Master of Science, University of Toledo, 2020, Civil Engineering

Elastomeric bearings have been used in bridges for more than 60 years. They are economical, durable, require minimal maintenance, and are suitable for larger rotations. The AASHTO LRFD Design Specifications provide two different design approaches for elastomeric bearings: Method A and Method B. Method A is simple and rational whereas Method B is complex but more realistic. Method A was proposed with the goal to make it quick and be consistent with Method B. The study has developed solution spaces for these methods which not only assist the analysts to understand the inter-relationship between these methods but also check their compatibility. The analysis of solution spaces demonstrated the possibilities of the cases where bearings designed using Method A fails to satisfy the provisions of Method B.These cases result in inconsistent design and cause difficulties to the designer. Thus, a profound analysis of the behavior of elastomeric bearings is required. For the purpose, nonlinear finite element models are developed using Abaqus/CAE 2019 and are validated with the experimental tests. The results from validated finite element models are compared with analytical closed-form solutions and current AASHTO design methodologies to check their accuracy and recommend a suitable analytical model for a more realistic design.

Master of Science, The Ohio State University, 2018, Chemical Engineering

Metal-organic frameworks (MOFs) are a new class of crystalline porous materials formed by connecting metal clusters with organic linkers. In recent years, there has been an increasing demand of ultra-high surface area MOFs as these materials can be potentially used in applications such as gas storage, catalysis, and drug delivery. Therefore, accurate determination of surface area is important toward the development of promising MOFs for these applications. Surface areas of MOFs are commonly characterized using the Brunauer-Emmett-Teller (BET) method, a widely used experimental technique. The BET method has been found to provide decent results but in recent years, some discrepancy has been reported. In this study, we carried out a comprehensive evaluation of the BET method for a large collection of MOFs. We used grand canonical Monte Carlo (GCMC) simulations to obtain isotherms in nanoporous materials. We also used cylindrical carbon nanotubes as model systems for capturing the behavior of the BET method. We compared, evaluated, analyzed our results and summarized several possible scenarios that could happen. The excess sorption work (ESW) method has been also utilized and demonstrated as an addition to provide better estimation of surface area, in particular for cases when the BET method overestimates the surface area significantly.

Master of Sciences, Case Western Reserve University, 2018, Statistics

Forecasting is an important area in statistics and as a result it is important that our forecasts reflect our uncertainties. But most importantly, our forecasts should be as accurate as possible. And how can forecasters tell whether their probabilistic forecast distribution are the same or close to the true distribution, which is unknown most of time (if not all time) to the forecaster. We need to come up with a diagnostic tool that helps us to know how close our probabilistic forecasts distributions are to the true distribution. Verification rank histograms and probability integral transforms (PIT) histograms are the most common diagnostic tools to determine if probabilistic forecast distributions and observations are well calibrated in the univariate settings. Calibration in a nutshell means how statistically compatible the probabilistic forecasts and observations are. The purpose of this study is to compare the sensitivity of the following calibration metrics/multivariate ranking methods - Multivariate ranking method, Minimum Spanning Tree, Band Depth and Average ranking method to mispecifications. A simulation study and a case study of the Orbiting Carbon Observatory 2 (OCO-2) are presented. The general findings from our study is that, when comparing the four diagnostic tools for forecast ensembles, the minimum spanning tree and the band depth methods are better at detecting misspecifications than the multivariate rank method. Also the average rank method with the band depth method and/or minimum spanning tree method gives us more information than band depth or minimum spanning tree alone.

Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering

This dissertation presents a domain decomposition method as an effective and efficient preconditioner for frequency domain FEM solution of geometrically complex and electrically large electromagnetic problems. The method reduces memory requirements by decomposing the original problem domain into several non-overlapping and possibly repeatable sub-domains. At the heart of this research are the Robin-to-Robin map, the “cement” finite element coupling of non-conforming grids and the concept of duality paring. The Robin's transmission condition is employed on interfaces between adjacent sub-domains to enforce continuity of electromagnetic fields and to ensure the sub-domain problems are well-posed. Through the introduction of cement variables, the meshes at the interface could be non-conformal which significantly relaxes the meshing procedures. By following the spirit of duality paring a symmetric system is obtained to better reflect physical nature of the problem. These concepts in conjunction with the so-called finite element tearing and interconnecting algorithm form the basic modules of the present domain decomposition method. To enhance the convergence of DDM solver, the Krylov solvers instead of classical stationary solvers are employed and studied. In order to account the radiation condition exactly thus eliminating spurious reflection, a boundary element formulation is hybridized with the present DD method, also through the aforementioned novel concepts. One of the special cases of present hybridization is the well known hybrid finite element and boundary element method. It will be shown that the proposed hybrid offers simultaneously: (1) symmetry, (2) modularity, (3) non-conformity between FEM and BEM domains, (4) free of internal resonance, and (5) natural and effective preconditioning scheme that guarantees spectral radius less or equal to one. Lastly this dissertation presents a DDM solution scheme for analyzing electromagnetic problems involving multiple se (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Mechanical Engineering

The mechanical properties of materials are fundamentally governed by their microstructural characteristics, delineating a profound relationship between structure and behavior. Whether manifesting as polycrystalline arrangements composed of grains, particulate dispersion within composites, or the intricacies of Selective Laser Melting (SLM)-induced melt pools, microstructural heterogeneity profoundly influences material response to external loads. Moreover, the presence of defects such as voids, precipitates, and cracks introduces additional complexities, underscoring the critical role of microstructural analysis in elucidating material performance. As such, comprehending and manipulating these microstructural features hold paramount importance in the design and optimization of materials tailored to specific engineering requirements. This introductory exploration sets the stage for a comprehensive investigation into the interplay between microstructure and mechanical behavior in diverse material systems. The first component of this dissertation focuses on modeling Polycrystalline materials from imaging data. As mentioned earlier, polycrystalline microstructures are composed of grains and hence, it is important to accurately capture the grain boundaries when modeling them from microstructure images. Moreover, it is also possible for defects to be present in microstructures such as precipitates, voids, and cracks, which can impact mechanical behavior. Therefore, we also present an example modeling the presence of precipitates in a polycrystalline microstructure, which shows that the developed framework can handle them. To do this, we introduce a set of integrated image processing algorithms for processing low-resolution images of a polycrystalline microstructure and convert the grain boundaries into a Non-Uniform Rational B-Splines (NURBS) representation. Next, the NURBS representation of the material microstructures is used as an input to a non-iterative mesh (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2021, Mechanical Engineering

In computational materials science, finite element (FE) modeling and simulation are widely used to determine the material behavior and mechanisms, which can further guide the cost-effective design of materials with targeted properties. However, modeling materials with complex microstructures always confronts high labor costs, which can be attributed to two key challenges: (i) generating realistic microstructures of the material with arbitrary morphologies and converting them into appropriate conforming FE meshes; (ii) creating high-fidelity models and retaining computational efficiency. In this thesis, an automated computational framework is introduced to overcome these challenges by implementing novel microstructure reconstruction, reduced-order FE model, homogenization-based statistical analysis, and other advanced numerical techniques, for the modeling of materials with complex microstructures. As the first step, a new set of virtual reconstruction algorithms are introduced for the creation of realistic geometrical models of the microstructure. These algorithms employ a Non-Uniform Rational B-Spline (NURBS) function to explicitly represent the material morphologies from image data. For reconstructing the initial microstructure, a packing algorithm that utilizes bounding box representation of geometry is firstly presented to efficiently check the intersection between inclusions. An optimization phase is then implemented to build the final microstructural model, relying either on the Genetic Algorithm to selectively eliminate some of the inclusions or their sequential relocation within the initial microstructure. This reconstruction procedure is a high integration with mesh generation algorithms to facilitate subsequent FE analysis. Several high-fidelity FE models and advanced numerical techniques in this computational framework are presented to model materials and analyze their micromechanical behaviors. A reduced-order FE model is introduced for simulating (open full item for complete abstract)

PhD, University of Cincinnati, 2019, Engineering and Applied Science: Civil Engineering

Accurate prediction of crack propagation is of great importance in both academy and industry. Among various theoretical models and numerical techniques, the cohesive zone model and its related methods have been extensively employed to fracture problems due to its efficiency. Although well recognized, it still has its limitations. This dissertation starts with tackling a critical discontinuity issue of the exponential cohesive law in cyclic loading scenarios. A new formulation is proposed to correct that error. The cohesive zone model has also been combined with the fatigue crack growth rate to simulate composite delamination, and this method is improved by incorporating the damage accumulation in the ascending part of the cohesive law. The improved method is implemented into LS-DYNA and used to predict the fatigue life of adhesive joints. Another problem of the cohesive zone model is the artificial compliance issue when zero-thickness cohesive elements are inserted between every element boundary. A comprehensive study is done about the relationship between artificial compliance and the initial stiffness in the cohesive zone model. The study can serve as a guideline of whether to choose the bilinear cohesive law or exponential cohesive law in different scenarios. A mesh-independent cohesive element approach is invented for arbitrary crack propagation. This method adopts the cohesive zone enlargement method and is programmed as a user-defined cohesive material subroutine in LS-DYNA. It successfully predicts the crack shape of two benchmark examples. Diving deeper into the mathematical formulation of the cohesive element method, we found out that the Symmetric Interior Penalty Galerkin method can better constraint the elements' boundaries. A comprehensive comparison is conducted between these two formulations on both their convergence and artificial compliance properties. Furthermore, an algorithm is invented and implemented into LS-DYNA to enable the transition fro (open full item for complete abstract)

MS, University of Cincinnati, 2018, Engineering and Applied Science: Aerospace Engineering

Solving sparse linear system of equations represents the major computation cost in many scientific and engineering areas. There are two major approaches for solving large sparse linear system: direct method and iterative method. Both methods have their own advantages for certain type of problem. In general, the direct method is more robust and the iterative method has better scalability. High-order Discontinuous Galerkin (DG) Method has gained growing interest in Computation Fluid Dynamics (CFD) community. The Jacobian matrices that arise in the application of the DG method are sparse and block-structured. This thesis summarizes the development of direct and iterative solvers for sparse block linear system. Block capability is achieved by using Intel CPU library or Nvidia GPU based libraries. The direct solver uses left-looking method with fill-reducing ordering to factorize the matrices into lower/upper triangular parts. The iterative solver uses line-based Successive Over-Relaxation method (SLOR) and Alternating Direction Implicit method (ADI), which exploit the characteristic of structured grid. The direct and iterative solvers are tested with matrices from the simulation of a flow channel using DG method. The grid dimension is 6×2×2. The results show that direct solver performs better on these small matrices. However, the iterative solver using ADI method demonstrates better scalability with respect to the degree of polynomial used in DG scheme. This work advances the development of linear solver for DG method.

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

The numerical simulation of radio frequency devices is addressed in this dissertation, including the methods in frequency domain and time domain. In frequency domain, an embedded domain decomposition method (DDM) is presented herein for solving electromagnetic (EM) problems with complex geometries. In the method, the original computational domain is decomposed into a background subdomain and multiple embedded subdomains. The subdomain problems are easier to solve than the original problem. Furthermore, the shapes of the subdomains can be geometrically non-conformal, and the discretizations in the subdomains are allowed to be completely independent. Information exchange between the subdomains are addressed with respect to four ingredients: field continuity, material difference, perfect electrical conductor (PEC), and port. Field continuity on the subdomain boundaries is weakly enforced by employing Robin transmission condition. Modified volume sources are introduced to account for the material difference between the subdomains. For PEC and port, surface currents on them in the embedded subdomains are impressed into the background subdomain in a proper way. The numerical properties of the proposed DDM, such as accuracy and convergence, are well demonstrated through several examples. Furthermore, we illustrate its usefulness and flexibility via several engineering problems of practical interest, including the applications in electromagnetic compatibility (EMC), antenna design, and integrated circuit (IC) analysis. With the benefits of embedded meshes, the modification of one subdomain will hardly affect the discretization and matrix computation of another subdomain. As a consequence, the introduced method offers a high degree of flexibility in modeling and simulation, and facilitates moving/replacing/adding objects in a background problem straightforwardly. Such feature and flexibility would be desirable in practical and industrial applications, especially for s (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), University of Dayton, 2018, Electrical and Computer Engineering

This dissertation investigates the propagation of all electromagnetic fields inside anisotropic metamaterials using the Berreman 4 x 4 matrix method. Specifically, the Berreman matrix is used to derive the forward and backward propagating electric fields inside anisotropic metamaterials for all polarizations. Results from the Berreman method are compared with those obtained from the transfer matrix method and finite element methods. Examples include transmissivity and reflectivity as a function of wavelength and angle of incidence for multi-layer metallo-dielectric stacks and dielectric-phase change material stacks, modeled using effective medium theory for Berreman matrix calculations. It is shown that the Berreman matrix method used along with effective medium theory provides a fast and reliable estimate of the optical characteristics of the composite material. The Berreman technique also readily leads to the transfer function matrix for beam propagation in anisotropic materials. The eigenvalues of the Berreman matrix, which determine the transfer function, depend on the anisotropy. Beam propagation in anisotropic materials is analyzed both theoretically and numerically. It is shown that for transverse magnetic polarization, self-lensing of beams occur in a hyperbolic metamaterial. Finally, the transmission coefficient, which is a function of the spatial frequency, is used to determine the spatial shifts of beams propagating through anisotropic metamaterials. Again, for transverse magnetic polarization, negative refraction is observed. The results should prove useful for the analysis of arbitrary beam profiles through composite metamaterials.

PhD, University of Cincinnati, 0, Engineering and Applied Science: Mechanical Engineering

Accurate prediction of structural responses under combined, extreme environments often involves a wide range of spatial and temporal scales. In the traditional analysis of structural response problems, time dependent problems are generally solved using a semi-discrete finite element method. These methods have difficulty simulating high frequency ranges, long time durations, and capturing sharp gradients and discontinuities. Some limitations include time step constraints or a lack of convergence. The space-time finite element method based on time-discontinuous formulation extends the discretization into the temporal domain and is able to address some of these concerns. The constraints on the time-step are relaxed and the method has had some success in accurately capturing sharp gradients and discontinuities. For applications featured by multiscale responses in both space and time, the regular space-time finite element method is unable to capture the full spectrum of the response. An enriched space-time finite element method is proposed based on a coupled space-time approximation. Enrichment is introduced into the space-time framework based on the extended finite element method (XFEM). The effects of continuous enrichment functions are explored for high frequency wave propagation. Previous works are based primarily on enrichment in time. Numerical solvers are developed and benchmarked for the space-time system on high-performance platform. The method's robustness is demonstrated by convergence studies using energy error norms. Improvements are observed in terms of the convergence properties of the enriched space-time finite element method over the traditional space-time finite element method for problems with fine scale features. As a result, enrichment may be considered an alternative to mesh refinement. The numerical instability associated with the high condition number of the enriched space-time analogous stiffness matrices is studied. The factors affecting the (open full item for complete abstract)

Master of Science (MS), Ohio University, 1988, Mechanical Engineering (Engineering)

Analysis of rolling

Master of Science (MS), Ohio University, 1999, Mechanical Engineering (Engineering)

Formulation of steady-state and transient potential problems using boundary elements

Doctor of Philosophy, University of Akron, 2009, Mechanical Engineering

Efficient modeling of pressure drop, flowfield, particle capturing and sensing is needed for low Reynolds number micro- and nano-scale filtration flows with unstructured nets of multi-diameter fibers. For computational modeling of two- and three-dimensional partial-slip flows, a Stokeslet-based boundary singularity method (BSM) was developed in this study. For transition flows, the coupling of the BSM with the Direct Simulation Monte Carlo (DSMC) was proposed and developed. First, regular Stokeslets, regularized Stokeslets located at the boundary of the flow domain and submerged Stokeslets located outside of the flow domain were employed and compared with respect to the accuracy of the numerical results for three-dimensional flows. It was shown in the current study that the use of submerged Stokeslets removed the inaccuracy of pressure and velocity at the particle surface, typical for the prior approaches. Computations were then conducted using the proposed BSM for representative sets of spheres in translational and rotational motions in the partial-slip regime. The cases considered are typical for particles' capturing and sensing in filtration. The partial slip at the particles' surface appeared to affect significantly the velocity field and pressure distribution. In a more broad sense, the applications of the proposed methodology include assembly of particles under action of electromagnetic forces, synthesis of nano-particles and nanotubes, and micro-pumping, to name a few applications. Second, the BSM for two-dimensional partial-slip flows was developed and applied to partial-slip filtration flows. The numerical accuracy was investigated in terms of the location and the number of the Stokeslets for the benchmark study of the flow past an infinite long cylinder. It was shown that the numerical accuracy did not deteriorate with the dramatic decrease in the number of Stokeslets as long as the Stokeslets were sufficiently submerged. A relatively small number of Sto (open full item for complete abstract)

Doctor of Philosophy, University of Akron, 2024, Mechanical Engineering

For CMCs with an electrically conductive matrix, direct current potential drop techniques have the potential to detect composite state such as conductive constituent content (e.g., Si in melt infiltrated composites) or local defects such as delamination or porosity. Melt-infiltrated SiC based composites are an ideal candidate material for such to verify this since the Si content of the matrix is the primary current carrier in the system. In our study, we aimed to evaluate the effectiveness of Electrical Re-sistance (ER) as a NDE method for different 2D woven SiC-based Melt-infiltrated composites, each exhibiting varying degrees and types of processing defects. We con-ducted four types of ER measurements: a. Bulk Resistivity b. Through-thickness c. Axial d. Surface, along the length of the dogbone specimens in the gauge section and on a large plate. Microstructural analysis was performed to correlate observations with microstructure. The bulk resistivity of the specimens in our study exhibited a linear correlation with the infiltrated Si content of the matrix even with different percentage and type of porosities present, allowing us to comment on Si-content of the speci-mens. The Through-thickness set-up incorporates current leads to supply current in a through-thickness manner and determine the nature of current spreading (voltage drop) some distance   away from the current source. The absolute values of the measured through thickness potential represent Si-content, but it is unresponsive for processing defects. The axial set-up is more conventional and can generate local axial current flow. In some cases, such flow of current is affected by and able to locate the local and distinct type porosi-ty. Resistance was sensitive for regions of poor Si-infiltration i.e., “dry-slurry” type defects as well as for isolated larger rounded porosity. It was very sensitive to local surface porosity but not as sensitive for cases where porosity was homogenously pre-sent throug (open full item for complete abstract)

Master of Arts (MA), Bowling Green State University, 2024, Mathematics/Mathematical Statistics

Interface problems frequently appear in numerous physical, biological, and scientific contexts. These problems usually involve differential equations where the input data have discontinuities at one or more interface positions within the solution domain. In this thesis, a numerical method for solving one-dimensional elliptic and parabolic problems with linear and non-linear interface jump conditions at a single interface position is presented. To effectively solve these interface problems, we integrate jump conditions into the numerical method, ensuring that these conditions are met at the interface position. Our approach combines finite difference schemes with a technique termed the $a$-method, specifically devised to address the complexities associated with non-linear interface jump conditions. The convergence behavior of these methods in numerically solving elliptic and parabolic problems with linear interface jump conditions is also examined. Through our numerical examples, we demonstrate that our proposed method achieves an approximate first-order convergence. This occurs because the non-interface grid points exhibit second-order accuracy, while the interface points achieve only first-order accuracy, thereby lowering the overall convergence order to first order when evaluated using the maximum norm criterion.

Master of Computing and Information Systems, Youngstown State University, 0, Department of Computer Science and Information Systems

Bootstrap sampling serves as a cornerstone in statistical analysis, providing a robust method to evaluate the precision of sample-based estimators. As the landscape of data processing expands to accommodate big data, approximate query processing (AQP) emerges as a promising avenue, albeit accompanied by challenges inaccurate assessment. By leveraging bootstrap sampling, the errors of sample-based estimators in AQP can be effectively evaluated. However, the implementation of bootstrap sampling encounters obstacles, particularly in the computation-intensive resampling procedure. This thesis embarks on an exploration of various resampling methods, scrutinizing five distinct approaches: On Demand Materialization (ODM) Method, Conditional Binomial Method (CBM), Naive Method, Two-Step Poisson Random (TSPR), and Two-Step Poisson Adaptive (TSPA). Through rigorous evaluation and comparison of the execution time for each method, this thesis elucidates their relative efficiencies and contributions to AQP analyses within the realm of big data processing. Furthermore, this research contributes to the broader understanding of resampling techniques in statistical analysis, offering insights into their computational complexities and implications for big data analytics. By addressing the challenges posed by AQP in the context of bootstrap sampling, this thesis seeks to advance methodologies for accurate assessment in the era of big data processing.

PHD, Kent State University, 2022, College of Arts and Sciences / Department of Computer Science

Identifier names are an intrinsic part of the software and more importantly, program comprehension. They are the primary source of information programmers use to acquire knowledge about source code. There are many ways to improve the comprehension of software, but one crucial way is to improve the quality of names used inside the source code. High-quality identifiers play an essential role in increasing productivity, and according to the literature, high-quality identifier names save a significant amount of time and costs during software evolution. The dissertation comprehensively examines factors for assessing the quality of method names in source code. Ten method naming standards are proposed and evaluated by +1100 software engineering professionals. The various standards for source code method names are derived from and supported in the software engineering literature. The large-scale evaluation results in a consensus among developers that the standards are accepted and used in practice. Factors such as years of experience and programming language knowledge are also considered. The dissertation also presents an approach and a tool to automatically assess the quality of method names by providing a quality rate and feedback about the flaw in a name. The approach implements the ten method naming standards to evaluate a given method name. Natural language processing techniques such as part-of-speech tagging, identifier splitting, and dictionary lookup are used to implement the standards. A large golden set of method name quality ratings is developed. Each method name is rated by several experienced developers and labeled as conforming to each standard or not. These ratings allow comparing the results of the proposed approach against expert assessment. The golden set is used to evaluate the approach. The approach is also applied to several systems written in different programming languages, and the results are manually inspected for accuracy. The final resul (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2022, Biostatistics

Genome-wide association studies (GWAS) have identified a large number of genetic variants associated with human traits or diseases. Despite these successes, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods in GWAS are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. My dissertation research focuses on developing multi-trait methods for GWAS and I have developed two multi-trait methods. The first one, multi-trait adaptive Fisher's method (MTAF), is a multi-trait method using individual-level genotype data. It combines p-values adaptively and showed robust power performance in simulation studies. We applied MTAF to a dataset --- Study of Addiction: Genetics and Environment (SAGE) and identified genes associated with substance addiction. In contrast to individual-level genotype, GWAS summary statistics are usually more readily available publicly, and thus methods using only summary statistics have greater usage. Therefore, in the second part of the thesis, we developed MTAFS, an adaptive and robust method for multi-trait analysis of GWAS using summary statistics. In addition to its robust performance, its computational efficiency is greatly improved compared with MTAF by avoiding permutations. MTAFS was applied to a brain imaging dataset from the UK biobank and identified genes associated with brain functions.

Master of Science (M.S.), Xavier University, 2021, Psychology

In a study conducted by Shore et al. (2018), two management methods that aimed to promote diversity and inclusion were described: the management prevention orientation and the management promotion orientation. The management prevention orientation focuses on complying with employment law and avoiding litigation, whereas the management promotion orientation focuses on embracing and maintaining diversity and inclusion by adhering to six themes of workplace inclusion (feeling safe, involvement in the workgroup, feeling respected and valued, influence on decision making, authenticity, recognizing, honoring, and advancing of diversity). Using hypothetical vignettes, this study examined how inclusive and sincere minorities perceived the management prevention orientation to be compared to the management promotion orientation. It was hypothesized that minorities would find the management promotion orientation more inclusive and more sincere compared to the management prevention orientation. Using a sample of 79 minorities recruited via MTurk, the study's hypotheses were supported. These findings confirm that the six themes of workplace inclusion described by Shore et al. seem to be a reflection of an inclusive climate, and that sincerity matters when it comes to favorable perceptions of a management style. A noteworthy result was that most participants who met this study's requirements for being considered minorities did not self-identify as minorities, implying that although people may be considered a minority by others, they may not necessarily self-identify as a minority. Future research should explore more ways to determine who should be considered a minority, as well as further examine if the term “minority” may be outdated.