Doctor of Philosophy, Case Western Reserve University, 2004, Systems and Control Engineering

Cruse's model of leg coordination (CCM) was derived to account for gaits and gait transitions in arthropods (analogous to, e.g. walktrotgallop in some quadrupeds). It has also been adapted to control locomotion in a series of hexapod robots. CCM is a systems-level, kinematic model that abstracts key physiological and dynamical properties in favor of tractability. A key feature is that gaits emerge from interaction among pairs of legs as effected by a set of coordination mechanisms acting at discrete points in time. We represent CCM networks as systems of coupled hybrid oscillators. Gaits are quantified by a temporal (discrete) phase vector. System trajectories are polyhedral, hence solvable over finite-time, but the presence of the switching automaton renders infinite horizon properties harder to analyze. Via numerical and symbolic simulations, we have mapped out the synchronization behavior of CCM networks of various topologies parametrically. We have developed a section-map analysis approach that exploits the polyhedral geometry of the hybrid state space. Our approach is constructive. Once switching boundaries are appropriately parameterized, we can extract periodic orbits, their domains of admissibility and stability, as well as expressions for the period of oscillation and relative phase of each cycle, parametrically. Applied to 2-oscillator networks, our approach yields excellent agreement with simulation results. A key emergent concept is that of a virtual periodic orbit (VPO). Distinguished from admissible periodic orbits, VPOs do not correspond to any in the underlying hybrid dynamics. However, when stable and close to being admissible, they are canonical precursors for a class of nonsmooth bifurcations and predictive of long transient behavior. Last, we take into consideration the possibility and difficulties of extending our approach to larger networks and to related oscillator-like hybrid dynamical systems with polyhedral trajectories.

Committee: Randall Beer (Advisor) Subjects:

Master of Science, The Ohio State University, 2013, Electrical and Computer Engineering

Intelligent Transportation Systems (ITS) apply well-established technologies in communications, control, and computer hardware and software to increase safety and improve operational performance of the transportation network without expanding the current infrastructure. For many ITS applications, ensuring safety of the traffic participants, including drivers and pedestrians, is one of the most important research initiatives of the Intelligent Transportation Systems Society (ITSS). The ITS applications range from collision avoidance for autonomous or human-driven vehicles to cooperation of multiple vehicles to achieve common goals such as reduced fuel consumption or increased traffic throughput. The main challenges when designing controllers for such systems are the need to consider the close combination of, and coordination between, the system's computational and physical elements. Most of the vehicles nowadays are controlled by tens of or even hundreds of microcontrollers, which communicate via a CAN bus, for electric steering, braking, chassis and body control. Moreover, vehicles interact with other traffic participants including (semi) autonomous vehicles and human-driven cars and also with roadside units through a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication, resulting in a large-scale Cyber-Physical System. Thus, traditional control theory that has been devoted to modeling continuous systems cannot adequately model such complex Cyber-Physical Systems, where both continuous (physical plant, e.g., vehicle) and discrete components (computing and communication) closely interacting each other. This thesis studies the design of continuous control laws that satisfy the safety property of the systems and their interfaces with discrete components that abstract human's high-level, decision making process. Our primary goals are to design continuous controllers for ITS applications that by design guarantee the safety property without fur (open full item for complete abstract)

Committee: Umit Ozguner Ph.D. (Advisor); Wei Zhang Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2008, Mechanical Engineering

The gradual decline of oil reserves and the increasing demandfor energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems. Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability. In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation. Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system. To address the unique characteristics of the HEV driv (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Yann Guezennec PhD (Committee Member); Andrea Serrani PhD (Committee Member); Steve Yurkovich PhD (Committee Member) Subjects: Mechanical Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2008, Computing and Information Science

In this thesis, we demonstrate techniques to improve upon the Rapidly-exploring Random Tree (RRT) as a tool for planning and verification of hybrid systems. First, we perform experiments that show many planning/verification problems exhibit heavy-tailed behavior, where sampling-based algorithms sometimes require an inordinate number of nodes to solve them. We show that using restarts and multiple trees improves their solution time. Second, we note that for many complex planning/verification problems the hybrid state space admits a natural separation into distinct modes, such that search in one does not help find a path through any other. We use a forest of trees, each tasked with solving a specific mode, to find overall solutions more quickly and with fewer nodes. Third, we solve problems with unpredictable environment changes (because of other agents, unmodeled dynamics, or disturbances) using receding horizon search, where a new path is generated whenever the current path is invalidated.

Committee: Michael Branicky (Advisor); Guo-Qiang Zhang (Committee Member); M. Cenk Cavusoglu (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Iterative learning control (ILC) has been growing in applicability, along with growth in theory for classes of linear and nonlinear systems. The current study extends the theory of ILC to hybrid systems, primarily motivated by the need to develop efficient automated procedures for the calibration of gearshift controllers. A lifted form representation of hybrid systems with input-output dependent switching rules is developed, and the proposed lifted form representation used for control design. Causality of hybrid systems in the time domain results in a (lower) triangular structure of hybrid Markov matrices in the trial domain, the triangular structure enabling systematic and efficient control design. Specifically, a solution to the required set of linear matrix inequalities (LMIs) is guaranteed to exist under mild assumptions, which is in contrast to many other studies proposing LMI based solutions in general controls literature. In addition to extending the theory of ILC to hybrid systems, and developing systematic design methods for computation of the required learning controllers, ILC of linear and hybrid systems with uncertain trial duration and linear and hybrid systems with shape-constrained control inputs, which often result from the parameterization of feedforward control inputs using look-up tables, is also studied. Robustness to system uncertainty is explicitly incorporated using the interval systems formulation, and robust learning controllers are designed for linear and hybrid systems. In addition, for ILC of systems with large variations in the operating conditions such as the initial conditions and/or external forces, a novel idea of parametric learning is introduced, the resulting ILC being termed as parametric-ILC or P-ILC. The design methods presented for the computation of learning controllers are first validated numerically for several motion control applications, and then are used for developing automated procedures for the calibration of ge (open full item for complete abstract)

Committee: Krishnaswamy Srinivasan (Advisor); Giorgio Rizzoni (Committee Member); David Hoelzle (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

The Ohio State University has participated in Advanced Vehicle Technology Competitions (AVTCs) for over 21 years. These competitions challenge universities throughout North American to reengineer a vehicle with technologies advancing the automotive market. This work explores the use of systems engineering practices during the eleventh iteration of the AVTC program, the EcoCAR 3 competition. The document presents the systems engineering process and two case studies implementing the process. The systems engineering process presented is a simplification of the “Vee” and “Agile” systems engineering processes applicable to a high-cost, long-term, prototype program. The process is broken into five stages: Concept Creation and Refinement, Architecture and Metric Creation, Development, Verification, and Assessment and Validation. The two case studies present uses of the process at a low-level applied to a software algorithm and at a high-level applied to an entire project. The first case study reviews the development of a diagnostic algorithm for the automated manual transmission used in the EcoCAR 3 competition vehicle. The team automated a manual transmission and needed an algorithm to detect and isolate failures to components of the transmission system. The concept and requirements for this algorithm are detailed in Chapter 1 before continuing to discussion of development and testing. Testing of the algorithm utilizes a model-based environment. The second case study reviews the construction and execution of a behavioral study project evaluating driver performance during a vehicle to driver transition of an SAE Level 3 partially automated vehicle. Research was conducted in a model-based environment, simulating an autonomous vehicle by utilizing a driving simulator. The project requirements are derived from the applicable parent requirements, implemented, and tested.

Committee: Shawn Midlam-Mohler Ph.D. (Advisor); Giorgio Rizzoni Ph.D. (Advisor); Lisa Fiorentini Ph.D. (Committee Member); Sandra Metzler Ph.D. (Committee Member) Subjects: Electrical Engineering; Engineering; Mechanical Engineering

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

Dynamic systems incorporating physical plant and control systems should be designed in an integrated way to yield desirable and feasible solutions. Conventionally, these systems are designed in a sequential manner which often fails to produce system-level optimal solutions. However, combined physical and control system design (co-design) methods are able to manage the interactions between the physical artifact and the control part and consequently yield superior optimal solutions. Small-scale to moderate-scale dynamic systems can be addressed by using existing co-design methods effectively; nonetheless, these methods can be impractical and sometimes impossible to apply to large-scale dynamic systems which may hinder us from determining the optimal solution. This work addresses this issue by developing a new algorithm that combines decomposition-based optimization with a co-design method to optimize large-scale dynamic systems. Specifically, the new formulation applies a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) for the co-design of a representative large-scale dynamic system consisting of a plug-in hybrid-electric vehicle (PHEV) powertrain. Moreover, since many of dynamic systems may consist of several time-dependent linking variables among their subsystems, a new consistency measure for the management of such variables has also been proposed. To validate the accuracy of the presented method, the PHEV powertrain co-design problem has been studied with both simultaneous and ATC methods; results from the case studies indicate the new optimization formulation's ability in finding the system-level optimal solution.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Sam Anand Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2004, Engineering : Computer Science and Engineering

Hybrid Intelligent Systems (HIS) combine intelligent techniques in synergistic architectures in order to provide solutions for complex problems. These systems utilize at least two of the three techniques: fuzzy logic, genetic algorithms and neural networks. The goal of their combination is to amplify their strengths and complement their weaknesses. Research in hybrid intelligent systems primarily focuses on the integration and interaction of different techniques rather than merging different methods to create new techniques. However, it is not always obvious or easy to build HIS architectures that achieve the higher intelligence goal. A good architecture for a hybrid system should match each of its tasks to the appropriate intelligent technique and provide an efficient means for their integration. Classification of signal patterns represents a complex problem due to the voluminous nature of signal patterns. A signal pattern is the combination of a large sequence of values of one or more variables collected over a period of time. Noise is an intrinsic component to all signal pattern applications. Current classification methods are inadequate in classifying large sets of noisy signal patterns. We selected this problem as the target for our new HIS architecture. In this research we developed a new HIS architecture for classifying large sets of signal patterns. Our Hybrid Intelligent Signal Pattern Classifier (HISPC) has demonstrated superior performance with low classification cost and great flexibility on synthetic and real life large data sets. An equally important objective of this research is the study of the software engineering aspect for developing this architecture. A new software engineering process and a set of design and implementation principles were developed in this work. These tools are applicable to the development of any experimental software system.

Committee: Dr. Carla Purdy (Advisor) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

This thesis studies the Hybrid-State System models and their properties for different pieces of the urban autonomy problem. For autonomous vehicles that operate in real-life, mixed-mode traffic, a number of parallels between the human-driven system and the autonomous counterpart can be identified and captured in the hybrid-state system setting. For the control subproblem of the urban autonomy, this thesis proposes a system architecture, related design approaches for autonomous mobile systems and guidelines for self-sufficient operation. Development of a tiered layout for a hybrid-state control in a series of stages as well as the integration of such a controller in the overall autonomy structure are proposed and demonstrated as part of multiple examples, including The Ohio State University participation in Defense Advanced Research Projects Agency Urban Challenge 2007. The hierarchical layout and the iterative design methodology enable design flexibility through compartmentalization of the overall system and helps prepare for various contingencies, as illustrated on specific development cycles. The sensing and perception part of the autonomy implementation relies on a probabilistic hybrid-state system modelling method that is developed for driver-behavior analysis and prediction. The model fits into and captures the central modules of the existing Human Driver Model. The stochastic models, based on the observable actions of the driver/vehicle interaction, are useful in representing the behavior of human-driven vehicles in certain urban decision-making scenarios. The Driver Intention Estimator presented utilizes the developed stochastic models to detect and predict high-level, abstract decisions of observed drivers through traffic scenarios and it can be expanded to form scenario safety estimation tools as demonstrated. As for the analysis of the developed estimators and as a useful tool for hybrid-state systems in general, this study develops an encoding scheme for (open full item for complete abstract)

Committee: Umit Ozguner PhD (Advisor); Ashok Krishnamurthy PhD (Committee Member); Keith A. Redmill PhD (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

Due to the increased penetration of inverter-based resources (IBRs) in bulk power system (BPS) networks, to conduct interconnection studies, generic dynamic mod- els of the second-generation renewable energy system models were developed by the Western Electricity Coordinating Council (WECC) Renewable Energy Modeling Task Force. The dynamic models have been extensively implemented in various power system simulation software packages, and the block diagram representation of the dynamic models is widely present in various technical reports and literature. However, there is a gap between the mathematical model and knowledge of key parameters for the second-generation renewable energy system dynamic models. The complex nonlinear nature of the dynamic models makes it highly challenging for the transmission planning engineers to identify the key parameters when the IBRs are subjected to large-scale voltage and frequency disturbances. This is needed to ensure grid stability under contingencies. For instance, the Type 3 wind turbine generator (WTG-3) model consists of 7 modules with 118 user-defined parameters, interfaced through 26 states and 9 control flags to facilitate the plant operation in different control modes. Thus, this work presents a methodology for the key parameter identification in non- linear models of power systems. The proposed methodology is applied to identify the key parameters of the transmission-scale IBRs (solar PV power plants, wind power plants, and battery energy storage system plants) dynamic models using proposed global sensitivity analysis techniques. It fills up the gap regarding the requirement of the mathematical model and knowledge of key parameters. In contrast to the state-of-the-art methods, the proposed modified Morris, modified Sobol', and modified eFAST sensitivity analysis techniques do not linearize the dynamic models of IBRs around an operating point, providing critical insights into the large-signal stability analysis. The (open full item for complete abstract)

Committee: Mahesh Illindala Dr. (Advisor); Xin Feng Dr. (Committee Member); Jin Wang Dr. (Committee Member); Antonio Conejo Dr. (Committee Member) Subjects: Electrical Engineering; Energy

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

The success of modern digital electronics relies on compartmentalizing logical functions into individual gates, and controlling their order of operations via a global clock. In the absence of such a timekeeping mechanism, systems of connected logic gates can quickly become chaotic and unpredictable -- exhibiting analog, asynchronous, autonomous dynamics. Such recurrent circuitry behaves in a manner more consistent with neural networks than digital computers, exchanging and conducting electricity as quickly as its hardware allows. These physics enable new forms of information processing that are faster and more complex than clocked digital circuitry. However, modern electronic design tools often fail to measure or predict the properties of large recurrent networks, and their presence can disrupt other clocked architectures. In this thesis, I study and apply the physics of complex networks of self-interacting logic gates at sub-ns timescales. At a high level, my unique contributions are: 1. I derive a general theory of network dynamics and develop open-source simulation libraries and experimental circuit designs to re-create this work; 2. I invent a best-in-class digital measurement system to experimentally analyze signals at the trillionth-of-a-second (ps) timescale; 3. I introduce a network computing architecture based on chaotic fractal dynamics, creating the first `physically unclonable function' with near-infinite entropy. In practice, I use a digital computer to reconfigure a tabletop electronic device containing millions of logic gates (a field-programmable gate array; FPGA) into a network of Boolean functions (a hybrid Boolean network; HBN). From within the FPGA, I release the HBN from initial conditions and measure the resulting state of the network over time. These data are transferred to an external computer and used to study the system experimentally and via a mathematical model. Existing mathematical theories and FPGA simulation tools produce in (open full item for complete abstract)

Committee: Daniel Gauthier (Advisor); Emre Koksal (Committee Member); Gregory Lafyatis (Committee Member); Antonio Boveia (Committee Member) Subjects: Applied Mathematics; Computer Engineering; Computer Science; Condensed Matter Physics; Electrical Engineering; Electromagnetics; Electromagnetism; Engineering; Experiments; High Temperature Physics; Information Science; Information Systems; Information Technology; Low Temperature Physics; Materials Science; Mathematics; Medical Imaging; Nanotechnology; Particle Physics; Physics; Quantum Physics; Scientific Imaging; Solid State Physics; Systems Design; Technology; Theoretical Physics

Master of Science, The Ohio State University, 2021, Mechanical Engineering

This work presents a neural network (NN) based adaptive feedback regulator to ensure the lateral and longitudinal stability and regulate the desired walking velocity of a lower-limb exoskeleton under model uncertainty. The traditional model-based controllers for lower-limb exoskeletons often fail to stabilize the robot or accurately track the desired behaviors under model uncertainties or external disturbances. This paper proposes a neural network (NN) based online adaptive regulator that compensates for the unknown changes in model parameters and external disturbances by modifying the nominal joint trajectory. A gradient descent-based delta rule is implemented to update the weights of a single layer NN, which can be efficiently performed online by design. We demonstrate the performance of the presented regulator on ATALANTE, a fully actuated lower limb exoskeleton designed for paraplegic patients. The simulation results show that the proposed approach noticeably improves stability and the tracking performance of the system, despite significant changes in model parameters and large adversarial pushes.

Committee: Ayonga Hereid Dr. (Advisor); Haijun Su Professor (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2019, Electrical and Computer Engineering

This thesis addresses the design of feedback controllers for biped robots using techniques from the control theory and machine learning. A novel approach that combines Hybrid Zero Dynamics (HZD) and Reinforcement Learning (RL) is proposed to realize feedback controllers that achieve robust and stable walking limit cycles while tracking variable desired speed. The design of feedback controllers for bipedal locomotion is challenging due to the hybrid nature of its dynamics and the complexity imposed by high-dimensional bipedal models. Existing RL approaches for bipedal walking are inefficient as they do not consider the underlying physics, often requires substantial training, and the resulting controller may not be applicable to real robots. HZD is a powerful tool for bipedal control with local stability guarantees of the walking limit cycles. In this thesis, we propose a sample-efficient, non-traditional RL structure that embeds the HZD framework into the policy learning process. More specifically, we propose to use RL to find a control policy that maps from the robot's reduced order states to a set of parameters that define the desired trajectories for the robot's joints through the virtual constraints. Then, these trajectories are tracked using an adaptive PD controller. The proposed method is validated through simulation of RABBIT, a well-know biped robot used as a testbed for advanced control techniques. The simulation is implemented using OpenAI's Gym with the MuJoCo physics engine. The result is a stable and robust control policy that is able to track variable desired speed within a wide interval. To keep the tracking performance of higher speeds the controller is able to get the robot into a running phase. Robustness of the policy is evaluated by applying external forces to the torso of the robot and comparing its performance with a traditional HZD-based controller.

Committee: Wei Zhang Ph.D. (Advisor); Lisa Fiorentini Ph.D. (Committee Member) Subjects: Electrical Engineering; Robotics

Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering

The advent of SiC based switch technology has led to high efficiency, low weight power electronics. However, these switches lack the maximum current ratings of their Si predecessors, making them ill-suited for single-switch, high-current applications. Hybrid switches place traditional Si devices in parallel with SiC devices to obtain high efficiency while also maintaining a high current limit. To do so, hybrid switches need to be carefully selected based on the demands of the design, using datasheet values from both switches. Hybrid switches also need a control scheme that can safely and efficiently operate the pair of switches. This control scheme takes advantage of zero voltage switching to control devices with higher switching loss at approximately zero voltage, minimizing switching loss. To protect SiC devices, multiple zones of switching are established based on maximum current ratings of the devices, with the switching scheme changing based on the zone. Various switch pairs are tested for conduction losses, switching losses, and zone 2/ zone 3 control in both a DC-DC converter and a DC-AC inverter. Switch efficiency and power density are calculated based on these values to determine the advantages and disadvantages of using hybrid switches in a specific project.

Committee: Julia Zhang (Advisor); Mahesh Illindala (Committee Member) Subjects: Electrical Engineering; Energy; Engineering

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

Performing numerical optimization in large scale simulations environments is complicated by the fact that the overall objective function might be too computationally intensive or impossible to define in its closed form. In these cases, simulation-based optimization algorithms, which do not need the exact closed form objective function are the only viable solution method. Derivative Free Optimization algorithms are one such class of algorithms that does not need the derivative of the objective function in order to find the optimum. They instead use function evaluations to traverse the search space. This dissertation addresses the optimization challenges of large scale simulators that do not lend themselves to gradient based optimization. While the field of simulation-based optimization has been in existence for a few decades, the growing complexity of models in recent years puts a focus on the field to provide effective strategies to efficiently perform the required optimization. The difference between simulations and the real world systems they represent is that simulations use assumptions. It is important that these assumptions are within an acceptable tolerance which enable them to model reality with an appropriate level of certainty, within a reasonable amount of time, and using limited computational resources. Simulators use various ways to simplify reality and one way this is done is through the use of look-up tables (LUT). A look up table is an matrix that enables complicated computation to be replaced with relatively simpler array indexing. Finding optimal solutions to simulators which use LUTs is complicated by LUTs being discrete and event based. In addition, most simulation models that are used to model decision making mechanisms such as embedded control systems consist of both discrete and continuous state dynamics. These hybrid system models need both the discrete and continuous state dynamics to be analyzed and optimized simultaneously. This disser (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Yingbin Liang (Committee Member); Abhishek Gupta (Committee Member); Tunc Aldemir (Committee Member) Subjects: Computer Engineering; Electrical Engineering; Mechanical Engineering

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

This research investigates the quasi-static and dynamic properties of flexible hybrid electronic (FHE) material systems. FHE material systems are multifunctional systems combining tunable mechanical and electrical behavior and are constructed from the combination of FHEs and elastomeric metamaterial inspired geometries. FHEs are composed of conductive flakes embedded within an elastomer matrix such that electrical conductivity is determined by conductive flake proximity in a percolating network. Therefore, FHE conductivity is highly dependent on strain whereby large strains increase electrical resistance through the separation of conductive flakes. On the other hand, elastomeric metamaterials have been leveraged for energy mitigation purposes through control over strain transfer properties. The strain transfer properties of elastomeric metamaterials arise from built-in or applied geometric features, which capitalize on snap-through buckling or gradual collapse behaviors. By the innovative integration of concepts here, FHE material systems control electrical conductivity through the strain-transfer properties of the elastomeric metamaterial inspired geometries. In quasi-static experiments, conductive ink trace path choice within specimen geometry determines FHE electrical behavior through the strain transfer characteristics of the geometry under compression. A strain-sensitivity of FHEs to internal strain transfer is leveraged in a geometry with snap-through buckling features, which reveals electrical behavior useful for load or buckling event detection. On the other hand, FHE ink trace path choices within geometries with gradual collapse behavior can maintain a nearly constant conductivity due to a strain-insensitivity of the FHEs. FHE material systems are then investigated under high frequency dynamic excitations to investigate transient electrical resistance changes. It is found that the static strain within the conductive networks of the strain-sensitive and stra (open full item for complete abstract)

Committee: Ryan Harne (Advisor); Rebecca Dupaix (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Plant Pathology

Numerous diseases affect soybean [Glycine max (L.) Merr] yields throughout the growing season in Ohio. Two soil borne pathogens Fusarium graminearum and Phytophthora sojae are known to reduce stand and yield. Currently, fungicide seed treatments are used to manage these pathogens, however, host plant resistance is often the best management strategy for field crops. Thus, the overall objective of the five chapters this dissertation was to identify mechanisms and candidate genes of resistance that are effective towards seed, seedling, and root rots caused by Fusarium graminearum and P. sojae in soybean. Quantitative disease resistance loci (QDRL) have been mapped in two separate recombinant inbred line (RIL) populations for resistance to Fusarium graminearum . In the F7:8 RIL derived from a cross Magellan X PI 567516C, one major QDRL was mapped. Fine mapping of this region identified four putative candidate genes for resistance to Fusarium graminearum . In an additional population of Wyandot x PI 567301B, a major and minor QDRL was mapped to chromosome 8 and 6, respectively. Hybrid genome assembly, fine mapping, and RNA sequencing analysis narrowed the major QDRL to 2.5 cM containing three putative candidate genes for resistance or susceptibility. To validate these candidate genes functional analysis needs to be assessed at the seed level. To achieve this we modified the Apple latent spherical virus (ASLV) which allowed for direct inoculation of VIGS-triggering ALSV agro-infiltrated Nicotiana benthamiana leaves onto soybean unifoliates. However, this method is genotype dependent; the virus is detected in numerous reproductive structures including pods, embryos, stems, leaves, and roots. The last objective of this dissertation focuses on mechanisms of partial resistance to Phytophthora sojae . This oomycete is a leading pathogen of soybean, causing root and stem rot (PRR) across the North Central Region in the U.S. Twenty phenotypic quantitative trait loci ( (open full item for complete abstract)

Committee: Anne Dorrance Dr. (Advisor); Leah McHale Dr. (Committee Member); Christopher Taylor Dr. (Committee Member); Feng Qu Dr. (Committee Member) Subjects: Genetics; Plant Biology; Plant Pathology; Plant Sciences

Master of Science in Mechanical Engineering (MSME), Wright State University, 2016, Mechanical Engineering

Gas turbine technology for aerospace applications are approaching limits in efficiency gains as increases in efficiency today occurs in very small increments. One limitation in conventional gas turbine technology is the combustion process, which destroys most of the exergy in the cycle. To address this limitation in a traditional Brayton power cycle, a hybrid system which is integrated with Solid Oxide Fuel Cell (SOFC) and gas turbine is developed. Hybrid systems involving fuel cells have better efficiencies than conventional power generation systems. Power generation systems with improved performance from low fuel utilizations and low maintenance costs are possible. The combination of a SOFC fuel cell with a gas turbine has shown higher efficiencies than conventional gas turbine systems due to the reduction of exergy destruction in the heat addition process. A one-dimensional dynamic model of a Solid Oxide Fuel Cell (SOFC) integrated with a gas turbine model to develop an efficient electrical power generation system for aviation applications is investigated. The SOFC - Combustor concept model was developed based on first principles with detailed modeling of the internal steam reformer, electrochemical and thermodynamics analysis is included. Initially, a detailed investigation of internal steam reformer kinetics is presented. The overall purpose of this thesis is to analyze the performance of the hybrid SOFC-GT system for both on-design and off-design operation in an aerospace application. Transient analysis is performed to understand the uncertainties in the SOFC temperatures and hybrid system; control and stability with sudden transient iii changes of the system (rapid throttle changes, environment changes like climb). Finally, SOFC model integrated with a compressor and turbine model and investigation on the overall performance of the innovative hybrid thermodynamic cycle is presented. The SOFC hybrid system has a lower power density at sea level compared to a (open full item for complete abstract)

Committee: Rory Roberts Ph.D. (Advisor); Mitch Wolff Ph.D. (Committee Member); Scott Thomas Ph.D. (Committee Member) Subjects: Aerospace Engineering; Mechanical Engineering

MS, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

The design of large-scale, complex systems such as plug-in hybrid electric vehicles (PHEVs) motivates the use of formal optimization methods from both multidisciplinary design optimization (MDO) and optimal control theory. Traditionally, MDO methods have been used to address the integrated design of engineering systems comprised of multiple, interacting components and/or disciplines for superior static system performance. Optimal control theory, on the other hand, is often used to select the best operation strategy of a given dynamic system for superior dynamic system performance. Although many times in practice the optimal design and control of such dynamic systems are addressed almost independently, this approach generally yields sub-optimal overall design solutions. This is because the system architecture, or physical design, is inherently coupled with its operation strategy, or control design. Combined optimal design and control techniques, also known as co-design, can address this issue by using an integrated approach to enable superior design solutions for dynamic systems. This thesis focuses on the co-design of large-scale systems, specifically PHEVs based on simultaneous multidisciplinary dynamic system design optimization (MDSDO) methods using direct transcription (DT). In order to enable a simultaneous approach for optimizing the design and control of the PHEV, a toolbox was developed to design all the critical component of a PHEV powertrain including: electric motor, generator, engine, transmission, and high voltage battery. This toolbox takes the size related design variables as inputs and by using the embedded analytical equations, generates the output performance characteristics of each component. The MDSDO problem formulation is then solved using GPOPS-II,a DT-based MATLAB software for solving multiple-phase optimal control problems. DT-based simultaneous problem formulations in MDSDO has already been successfully used in moderate scale problems, howe (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering; Mechanics