplanets, it is important to employ low inner working angle (IWA) coronagraphs, which can image exoplanets close to the PSF. To achieve the full performance of such coronagraphs, it is crucial to correct for atmospheric dispersion to the highest degree, as any leakage will limit the contrast. To achieve the highest contrast with the state-of-the-art coronagraphs in the SCExAO instrument, the spread in the point-spread function due to residual atmospheric dispersion should not be more than 1 mas in the science band. In a traditional approach, atmospheric dispersion is compensated by an atmospheric dispersion compensator (ADC), which is simply based on model which only takes into account the elevation of telescope and hence results in imperfect correction of dispersion. In this paper we present the first on-sky closed-loop measurement and correction of residual atmospheric dispersion. Exploiting the elongated nature of chromatic speckles, we can precisely measure the presence of atmospheric dispersion and by driving the ADC, we can do real-time correction. With the above approach, in broadband operation (y-H band) we achieved a residual of 4.2 mas from an initial 18.8 mas and as low as 1.4 mas in H-band only after correction, which is close to our science requirement. This work will be valuable in the field of high contrast imaging of habitable exoplanets in the era of the ELTs.

Profiles of height-dependent diffusion which accommodate site-specific diffusivities were produced. A numerical model was adapted to incorporate the profiles. The model represented three-dimensional steady-state advection and diffusion of aerosols from an elevated point source. Sorption effects were simulated with surface attachment coefficients greater than unity. This proved effective in depleting the plume differentially upward from the surface.

In the event of a hazardous release (chemical, biological, or radiological) in an urban environment, monitoring agencies must have the tools to locate and characterize the source of the emission in order to respond and minimize damage. Given a finite and noisy set of concentration measurements, determining the source location, strength and time of release is an ill-posed inverse problem. We treat this problem using Bayesian inference, a framework under which uncertainties in modelled and measured concentrations can be propagated, in a consistent, rigorous manner, toward a final probabilistic estimate for the source. The Bayesian methodology operates independently of the chosen dispersion model, meaning it can be applied equally well to problems in urban environments, at regional scales, or at global scales. Both Lagrangian stochastic (particle-tracking) and Eulerian (fixed-grid, finite-volume) dispersion models have been used successfully. Calculations are accomplished efficiently by using adjoint (backward) dispersion models, which reduces the computational effort required from calculating one [forward] plume per possible source configuration to calculating one [backward] plume per detector. Markov chain Monte Carlo (MCMC) is used to efficiently sample from the posterior distribution for the source parameters; both the Metropolis-Hastings and hybrid Hamiltonian algorithms are used. In this thesis, four applications falling under the rubric of source determination are addressed: dispersion in highly disturbed flow fields characteristic of built-up (urban) environments; dispersion of a nonconservative scalar over flat terrain in a statistically stationary and horizontally homogeneous (turbulent) wind field; optimal placement of an auxiliary detector using a decision-theoretic approach; and source apportionment of particulate matter (PM) using a chemical mass balance (CMB) receptor model. For the first application, the data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. For the second and third applications, the background wind and terrain conditions are based on those encountered during the Project Prairie Grass field experiment; mean concentration and turbulent scalar flux data are synthesized using a Lagrangian stochastic model where necessary. In the fourth and final application, Bayesian source apportionment results are compared to the US Environmental Protection Agency's standard CMB model using a test case involving PM data from Fresno, California. For each of the applications addressed in this thesis, combining Bayesian inference with appropriate computational techniques results in a computationally efficient methodology for performing source determination.

In the event of a hazardous release (chemical, biological, or radiological) in an urban environment, monitoring agencies must have the tools to locate and characterize the source of the emission in order to respond and minimize damage. Given a finite and noisy set of concentration measurements, determining the source location, strength and time of release is an ill-posed inverse problem. We treat this problem using Bayesian inference, a framework under which uncertainties in modelled and measured concentrations can be propagated, in a consistent, rigorous manner, toward a final probabilistic estimate for the source. The Bayesian methodology operates independently of the chosen dispersion model, meaning it can be applied equally well to problems in urban environments, at regional scales, or at global scales. Both Lagrangian stochastic (particle-tracking) and Eulerian (fixed-grid, finite-volume) dispersion models have been used successfully. Calculations are accomplished efficiently by using adjoint (backward) dispersion models, which reduces the computational effort required from calculating one [forward] plume per possible source configuration to calculating one [backward] plume per detector. Markov chain Monte Carlo (MCMC) is used to efficiently sample from the posterior distribution for the source parameters; both the Metropolis-Hastings and hybrid Hamiltonian algorithms are used. In this thesis, four applications falling under the rubric of source determination are addressed: dispersion in highly disturbed flow fields characteristic of built-up (urban) environments; dispersion of a nonconservative scalar over flat terrain in a statistically stationary and horizontally homogeneous (turbulent) wind field; optimal placement of an auxiliary detector using a decision-theoretic approach; and source apportionment of particulate matter (PM) using a chemical mass balance (CMB) receptor model. For the first application, the data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. For the second and third applications, the background wind and terrain conditions are based on those encountered during the Project Prairie Grass field experiment; mean concentration and turbulent scalar flux data are synthesized using a Lagrangian stochastic model where necessary. In the fourth and final application, Bayesian source apportionment results are compared to the US Environmental Protection Agency's standard CMB model using a test case involving PM data from Fresno, California. For each of the applications addressed in this thesis, combining Bayesian inference with appropriate computational techniques results in a computationally efficient methodology for performing source determination.

De nombreuses applications pratiques ou industrielles, telles que l’étude de la dispersion atmosphérique de polluants, la qualité de l’air, la micro-météorologie en terrain complexe et l’évaluation du potentiel éolien, nécessitent la prédiction précise de l’écoulement atmosphérique à une échelle dite locale (environ 10 km horizontalement). Le travail de recherche dans le cadre de cette thèse s’inscrit donc dans la proposition d’une chaine de méthodologies et de modélisations permettant de simuler l’écoulement atmosphérique à cette échelle, avec une résolution spatiale horizontale hectométrique. Tout d’abord, nous nous sommes intéressés à la modélisation de la turbulence dans la couche limite atmosphérique (CLA). Pour cela, nous avons choisi le modèle RANS k− ε (déjà largement utilisé dans la littérature), ainsi que le modèle RANS Ri j − ε afin de simuler l’anisotropie de la turbulence. Nous avons ainsi pu vérifier la nécessité d’utiliser les constantes de Duynkerke (1988) pour l’atteinte des niveaux de turbulence atmosphérique avec le modèle k− ε. Dans cette optique, nous avons également développé un nouveau jeu de constantes atmosphériques pour le modèle Ri j − ε. Finalement, nous avons proposé un modèle théorique capable de reproduire les caractéristiques turbulentes de l’écoulement pour n’importe quel temps d’intégration, permettant ainsi de trouver une continuité entre les constantes « standards » et les constantes « atmosphériques » des modèles de turbulence. D’autre part, nous avons développé l’approche de modélisation « CFD 1D-3D », qui consiste en l’utilisation d’un modèle CFD 1D afin de fournir les profils verticaux nécessaires pour forcer le code CFD 3D en données météorologiques (utilisé en topographie complexe). Le modèle 1D a été développé au cours de cette thèse avec les modèles de turbulence k− ε et Ri j− ε. Il a été validé grâce à une comparaison avec des résultats empiriques et théoriques issus de la littérature. Cette comparaison a montré des résultats très encourageants de ce modèle dans la simulation de la CLA en sol plat. De plus, la méthodologie « CFD 1D-3D » a été évaluée grâce à une comparaison avec des mesures en soufflerie en présence d’un relief complexe : les résultats sont globalement très satisfaisants. Ces comparaisons ont permis enfin de valider le nouveau jeu de constantes pour le modèle Ri j− ε. Finalement, nous nous sommes intéressés à l’utilisation de calculs CFD partiellement convergés comme moyen de réduction du temps CPU des codes CFD, dans des contextes d’utilisation opérationnelle. Dans cette optique, nous avons montré que l’on arrive à une solution dont l’erreur est faible par rapport à la solution convergée (< 10% d’erreur), avec un temps CPU de l’ordre de 5%−10% du temps nécessaire pour atteindre la convergence. C’est un résultat très intéressant car il permet de réduire considérablement le temps de calcul, tout en gardant une erreur faible devant l’incertitude générale de l’approche CFD. / Many practical and industrial applications, such as the study of atmospheric dispersion of pollutants, air quality,micro-meteorology in complex terrain and wind assessment, require accurate prediction of the atmospheric flow at a so-called local scale (approximately 10 km horizontally). Therefore, the main objective in this thesis is to propose a chain of methodologies capable of simulating the atmospheric flow at this scale, with a horizontal hectometric spatial resolution. First of all, we were interested in modeling of turbulence in the atmospheric boundary layer (ABL). In addition to the largely used RANS k−ε model, we considered the use of the RANS Ri j− ε model as a way of simulating turbulence anisotropy.We were able to verify the necessity of using the Duynkerke (1988) constants in order to achieve atmospheric levels of turbulence with the k− ε model. In a similar way, we also developed a new set of atmospheric constants for the Ri j− ε model. Finally, we proposed a theoretical model capable of reproducing the main characteristics of a turbulent flow for any given sampling duration, thus allowing a more continuous approach between « standard » and « atmospheric » constants for turbulence models. Also, in this thesis, we developed the « CFD 1D-3D » modeling approach. It is based on the use of a 1D CFD model as a way of providing vertical profiles of meteorological data for boundary conditions of a 3D CFD code, used in complex terrain. This 1D model was developed as a part of the thesis, along with k− ε and Ri j − ε turbulence models. It was validated by being compared with empirical and theoretical results. The comparisons showed very encouraging results concerning the ability of this model in simulating ABL in the presence of a flat terrain. In addition, the « CFD 1D- 3D » methodology was assessed by comparison with wind tunnel measurements in the presence of complex terrain, which showed very satisfactory resultst. These comparisons also validated the newly developed set of constants for the Ri j− ε model. Finally, we studied the use of partially converged CFD as a way of reducing the CPU time of CFD simulations for operational purposes. We therefore demonstrated that we can achieve a low error solution (< 10% error compared with the converged solution), with a CPU time of about 5%−10% of the time required to achieve convergence. This result was very interesting because the methodology significantly reduces the computational time while maintaining a low error as compared to the overall uncertainty of the CFD approach.

[pt] O objetivo principal do presente trabalho foi investigar computacionalmente o escoamento e a dispersão de poluentes atmosféricos sobre topografias complexas tridimensionais em escala de laboratório. Foram realizadas simulações numéricas de escoamentos neutros e estavelmente estratificados sobre colinas e também sobre terreno plano. A modelagem matemática, baseada na solução das equações gerais de conservação, inclui o modelo de tensões de Reynolds para a turbulência e um modelo de duas camadas para o tratamento do escoamento na região próxima à parede. O código comercial Fluent (Versão 6.0.12), que emprega o método de volumes finitos, foi utilizado nas simulações computacionais. Os resultados numéricos foram comparados a dados obtidos em experimentos em túnel de vento disponíveis na literatura. Também foram realizadas comparações com resultados obtidos com a utilização do modelo (k menos épsilon) clássico. A comparação entre os resultados obtidos com as diversas modelagens numéricas e os dados experimentais mostrou que a utilização conjunta do modelo de tensões de Reynolds e do tratamento em duas camadas produziu os melhores resultados na predição do escoamento. O desempenho dessa modelagem foi particularmente superior na representação da recirculação no escoamento na região a jusante da colina. Com relação ao cálculo das concentrações, os resultados obtidos foram razoáveis nas regiões mais distantes da fonte quando comparados aos experimentais. Na região mais próxima à fonte emissora, foram calculadas concentrações excessivamente altas ao nível do solo. Estas discrepâncias foram atribuídas ao fato de ter-se utilizado um modelo de difusividade turbulenta isotrópica para os cálculos da dispersão turbulenta do poluente. Ainda assim, os campos de concentrações apresentados mostraram importantes aspectos qualitativos relativos ao problema como, por exemplo, os efeitos da estabilidade atmosférica na dispersão do poluente, que foram adequadamente previstos. / [en] The main objective of the present work was to investigate computationally the flow and the dispersion of atmospheric pollutants over three dimensional complex topographies in laboratory scale. The investigations included the numerical simulation on the neutral and stably stratified flows over hills and flat terrain. The mathematical model was based on the solution of the general conservation equations and included the Reynolds stress model for turbulence and a two layer zonal model for the flow treatment in the near wall region. The commercial code Fluent (Version 6.0.12), which is based on the finite volume method, was employed in the computational simulations. The numerical results were compared to data obtained in wind tunnel experiments, available in the literature. Comparisons were also made with results obtained by employing the standard (k less épsilon) model for turbulence. The comparisons between the experimental data and the numerical results showed that the combined use of the Reynolds stress model and the two layer treatment provided the best results for the flow representation. This modeling approach was particularly superior in representing the flow recirculation on the leeside of the hill. The predicted concentrations results were reasonably good at regions far away from the emission source. In the near source regions, the ground level concentrations were overestimated by the numerical modeling. These discrepancies were attributed to the employment of an isotropic turbulent diffusivity model in the turbulent dispersion calculations. Nevertheless, the calculated concentration fields represented well important qualitative features of PUC Rio.

Les circonstances pouvant conduire à un rejet incontrôlé de polluants dans l'atmosphère sont variées : il peut s'agir de situations accidentelles, par exemples des fuites ou explosions sur un site industriel, ou encore de menaces terroristes : bombe sale, bombe biologique, notamment en milieu urbain. Face à de telles situations, les objectifs des autorités sont multiples : prévoir les zones impactées à court terme, notamment pour évacuer les populations concernées ; localiser la source pour pouvoir intervenir directement sur celle-ci ; enfin déterminer les zones polluées à plus long terme, par exemple par le dépôt de polluants persistants, et soumises à restriction de résidence ou d'utilisation agricole. Pour atteindre ces objectifs, des modèles numériques peuvent être utilisés pour modéliser la dispersion atmosphérique des polluants. Après avoir rappelé les processus physiques qui régissent le transport de polluants dans l'atmosphère, nous présenterons les différents modèles à disposition. Le choix de l'un ou l'autre de ces modèles dépend de l'échelle d'étude et du niveau de détails (topographiques notamment) désiré. Nous présentons ensuite le cadre général (bayésien) de la modélisation inverse pour l'estimation de sources. Le principe est l'équilibre entre des informations a priori et des nouvelles informations apportées par des observations et le modèle numérique. Nous mettons en évidence la forte dépendance de l'estimation du terme source et de son incertitude aux hypothèses réalisées sur les statistiques des erreurs a priori. Pour cette raison nous proposons plusieurs méthodes pour estimer rigoureusement ces statistiques. Ces méthodes sont appliquées sur des exemples concrets : tout d'abord un algorithme semi-automatique est proposé pour la surveillance opérationnelle d'un parc de centrales nucléaires. Un second cas d'étude est la reconstruction des termes sources de césium-137 et d'iode-131 consécutifs à l'accident de la centrale nucléaire de Fukushima Daiichi. En ce qui concerne la localisation d'une source inconnue, deux stratégies sont envisageables : les méthodes dites paramétriques et les méthodes non-paramétriques. Les méthodes paramétriques s'appuient sur le caractère particulier des situations accidentelles dans lesquelles les émissions de polluants sont généralement d'étendue limitée. La source à reconstruire est alors paramétrisée et le problème inverse consiste à estimer ces paramètres, en nombre réduit. Dans les méthodes non-paramétriques, aucune hypothèse sur la nature de la source (ponctuelle, localisée, ...) n'est réalisée et le système cherche à reconstruire un champs d'émission complet (en 4 dimensions). Plusieurs méthodes sont proposées et testées sur des situations réelles à l'échelle urbaine avec prise en compte des bâtiments, pour lesquelles les méthodes que nous proposons parviennent à localiser la source à quelques mètres près, suivant les situations modélisées et les méthodes inverses utilisées / Uncontrolled releases of pollutant in the atmosphere may be the consequence of various situations : accidents, for instance leaks or explosions in an industrial plant, or terrorist attacks such as biological bombs, especially in urban areas. In the event of such situations, authorities' objectives are various : predict the contaminated zones to apply first countermeasures such as evacuation of concerned population ; determine the source location ; assess the long-term polluted areas, for instance by deposition of persistent pollutants in the soil. To achieve these objectives, numerical models can be used to model the atmospheric dispersion of pollutants. We will first present the different processes that govern the transport of pollutants in the atmosphere, then the different numerical models that are commonly used in this context. The choice between these models mainly depends of the scale and the details one seeks to take into account.We will then present the general framework of inverse modeling for the estimation of source. Inverse modeling techniques make an objective balance between prior information and new information contained in the observation and the model. We will show the strong dependency of the source term estimation and its uncertainty towards the assumptions made on the statistics of the prior errors in the system. We propose several methods to estimate rigorously these statistics. We will apply these methods on different cases, using either synthetic or real data : first, a semi-automatic algorithm is proposed for the operational monitoring of nuclear facilities. The second and third studies concern the source term estimation of the accidental releases from the Fukushima Daiichi nuclear power plant. Concerning the localization of an unknown source of pollutant, two strategies can be considered. On one hand parametric methods use a limited number of parameters to characterize the source term to be reconstructed. To do so, strong assumptions are made on the nature of the source. The inverse problem is hence to estimate these parameters. On the other hand non-parametric methods attempt to reconstruct a full emission field. Several parametric and non-parametric methods are proposed and evaluated on real situations at a urban scale, with a CFD model taking into account buildings influence on the air flow. In these experiments, some proposed methods are able to localize the source with a mean error of some meters, depending on the simulated situations and the inverse modeling methods