Browsing by Author "Gugercin, Serkan"

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    Application of Interpolatory Methods of Model Reduction to an Elevated Railway Pier
    Bertero, S.; Gugercin, Serkan; Sarlo, R. (2023-01-01)
    Be it due to time constraints or insufficient processing power - or a combination of both - the use of models with large numbers of degrees of freedom (DoF) may be unsuitable to provide a client with results in a timely manner. The use of physics-based reduced models - or proxy structures - are popular among practitioners to solve this issue, as they keep intact all the underlying properties of the second order problems at a fraction of the cost. In this paper, interpolatory methods of model reduction are explored as an alternative, and applied to a 3D Space Frame. The methods chosen allow for structure-preserving reduced models and differ mainly on the selection of interpolation points. A comparison between the response of these reduced models and a proxy structure against two different types of inputs show that interpolatory methods are a viable, more flexible option when it comes to reducing the internal DoF's of a structural model, though engineering judgement helps to ensure it adequately captures the most relevant aspects of the response for the specific application.
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    Approximate Deconvolution Reduced Order Modeling
    Xie, Xuping (Virginia Tech, 2015-11-03)
    This thesis proposes a large eddy simulation reduced order model (LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition (POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures. An approximate deconvolution (AD) approach is used to solve the ROM closure problem and develop a new AD-ROM. This AD-ROM is tested in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient ( ν= 10⁻³).
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    Approximation of Parametric Dynamical Systems
    Carracedo Rodriguez, Andrea (Virginia Tech, 2020-09-02)
    Dynamical systems are widely used to model physical phenomena and, in many cases, these physical phenomena are parameter dependent. In this thesis we investigate three prominent problems related to the simulation of parametric dynamical systems and develop the analysis and computational framework to solve each of them. In many cases we have access to data resulting from simulations of a parametric dynamical system for which an explicit description may not be available. We introduce the parametric AAA (p-AAA) algorithm that builds a rational approximation of the underlying parametric dynamical system from its input/output measurements, in the form of transfer function evaluations. Our algorithm generalizes the AAA algorithm, a popular method for the rational approximation of nonparametric systems, to the parametric case. We develop p-AAA for both scalar and matrix-valued data and study the impact of parameter scaling. Even though we present p-AAA with parametric dynamical systems in mind, the ideas can be applied to parametric stationary problems as well, and we include such examples. The solution of a dynamical system can often be expressed in terms of an eigenvalue problem (EVP). In many cases, the resulting EVP is nonlinear and depends on a parameter. A common approach to solving (nonparametric) nonlinear EVPs is to approximate them with a rational EVP and then to linearize this approximation. An existing algorithm can then be applied to find the eigenvalues of this linearization. The AAA algorithm has been successfully applied to this scheme for the nonparametric case. We generalize this approach by using our p-AAA algorithm to find a rational approximation of parametric nonlinear EVPs. We define a corresponding linearization that fits the format of the compact rational Krylov (CORK) algorithm for the approximation of eigenvalues. The simulation of dynamical systems may be costly, since the need for accuracy may yield a system of very large dimension. This cost is magnified in the case of parametric dynamical systems, since one may be interested in simulations for many parameter values. Interpolatory model order reduction (MOR) tackles this problem by creating a surrogate model that interpolates the original, is of much smaller dimension, and captures the dynamics of the quantities of interest well. We generalize interpolatory projection MOR methods from parametric linear to parametric bilinear systems. We provide necessary subspace conditions to guarantee interpolation of the subsystems and their first and second derivatives, including the parameter gradients and Hessians. Throughout the dissertation, the analysis is illustrated via various benchmark numerical examples.
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    Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning
    Bales, Dustin Bennett (Virginia Tech, 2016-06-20)
    The ability to classify occupants in a building has far-reaching applications in security, monitoring human health, and managing energy resources effectively. In this work, gender and weight of walkers are classified via machine learning or pattern recognition techniques. Accelerometers mounted beneath the floor of Virginia Tech's Goodwin Hall measured walkers' gait. These acceleration measurements serve as the inputs to machine learning techniques allowing for classification. For this work, the gait of fifteen individual walkers was recorded via fourteen accelerometers as they, alone, walked down the instrumented hallway, in multiple trials. These machine learning algorithms produce an 88 % accurate model for gender classification. The machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are Discrete Empirical Interpolation Method (DEIM), Q-DEIM, and Projection Coefficients. A two-part methodology is proposed to implement the approach completed in this thesis work. The first step validates the algorithm design choices, i.e. using bagged or boosted decision trees for classification. The second step reduces the walking data measured to truncate accelerometers which do not aid in increasing characteristic classification.
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    Commutation Error in Reduced Order Modeling
    Koc, Birgul (Virginia Tech, 2018-10-01)
    We investigate the effect of spatial filtering on the recently proposed data-driven correction reduced order model (DDC-ROM). We compare two filters: the ROM projection, which was originally used to develop the DDC-ROM, and the ROM differential filter, which uses a Helmholtz operator to attenuate the small scales in the input signal. We focus on the following questions: ``Do filtering and differentiation with respect to space variable commute, when filtering is applied to the diffusion term?'' or in other words ``Do we have commutation error (CE) in the diffusion term?" and ``If so, is the commutation error data-driven correction ROM (CE-DDC-ROM) more accurate than the original DDC-ROM?'' If the CE exists, the DDC-ROM has two different correction terms: one comes from the diffusion term and the other from the nonlinear convection term. We investigate the DDC-ROM and the CE-DDC-ROM equipped with the two ROM spatial filters in the numerical simulation of the Burgers equation with different diffusion coefficients and two different initial conditions (smooth and non-smooth).
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    Computing Reduced Order Models via Inner-Outer Krylov Recycling in Diffuse Optical Tomography
    O'Connell, Meghan; Kilmer, Misha E.; de Sturler, Eric; Gugercin, Serkan (Siam Publications, 2017-01-01)
    In nonlinear imaging problems whose forward model is described by a partial differential equation (PDE), the main computational bottleneck in solving the inverse problem is the need to solve many large-scale discretized PDEs at each step of the optimization process. In the context of absorption imaging in diffuse optical tomography, one approach to addressing this bottleneck proposed recently (de Sturler, et al, 2015) reformulates the viewing of the forward problem as a differential algebraic system, and then employs model order reduction (MOR). However, the construction of the reduced model requires the solution of several full order problems (i.e. the full discretized PDE for multiple right-hand sides) to generate a candidate global basis. This step is then followed by a rank-revealing factorization of the matrix containing the candidate basis in order to compress the basis to a size suitable for constructing the reduced transfer function. The present paper addresses the costs associated with the global basis approximation in two ways. First, we use the structure of the matrix to rewrite the full order transfer function, and corresponding derivatives, such that the full order systems to be solved are symmetric (positive definite in the zero frequency case). Then we apply MOR to the new formulation of the problem. Second, we give an approach to computing the global basis approximation dynamically as the full order systems are solved. In this phase, only the incrementally new, relevant information is added to the existing global basis, and redundant information is not computed. This new approach is achieved by an inner-outer Krylov recycling approach which has potential use in other applications as well. We show the value of the new approach to approximate global basis computation on two DOT absorption image reconstruction problems.
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    Damping optimization of parameter dependent mechanical systems by rational interpolation
    Tomljanović, Z.; Beattie, Christopher A.; Gugercin, Serkan (2017-07-06)
    We consider an optimization problem related to semi-active damping of vibrating systems. The main problem is to determine the best damping matrix able to minimize influence of the input on the output of the system. We use a minimization criteria based on the $\mathcal{H}_2$ system norm. The objective function is non-convex and the associated optimization problem typically requires a large number of objective function evaluations. We propose an optimization approach that calculates `interpolatory' reduced order models, allowing for significant acceleration of the optimization process. In our approach, we use parametric model reduction (PMOR) based on the Iterative Rational Krylov Algorithm, which ensures good approximations relative to the $\mathcal{H}_2$ system norm, aligning well with the underlying damping design objectives. For the parameter sampling that occurs within each PMOR cycle, we consider approaches with predetermined sampling and approaches using adaptive sampling, and each of these approaches may be combined with three possible strategies for internal reduction. In order to preserve important system properties, we maintain second-order structure, which through the use of modal coordinates, allows for very efficient implementation. The methodology proposed here provides a significant acceleration of the optimization process; the gain in efficiency is illustrated in numerical experiments.
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    Data-Driven Modeling of Tracked Order Vibration in Turbofan Engine
    Krishnan, Manu (Virginia Tech, 2022-01-11)
    Aircraft engines are one of the most heavily instrumented parts of an aircraft, and the data from various types of instrumentation across these engines are continuously monitored both offline and online for potential anomalies. Vibration monitoring in aircraft engines is traditionally performed using an order tracking methodology. Currently, there are no representative and efficient physics-based models with the adequate fidelity to perform vibration predictions in aircraft engines, given various parametric dependencies existing among different attributes such as temperature, pressure, and external conditions. This gap in research is primarily attributed to the limited understanding of mutual interactions of different variables and the nonlinear nature of engine vibrations. The objective of the current study is three-fold: (i) to present a preliminary investigation of tracked order vibrations in aircraft engines and statistically analyze them in the context of their operating environment, (ii) to develop data-driven modeling methodology to approximate a dynamical system from input-output data, and (iii) to leverage these data-driven modeling methodologies to develop highly accurate models for tracked order vibration in a turbo-fan engine valid over a wide range of operating conditions. Off-the-shelf data-driven modeling techniques, such as machine learning methods (eg., regression, neural networks), have several drawbacks including lack of interpretability and limited scope, when applying them to a complex multiscale multi-physical dynamical system. Moreover, for dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function, but should also generally approximate the input-output behavior of the data source. The author proposes a novel methodology known as Wavelet-based Dynamic Mode Decomposition (WDMD). The methodology entails using wavelets in conjunction with input-output dynamic mode decomposition (ioDMD). Similar to time-delay embedded DMD (Delay-DMD), WDMD builds on the ioDMD framework without the restrictive assumption of full state measurements. The author demonstrates the present methodology's applicability by modeling the input-output response of an Euler-Bernoulli finite element beam model, followed by an experimental investigation. As a first step towards modeling the tracked order vibration amplitudes of turbofan engines, the interdependencies and cross-correlation structure between various thermo-mechanical variables and tracked order vibration are analyzed. The order amplitudes are further contextualized in terms of their operating regime, and exploratory data analyses are performed to quantify the variability within each operating condition (OC). The understanding of complex correlation structures is leveraged and subsequently utilized to model tracked order vibrations. Switching linear dynamical system (SLDS) models are developed using individual data-driven models constructed using WDMD, and its performance in approximating the dynamics of the $1^{st}$ order amplitudes are compared with the state-of-the-art time-delay embedded dynamic mode decomposition (Delay-DMD) and Lasso regression. A parametric approach is proposed to improve the model further by leveraging previously developed WDMD and Delay-DMD methods and a parametric interpolation scheme. In particular, a recently developed pole-residue interpolation scheme is adopted to interpolate between several linear, data-driven reduced-order models (ROMs), constructed using WDMD and Delay-DMD surrogates, at known parameter samples. The parametric modeling approach is demonstrated by modeling the transverse vibration of an axially loaded finite element (FE) beam, where the axial loading is the parameter. Finally, a parametric modeling strategy for tracked order amplitudes is presented by constructing locally valid ROMs at different parametric samples corresponding to each pass-off test. The performance of the parametric-ROM is quantified and compared with the previous frameworks. This work was supported by the Rolls-Royce Fellowship, sponsored by the College of Engineering, Virginia Tech.
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    Data-driven structured realization
    Schulze, P.; Unger, Benjamin; Beattie, Christopher A.; Gugercin, Serkan (Elsevier, 2018-01-15)
    We present a framework for constructing structured realizations of linear dynamical systems having transfer functions of the form C̃(∑k=1K hk(s)Ãk)-1B̃ where h1, h2, ..., hk are prescribed functions that specify the surmised structure of the model. Our construction is data-driven in the sense that an interpolant is derived entirely from measurements of a transfer function. Our approach extends the Loewner realization framework to more general system structure that includes second-order (and higher) systems as well as systems with internal delays. Numerical examples demonstrate the advantages of this approach.
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    Diagonal Estimation with Probing Methods
    Kaperick, Bryan James (Virginia Tech, 2019-06-21)
    Probing methods for trace estimation of large, sparse matrices has been studied for several decades. In recent years, there has been some work to extend these techniques to instead estimate the diagonal entries of these systems directly. We extend some analysis of trace estimators to their corresponding diagonal estimators, propose a new class of deterministic diagonal estimators which are well-suited to parallel architectures along with heuristic arguments for the design choices in their construction, and conclude with numerical results on diagonal estimation and ordering problems, demonstrating the strengths of our newly-developed methods alongside existing methods.
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    Efficient 𝐻₂-Based Parametric Model Reduction via Greedy Search
    Cooper, Jon Carl (Virginia Tech, 2021-01-19)
    Dynamical systems are mathematical models of physical phenomena widely used throughout the world today. When a dynamical system is too large to effectively use, we turn to model reduction to obtain a smaller dynamical system that preserves the behavior of the original. In many cases these models depend on one or more parameters other than time, which leads to the field of parametric model reduction. Constructing a parametric reduced-order model (ROM) is not an easy task, and for very large parametric systems it can be difficult to know how well a ROM models the original system, since this usually involves many computations with the full-order system, which is precisely what we want to avoid. Building off of efficient 𝐻-infinity approximations, we develop a greedy algorithm for efficiently modeling large-scale parametric dynamical systems in an 𝐻₂-sense. We demonstrate the effectiveness of this greedy search on a fluid problem, a mechanics problem, and a thermal problem. We also investigate Bayesian optimization for solving the optimization subproblem, and end with extending this algorithm to work with MIMO systems.
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    Environmental Tracking and Formation Control for an Autonomous Underwater Vehicle Platoon with Limited Communication
    Roberson, David Gray (Virginia Tech, 2008-02-01)
    A platoon of autonomous underwater vehicles provides a compelling platform for studying many challenging issues in multi-agent cooperative control. These challenges include developing cooperative algorithms suitable to practical multi-vehicle applications. They also include addressing intervehicle communication issues, such as sharing information via limited bandwidth channels and selecting network architecture to facilitate control design. This work addresses problems in each of these areas. Environmental tracking and formation control serves as the main application upon which this work focuses. In the tracking and formation control application, a team of vehicles obtains a spatial average of an environmental feature by collecting and sharing local measurements. To achieve this objective, vehicles track a desired environmental field contour with their average position while maintaining a desired spatial formation about the average. A decentralized consensus-based algorithm is developed for controlling the platoon. In a novel two-level consensus approach, each vehicle estimates a virtual leader trajectory using local and shared measurements at one level, then positions itself about the virtual leader at a second level. Due to very low bandwidth underwater communication, vehicles share information intermittently, and the platoon network is effectively disconnected at every instant of time. This issue is addressed by modeling the platoon as a periodic switched system whose frozen-time subsystems possess disconnected networks, but whose time-averaged system is connected. The stability and input-output properties of the switched system are related to those of the corresponding average system. Under sufficiently fast switching, asymptotic stability of the average system implies asymptotic stability of the switched system and the existence of an L2 gain. Estimates of the slowest stabilizing switching rate and the L2 gain are derived. Controller and estimator design are complicated by the lack of a separation principle for decentralized systems and by the effects of intervehicle coupling. The potential for choosing the communication topology in a manner that leads to design simplifications is investigated. In particular, a transformation is presented that converts the platoon state coefficient matrix to block diagonal form when the communication network has a circulant structure.
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    Finite Horizon Optimality and Operator Splitting in Model Reduction of Large-Scale Dynamical System
    Sinani, Klajdi (Virginia Tech, 2020-07-15)
    Simulation, design, and control of dynamical systems play an important role in numerous scientific and industrial tasks. The need for detailed models leads to large-scale dynamical systems, posing tremendous computational difficulties when employed in numerical simulations. In order to overcome these challenges, we perform model reduction, replacing the large-scale dynamics with high-fidelity reduced representations. There exist a plethora of methods for reduced order modeling of linear systems, including the Iterative Rational Krylov Algorithm (IRKA), Balanced Truncation (BT), and Hankel Norm Approximation. However, these methods generally target stable systems and the approximation is performed over an infinite time horizon. If we are interested in a finite horizon reduced model, we utilize techniques such as Time-limited Balanced Truncation (TLBT) and Proper Orthogonal Decomposition (POD). In this dissertation we establish interpolation-based optimality conditions over a finite horizon and develop an algorithm, Finite Horizon IRKA (FHIRKA), that produces a locally optimal reduced model on a specified time-interval. Nonetheless, the quantities being interpolated and the interpolant are not the same as in the infinite horizon case. Numerical experiments comparing FHIRKA to other algorithms further support our theoretical results. Next, we discuss model reduction for nonlinear dynamical systems. For models with unstructured nonlinearities, POD is the method of choice. However, POD is input dependent and not optimal with respect to the output. Thus, we use operator splitting to integrate the best features of system theoretic approaches with trajectory based methods such as POD in order to mitigate the effect of the control inputs for the approximation of nonlinear dynamical systems. We reduce the linear terms with system theoretic methods and the nonlinear terms terms via POD. Evolving the linear and nonlinear terms separately yields the reduced operator splitting solution. We present an error analysis for this method, as well as numerical results that illustrate the effectiveness of our approach. While in this dissertation we only pursue the splitting of linear and nonlinear terms, this approach can be implemented with Quadratic Bilinear IRKA or Balanced Truncation for Quadratic Bilinear systems to further diminish the input dependence of the reduced order modeling.
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    Frequency-Domain Learning of Dynamical Systems From Time-Domain Data
    Ackermann, Michael Stephen (Virginia Tech, 2022-06-21)
    Dynamical systems are useful tools for modeling many complex physical phenomena. In many situations, we do not have access to the governing equations to create these models. Instead, we have access to data in the form of input-output measurements. Data-driven approaches use these measurements to construct reduced order models (ROMs), a small scale model that well approximates the true system, directly from input/output data. Frequency domain data-driven methods, which require access to values (and in some cases to derivatives) of the transfer function, have been very successful in constructing high-fidelity ROMs from data. However, at times this frequency domain data can be difficult to obtain or one might have only access to time-domain data. Recently, Burohman et al. [2020] introduced a framework to approximate transfer function values using only time-domain data. We first discuss improvements to this method to allow a more efficient and more robust numerical implementation. Then, we develop an algorithm that performs optimal-H2 approximation using purely time-domain data; thus significantly extending the applicability of H2-optimal approximation without a need for frequency domain sampling. We also investigate how well other established frequency-based ROM techniques (such as the Loewner Framework, Adaptive Anderson-Antoulas Algorithm, and Vector Fitting) perform on this identified data, and compare them to the optimal-H2 model.
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    Gappy POD and Temporal Correspondence for Lizard Motion Estimation
    Kurdila, Hannah Robertshaw (Virginia Tech, 2018-06-20)
    With the maturity of conventional industrial robots, there has been increasing interest in designing robots that emulate realistic animal motions. This discipline requires careful and systematic investigation of a wide range of animal motions from biped, to quadruped, and even to serpentine motion of centipedes, millipedes, and snakes. Collecting optical motion capture data of such complex animal motions can be complicated for several reasons. Often there is the need to use many high-quality cameras for detailed subject tracking, and self-occlusion, loss of focus, and contrast variations challenge any imaging experiment. The problem of self-occlusion is especially pronounced for animals. In this thesis, we walk through the process of collecting motion capture data of a running lizard. In our collected raw video footage, it is difficult to make temporal correspondences using interpolation methods because of prolonged blurriness, occlusion, or the limited field of vision of our cameras. To work around this, we first make a model data set by making our best guess of the points' locations through these corruptions. Then, we randomly eclipse the data, use Gappy POD to repair the data and then see how closely it resembles the initial set, culminating in a test case where we simulate the actual corruptions we see in the raw video footage.
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    Generating Traveling Waves in Finite Media Using Single-Point Excitation via Passive Absorber
    Motaharibidgoli, Seyedmostafa (Virginia Tech, 2023-05-24)
    In the mammalian auditory system, specifically in the cochlea of the inner ear, the Basilar Membrane (BM) and hair cells are responsible for transducing incoming acoustic waves into electrical signals. These acoustic signals are carried as traveling waves by the BM and propagate from the base of the cochlea toward its apex where the helicotrema is located. An impressive feature of the mammalian auditory system is to prevent the propagated waves from reflecting which allows mammals to hear sounds without any reflection or overlap. This extraordinary characteristic of the inner ear is the main inspiration for this work. In the present study, the dynamic behavior of a beam structure with one or more attached spring-damper (SD) systems as passive absorbers is studied when excited by a harmonic force. The location of the spring-damper system divides the beam into two dynamic regions: one which exhibits non-reflecting traveling waves and the other with standing waves. In this work, the separation of traveling and standing waves is studied analytically, numerically, and experimentally. To the best of the author's knowledge, this is the first time in the literature that traveling and standing wave separation in a beam is realized experimentally using a single-point excitation and a spring-damper. Experimental results are used to validate the models of the system. Moreover, a parametric study is performed to gain a better understanding of the effect of different parameters on the quality of the generated waves in the structure. Furthermore, the effect of attaching the second spring-damper to the system is presented. Adding the secondary SD system results in increasing the excitation frequency range so that wave separation can be achieved. The results of this work can be used in various applications such as vibration suppression, energy absorption, particle transportation, and in exploring possible explanations for the BM and helicotrema functions in the cochlea.
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    H2 model reduction for large-scale linear dynamical systems
    Gugercin, Serkan; Antoulas, Athanasios C.; Beattie, Christopher A. (Siam Publications, 2008)
    The optimal H2 model reduction problem is of great importance in the area of dynamical systems and simulation. In the literature, two independent frameworks have evolved focusing either on solution of Lyapunov equations on the one hand or interpolation of transfer functions on the other, without any apparent connection between the two approaches. In this paper, we develop a new unifying framework for the optimal H2 approximation problem using best approximation properties in the underlying Hilbert space. This new framework leads to a new set of local optimality conditions taking the form of a structured orthogonality condition. We show that the existing Lyapunov- and interpolation-based conditions are each equivalent to our conditions and so are equivalent to each other. Also, we provide a new elementary proof of the interpolation-based condition that clarifies the importance of the mirror images of the reduced system poles. Based on the interpolation framework, we describe an iteratively corrected rational Krylov algorithm for H2 model reduction. The formulation is based on finding a reduced order model that satisfies interpolation-based first-order necessary conditions for H2 optimality and results in a method that is numerically effective and suited for large-scale problems. We illustrate the performance of the method with a variety of numerical experiments and comparisons with existing methods.
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    Identification, Analysis, and Control of Power System Events Using Wide-Area Frequency Measurements
    Wang, Joshua Kevin (Virginia Tech, 2009-01-27)
    The power system has long been operated in a shroud of introspection. Only recently have dynamic, wide-area time synchronized grid measurements brought to light the complex relationships between large machines thousands of miles apart. These measurements are invaluable to understanding the health of the system in real time, for disturbances to the balance between generation and load are manifest in the propagation of electromechanical waves throughout the grid. The global perspective of wide-area measurements provides a platform from which the destructive effects of these disturbances can be avoided. Virginia Tech's distributed network of low voltage frequency monitors, FNET, is able to track these waves as they travel throughout the North American interconnected grids. In contrast to other wide-area measurement systems, the ability to easily measure frequency throughout the grid provides a way to identify, locate, and analyze disturbances with high dynamic accuracy. The unique statistical properties of wide-area measurements require robust tools in order to accurately understand the nature of these events. Expert systems and data conditioning can then be used to quantify the magnitude and location of these disturbances without requiring any knowledge of the system state or topology. Adaptive application of these robust methods form the basis for real-time situational awareness and control. While automated control of the power system rarely utilize wide-area measurements, global insight into grid behavior can only improve disturbance rejection.
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    Immersed and Discontinuous Finite Element Methods
    Chaabane, Nabil (Virginia Tech, 2015-04-20)
    In this dissertation we prove the superconvergence of the minimal-dissipation local discontinuous Galerkin method for elliptic problems and construct optimal immersed finite element approximations and discontinuous immersed finite element methods for the Stokes interface problem. In the first part we present an error analysis for the minimal dissipation local discontinuous Galerkin method applied to a model elliptic problem on Cartesian meshes when polynomials of degree at most k and an appropriate approximation of the boundary condition are used. This special approximation allows us to achieve k + 1 order of convergence for both the potential and its gradient in the L2 norm. Here we improve on existing estimates for the solution gradient by a factor √h. In the second part we present discontinuous immersed finite element (IFE) methods for the Stokes interface problem on Cartesian meshes that does not require the mesh to be aligned with the interface. As such, we allow unfitted meshes that are cut by the interface. Thus, elements may contain more than one fluid. On these unfitted meshes we construct an immersed Q1/Q0 finite element approximation that depends on the location of the interface. We discuss the basic features of the proposed Q1/Q0 IFE basis functions such as the unisolvent property. We present several numerical examples to demonstrate that the proposed IFE approximations applied to solve interface Stokes problems maintain the optimal approximation capability of their standard counterpart applied to solve the homogeneous Stokes problem. Similarly, we also show that discontinuous Galerkin IFE solutions of the Stokes interface problem maintain the optimal convergence rates in both L2 and broken H1 norms. Furthermore, we extend our method to solve the axisymmetric Stokes interface problem with a moving interface and test the proposed method by solving several benchmark problems from the literature.
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    Immersed Finite Elements for a Second Order Elliptic Operator and Their Applications
    Zhuang, Qiao (Virginia Tech, 2020-06-17)
    This dissertation studies immersed finite elements (IFE) for a second order elliptic operator and their applications to interface problems of related partial differential equations. We start with the immersed finite element methods for the second order elliptic operator with a discontinuous coefficient associated with the elliptic interface problems. We introduce an energy norm stronger than the one used in [111]. Then we derive an estimate for the IFE interpolation error with this energy norm using patches of interface elements. We prove both the continuity and coercivity of the bilinear form in a partially penalized IFE (PPIFE) method. These properties allow us to derive an error bound for the PPIFE solution in the energy norm under the standard piecewise $H^2$ regularity assumption instead of the more stringent $H^3$ regularity used in [111]. As an important consequence, this new estimation further enables us to show the optimal convergence in the $L^2$ norm which could not be done by the analysis presented in [111]. Then we consider applications of IFEs developed for the second order elliptic operator to wave propagation and diffusion interface problems. The first application is for the time-harmonic wave interface problem that involves the Helmholtz equation with a discontinuous coefficient. We design PPIFE and DGIFE schemes including the higher degree IFEs for Helmholtz interface problems. We present an error analysis for the symmetric linear/bilinear PPIFE methods. Under the standard piecewise $H^2$ regularity assumption for the exact solution, following Schatz's arguments, we derive optimal error bounds for the PPIFE solutions in both an energy norm and the usual $L^2$ norm provided that the mesh size is sufficiently small. {In the second group of applications, we focus on the error analysis for IFE methods developed for solving typical time-dependent interface problems associated with the second order elliptic operator with a discontinuous coefficient.} For hyperbolic interface problems, which are typical wave propagation interface problems, we reanalyze the fully-discrete PPIFE method in [143]. We derive the optimal error bounds for this PPIFE method for both an energy norm and the $L^2$ norm under the standard piecewise $H^2$ regularity assumption in the space variable of the exact solution. Simulations for standing and travelling waves are presented to corroborate the results of the error analysis. For parabolic interface problems, which are typical diffusion interface problems, we reanalyze the PPIFE methods in [113]. We prove that these PPIFE methods have the optimal convergence not only in an energy norm but also in the usual $L^2$ norm under the standard piecewise $H^2$ regularity.