Fractional calculus has been introduced as an e cient tool for modeling physical phenomena, thanks to its memory and hereditary properties. For example, fractional models have been successfully used to describe anomalous di↵usion processes such as contaminant transport in soil, oil flow in porous media, and groundwater flow. These models capture important features of particle transport such as particles with velocity variations and long-rest periods. Mathematical modeling of physical phenomena requires the identification of pa- rameters and variables from available measurements. This is referred to as an inverse problem. In this work, we are interested in studying theoretically and numerically inverse problems for space Fractional Advection-Dispersion Equation (FADE), which is used to model solute transport in porous media. Identifying parameters for such an equa- tion is important to understand how chemical or biological contaminants are trans- ported throughout surface aquifer systems. For instance, an estimate of the di↵eren- tiation order in groundwater contaminant transport model can provide information about soil properties, such as the heterogeneity of the medium. Our main contribution is to propose a novel e cient algorithm based on modulat-ing functions to estimate the coe cients and the di↵erentiation order for space FADE, which can be extended to general fractional Partial Di↵erential Equation (PDE). We also show how the method can be applied to the source inverse problem. This work is divided into two parts: In part I, the proposed method is described and studied through an extensive numerical analysis. The local convergence of the proposed two-stage algorithm is proven for 1D space FADE. The properties of this method are studied along with its limitations. Then, the algorithm is generalized to the 2D FADE. In part II, we analyze direct and inverse source problems for a space FADE. The problem consists of recovering the source term using final observations. An analytic solution for the non-homogeneous case is derived and existence and uniqueness of the solution are established. In addition, the uniqueness and stability of the inverse problem is studied. Moreover, the modulating functions-based method is used to solve the problem and it is compared to a standard Tikhono-based optimization technique.

This work determines a suitable value for a dispersion coefficient to be used in the One-Dimensional Advection-Dispersion equation to model dispersion within sewer collection systems. Dispersion coefficients for sewer systems have only recently begun to be studied, and there is not yet an established value that is commonly accepted. The work described in this paper aimed, through observational study, to find a suitable value to be used. Salt tracers were placed in two separate reaches of sewer line. The first line studied was a straight, linear reach of sewer that included three manholes. The tracer was placed in the first manhole and the conductivity was measured at the two consecutive manholes downstream. These measurements were compared to a model developed using the 1D Advection-Dispersion Equation. The flow information and sewer network geometry was used in the model and the dispersion coefficient was adjusted to find a best fit. It was found that a value of 0.18 m2/s for the dispersion coefficient provided the best statistical match. The next reach of sewer that was studied was a reach with a 90 degree angle. This section was chosen to observe the effect that mixing has on dispersion, because of the change in direction of flow. The same procedure was applied, and an optimal dispersion coefficient of 0.22 m2/s was found. These values represent optimal dispersion coefficients under a specific set of conditions. It should not be assumed that they will provide accurate results in all circumstances, but are rather a base point for average flows under dry, stable conditions. Using these values inferences can begin to be made about dispersion characteristics throughout the entire sewer network. This can lead to specific engineering applications, and well as applications in other fields of study.

Missing page 91 / <p> This report presents a numerical method which can be used to solve the advection equation </p> <p> (∂ɸ/∂t) + (∂[u(x,t)ɸ]/∂x) = S(x,t) </p> where: </p> <p> ɸ ≣ concentration field </p> <p> u(x,t) ≣ velocity field </p> <p> S(x,t) ≣ source term </p> <p> Central to this method are the concept of particle path and the Eulerian interpretation of the time rate of change of the concentration field ɸ. </p> In actual comparison tests for particular cases with known solutions this method proved to be at least two orders of magnitude more accurate than the usual one sided upwind finite difference method. </p> / Thesis / Master of Engineering (ME)

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In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is di cult as these functions should yield positive de nite covariance matrices. In recent years, we have seen a ourishing of methods and theories on constructing spatiotemporal covariance functions satisfying the positive de niteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions ful lls this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In the three main chapters that comprise this dissertation, several developments are proposed and new features are provided to this special class. First, the application of the Lagrangian reference frame on transported purely spatial random elds with second-order nonstationarity is explored, an appropriate estimation methodology is proposed, and the consequences of model misspeci cation is tackled. Furthermore, the new Lagrangian models and the new estimation technique are used to analyze particulate matter concentrations over Saudi Arabia. Second, a multivariate version of the Lagrangian framework is established, catering to both secondorder stationary and nonstationary spatio-temporal random elds. The capabilities of the Lagrangian spatio-temporal cross-covariance functions are demonstrated on a bivariate reanalysis climate model output dataset previously analyzed using purely spatial covariance functions. Lastly, the class of Lagrangian spatio-temporal cross-covariance functions with multiple transport behaviors is presented, its properties are explored, and its use is demonstrated on a bivariate pollutant dataset of particulate matter in Saudi Arabia. Moreover, the importance of accounting for multiple transport behaviors is discussed and validated via numerical experiments. Together, these three extensions to the Lagrangian framework makes it a more viable geostatistical approach in modeling realistic transport scenarios.

Creating chaotic advection is the most efficient strategy to achieve mixing in a microscale or in a very viscous fluid, and it has many important applications in microfluidic devices, material processing and so on. In this paper, we present a quantitative long-term theory of resonant mixing in 3-D near-integrable flows. We use the flow in the annulus between two coaxial elliptic counter-rotating cylinders as a demonstrative model. We illustrate that such resonance phenomena as resonance and separatrix crossings accelerate mixing by causing the jumps of adiabatic invariants. We calculate the width of the mixing domain and estimate a characteristic time of mixing. We show that the resulting mixing can be described in terms of a single diffusion-type equation with a diffusion coefficient depending on the averaged effect of multiple passages through resonances. We discuss what must be done to accommodate the effects of the boundaries of the chaotic domain. / Mechanical Engineering

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.