Many complexities arise when writing software for computational physics. The choice of underlying data structures, physics model representation, and numerical methods used for the solver all add to the overall complexity of a code and significantly affect the simulation speed and accuracy of the solution. This work has integrated multiple recently developed software tools into a unified framework called ELEMENTS. ELEMENTS contains tools to address the complexities of data representation and numerical methods implementation for computational physics applications. ELEMENTS consists of multiple software packages: Elements, MATAR, Swage, Geometry, and SLAM. MATAR is a performance portability and productivity implementation of data-oriented design that leverages KOKKOS for multi-architecture portability. MATAR's data-oriented design allows for highly efficient memory use through the use of contiguous memory allocation and access for optimal performance. The elements library contains the requisite mathematical functions for a wide range of numerical methods and high order field representation, including the Serendipity basis set that allows for a higher-order solution with fewer degrees of freedom than the more standard tensor product elements. Swage is a novel mesh class capable of representing all of the geometric entities required to implement low and high-order continuous and discontinuous Galerkin methods on unstructured hexahedral meshes as well as connectivity structures between the disparate index spaces. SLAM is a library for linear algebra solvers and tools for linking to external solver packages. Combining these tools allows for the research and development of novel methods for solving problems in computational physics. This work discusses the ELEMENTS package and reviews multiple numerical methods built using ELEMENTS.

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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.

The purpose of this work aims to develop and explore a nonlinear multiperiod petroleum refinery model based on a real-world model. Due to the inherent complexity and interconnected nature of petroleum refineries, various studies are implemented to describe the multiperiod model. The model is based around maximizing the profit of a petroleum refinery, starting from the crude inputs through the crude distillation unit, to the intermediate product processing through various unit operations, and finally to the blending of the final products. The model begins as a single period model, and is re-formulated as a multiperiod model by incorporating intermediate product tanks and dividing the model into partitions. In solving the multiperiod model, the termination criteria for convergence was varied in order to investigate the effect on the solution; it was found that it is acceptable to terminate at a relaxed tolerance due to minimal differences in solution. Several case studies, defined as deviations from normal operation, are implemented in order to draw comparisons between the real-world model and the model studied in this thesis. The thesis model, solved by CONOPT and IPOPT, resulted in significant gains over the real-world model. Next, a Lagrangean decomposition scheme was implemented in an attempt to decrease computation times. The decomposition was unable to find feasible solutions for the subproblems, as the nonlinear and nonconvex nature of the problem posed difficulty in finding feasibilities. However, in the case of a failed decomposition, the point where the decomposition ends may be used as an initial guess to solve the full space problem, regardless of feasibility of the subproblems. It was found that running the decomposition fewer times provided better initial guesses due to lower constraint violations from the infeasibilities, and then combined with the shorter decomposition time resulted in faster computation times. / Thesis / Master of Applied Science (MASc) / Petroleum refineries consist of complex units that serve a certain purpose, such as separating components of a mixed stream or blending intermediate products, in order to create final commercial products, e.g. gasoline and diesel. Due to the complexity and interconnectivity in a refinery, it is difficult to determine the optimal mode of operation. Thus, by formulating the refinery in mathematical form, optimization techniques may be used to find optimal operation. Furthermore, optimization problems can be formulated in a multiperiod fashion, where the problem is repeated over a set time horizon in partitions. The advantage is a higher detail in the operation of the refinery but this comes at a cost of higher computation time. In this work, a multiperiod refinery is formulated and studied by exploring model size, computation times, comparison of solvers, and solution strategies such as decomposition.

Yes / A novel Euler-Lagrangian (EL) computational uid dynamics (CFD) nite volume-based model to simulate the gas mixing of sludge for anaerobic digestion is developed and described. Fluid motion is driven by momentum transfer from bubbles to liquid. Model validation is undertaken by assessing the ow eld in a labscale model with particle image velocimetry (PIV). Conclusions are drawn about the upscaling and applicability of the model to full-scale problems, and recommendations are given for optimum application.

Yes / For the first time, an Euler-Lagrange model for Computational Fluid Dynamics (CFD) is used to model a full-scale gas-mixed anaerobic digester. The design and operation parameters of a digester from a wastewater treatment works are modelled, and mixing is assessed through a novel, multi-facetted approach consisting of the simultaneous analysis of (i) velocity, shear rate and viscosity flow patterns, (ii) domain characterization following the average shear rate value, and (iii) concentration of a non-diffusive scalar tracer. The influence of sludge’s non-Newtonian behaviour on flow patterns and its consequential impact on mixing quality were discussed for the first time. Recommendations to enhance mixing effectiveness are given: (i) a lower gas mixing input power can be used in the digester modelled within this work without a significant change in mixing quality, and (ii) biogas injection should be periodically switched between different nozzle series placed at different distances from the centre. / The first author is funded via a University of Birmingham Postgraduate Teaching Assistantship award.

As data-driven modeling becomes more prevalent for representing the uncertain dynamical systems, concerns also arise regarding the reliability of these methods. Recent developments in approximation theory provide a new perspective to studying these problems. This dissertation analyzes the convergence of two kernel-based, data-driven modeling methods, the reproducing kernel Hilbert space (RKHS) embedding method and the empirical-analytical Lagrangian (EAL) model. RKHS embedding is a non-parametric extension of the classical adaptive estimation method that embeds the uncertain function in an RKHS, an infinite-dimensional function space. As a result the original uncertain system of ordinary differential equations are understood as components of a distributed parameter system. Similarly to the classical approach for adaptive estimation, a novel definition of persistent excitation (PE) is introduced, which is proven to guarantee the pointwise convergence of the estimate of function over the PE domain. The finite-dimensional approximation of the RKHS embedding method is based on approximant spaces that consist of kernel basis functions centered at samples in the state space. This dissertation shows that explicit rate of convergence of the RKHS embedding method can be derived by choosing specific types of native spaces. In particular, when the RKHS is continuously embedded in a Sobolev space, the approximation error is proven to decrease at a rate determined by the fill distance of the samples in the PE domain. This dissertation initially studies scalar-valued RKHS, and subsequently the RKHS embedding method is extended for the estimation of vector-valued uncertain functions. Like the scalar-valued case, the formulation of vector-valued RKHS embedding is proven to be well-posed. The notion of partial PE is also generalized, and it is shown that the rate of convergence derived for the scalar-valued approximation still holds true for certain separable operator-valued kernels. The second part of this dissertation studies the EAL modeling method, which is a hybrid mechanical model for Lagrangian systems with uncertain holonomic constraints. For the singular perturbed form of the system, the kernel method is applied to approximate a penalty potential that is introduced to approximately enforce constraints. In this dissertation, the accuracy confidence function is introduced to characterize the constraint violation of an approximate trajectory. We prove that the confidence function can be decomposed into a term representing the bias and another term representing the variation. Numerical simulations are conducted to examine the factors that affect the error, including the spectral filtering, the number of samples, and the accumulation of integration error. / Doctor of Philosophy / As data-driven modeling is becoming more prevalent for representing uncertain dynamical systems, concerns also arise regarding the reliability of these methods. This dissertation employs recent developments in approximation theory to provide rigorous error analysis for two certain kernel-based approaches for modeling dynamical systems. The reproducing kernel Hilbert space (RKHS) embedding method is a non-parametric extension of the classical adaptive estimation for identifying uncertain functions in nonlinear systems. By embedding the uncertain function in a properly selected RKHS, the nonlinear state equation in Euclidean space is transformed into a linear evolution in an infinite-dimensional RKHS, where the function estimation error can be characterized directly and precisely. Pointwise convergence of the function estimate is proven over the domain that is persistently excited (PE). And a finite-dimensional approximation can be constructed within an arbitrarily small error bound. The empirical-analytical Lagrangian (EAL) model is developed to approximate the trajectory of Lagrangian systems with uncertain configuration manifold. Employing the kernel method, a penalty potential is constructed from the observation data to ``push'' the trajectory towards the actual configuration manifold. A probabilistic error bound is derived for the distance of the approximated trajectory away from the actual manifold. The error bound is proven to contain a bias term and a variance term, both of which are determined by the parameters of the kernel method.

The transport of material in geophysical fluid flows is a problem with important implications for fields as diverse as: agriculture, aviation, human health, disaster response, and weather forecasting. Due to the unsteady nature of geophysical flows, predicting how material will be transported in these systems can often be challenging. Tools from dynamical systems theory can help to improve the prediction of material transport by revealing important transport structures. These transport structures reveal areas of the flow where fluid parcels, and thus material transported by those parcels, are likely to converge or diverge. Typically, these transport structures have been uncovered by the use of Lagrangian diagnostics. Unfortunately, calculating Lagrangian diagnostics can often be time consuming and computationally expensive. Recently new Eulerian diagnostics have been developed. These diagnostics are faster and less expensive to compute, while still revealing important transport structures in fluid flows. Because Eulerian diagnostics are so new, there is still much about them and their connection to Lagrangian diagnostics that is unknown. This dissertation will fill in some of this gap and provide a mathematical bridge between Lagrangian and Eulerian diagnostics. This dissertation is composed of three projects. These projects represent theoretical, numerical, and experimental advances in the understanding of Eulerian diagnostics and their relationship to Lagrangian diagnostics. The first project rigorously explores the deep mathematical relationship that exists between Eulerian and Lagrangian diagnostics. It proves that some of the new Eulerian diagnostics are the limit of Lagrangian diagnostics as integration time of the velocity field goes to zero. Using this discovery, a new Eulerian diagnostic, infinitesimal-time Lagrangian coherent structures is developed. The second project develops a methodology for estimating local Eulerian diagnostics from wind velocity data measured by a fixed-wing unmanned aircraft system (UAS) flying in circular arcs. Using a simulation environment, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and can predict the passage of Lagrangian diagnostics. The third project applies Eulerian diagnostics to experimental data of atmospheric wind measurements. These are then compared to Eulerian diagnostics as calculated from a numerical weather simulation to look for indications of Lagrangian diagnostics. / Doctor of Philosophy / How particles are moved by fluid flows, such as the oceanic currents and the atmospheric winds, is a problem with important implications for fields as diverse as: agriculture, aviation, human health, disaster response, and weather forecasting. Because these fluid flows tend to change over time, predicting how particles will be moved by these flows can often be challenging. Fortunately, mathematical tools exist which can reveal important geometric features in these flows. These geometric features can help us to visualize regions where particles are likely to come together or spread apart, as they are moved by the flow. In the past, these geometric features have been uncovered by using methods which look at the trajectories of particles in the flow. These methods are referred to as Lagrangian, in honor of the Italian mathematician Joseph-Louis Lagrange. Unfortunately, calculating the trajectories of particles can be a time consuming and computationally expensive process. Recently, new methods have been developed which look at how the speed of the flow changes in space. These new methods are referred to as Eulerian, in honor of the Swiss mathematician Leonhard Euler. These new Eulerian methods are faster and less expensive to calculate, while still revealing important geometric features within the flow. Because these Eulerian methods are so new, there is still much that we do not know about them and their connection to the older Lagrangian methods. This dissertation will fill in some of this gap and provide a mathematical bridge between these two methodologies. This dissertation is composed of three projects. These projects represent theoretical, numerical, and experimental advances in the understanding of these new Eulerian methods and their relationship to the older Lagrangian methods. The first project explores the deep mathematical relationship that exists between Eulerian and Lagrangian diagnostic tools. It mathematically proves that some of the new Eulerian diagnostics are the limit of Lagrangian diagnostics as the trajectory’s integration times is decreased to zero. Taking advantage of this discovery, a new Eulerian diagnostic is developed, called infinitesimal-time Lagrangian coherent structures. The second project develops a technique for estimating local Eulerian diagnostics using wind speed measures from a single fixed-wing unmanned aircraft system (UAS) flying in a circular path. Using computer simulations, we show that the Eulerian diagnostics as calculated from UAS measurements provide a reasonable estimate of the true local Eulerian diagnostics. Furthermore, we show that these Eulerian diagnostics can be used to estimate the local Lagrangian diagnostics. The third project applies these Eulerian diagnostics to real-world wind speed measurements. These results are then compared to Eulerian diagnostics that were calculated from a computer simulation to look for indications of Lagrangian diagnostics.