An algorithm is presented for the solution of mechanical contact problems using the displacement based Finite Element Method. The corrections are applied as forces at the global level, together with any corrections for other nonlinearities, without having to nominate either body as target or contactor. The technique requires statically reducing the global stiffness matrices to each degree of freedom involved in contact. Nodal concentrated force are redistributed as continuous tractions. These tractions are re-integrated over the element domains of the opposing body. This creates a set of virtual elements which are assembled to provide a convenient mesh of the properties of the opposing body no matter what its actual discretizaton into elements. Virtual nodal quantities are used to calculate corrective forces that are optimal to first order. The work also presents a derivation of refereritial strain tensors. This sheds new light on the updated Lagrangian formulation, gives a complete and correct incremental form for the Lagrangian strain tensor and illustrates the role of the reference configuration and what occurs when it is changed. / Thesis / Doctor of Philosophy (PhD)

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.