Master of Science (M.S.), University of Dayton, 2018, Aerospace Engineering

This work seeks to provide a proof-of-concept for the use of variable-fidelity (VF) kriging to approximate the lift and drag values for a complex hypersonic flight vehicle. Otherwise known as aerodynamic database generation within the aerospace engineering community, the force or moment experienced by a vehicle due to airflow, as a function of independent inputs such as flight speed or attitude, is approximated via some mathematical form. In the case of this work, VF kriging is implemented such that the vehicle response is interpolated directly through the points of high-fidelity (HF) simulation data while the trends of the response approximation are guided by low-fidelity (LF) information. High-fidelity simulations are implemented via the Euler flow computational software package Cart3D. The low-fidelity information is given by supersonic-hypersonic small-disturbance theory implemented in a surface pressure estimation code, developed specifically for this work for completely arbitrary body shapes represented by unstructured, triangular-cell surface meshes. The major contribution is a framework that connects the two fidelity levels to VF kriging routines to produce lift and drag approximations of arbitrary complex vehicles under hypersonic flight conditions. Assessment of the quality of the approximations is given by the root-mean-square error (RMSE) between the VF kriging surrogates and high-fidelity simulations performed over the same independent input domain. Results in two dimensions show that the use of VF kriging, to produce an interpolant as a function of angle-of-attack and Mach number, increases surrogate accuracy by nearly an order of magnitude for lift and by over twenty times for drag, when compared to ordinary kriging without variable-fidelity modeling. Three-dimensional surrogates, with input of angle-of-attack and two independent elevon control surface deflections, show roughly two and four times more accuracy for lift and drag, respectively, compared (open full item for complete abstract)

Committee: Markus Rumpfkeil (Advisor); Jose Camberos (Committee Member); Raymond Kolonay (Committee Member) Subjects: Aerospace Engineering; Applied Mathematics

MS, University of Cincinnati, 2014, Engineering and Applied Science: Aerospace Engineering

Air-breathing hypersonic vehicles have the potential to provide global reach and affordable access to space. Recent technological advancements have made scramjet-powered flight achievable, as evidenced by the successes of the X-43A and X-51A flight test programs over the last decade. Air-breathing hypersonic vehicles present unique modeling and control challenges in large part due to the fact that scramjet propulsion systems are highly integrated into the airframe, resulting in strongly coupled and often unstable dynamics. Additionally, the extreme flight conditions and inability to test fully integrated vehicle systems larger than X-51 before flight leads to inherent uncertainty in hypersonic flight. This thesis presents a means to design vehicle geometries, simulate vehicle dynamics, and develop and analyze control systems for hypersonic vehicles. First, a software tool for generating three-dimensional watertight vehicle surface meshes from simple design parameters is developed. These surface meshes are compatible with existing vehicle analysis tools, with which databases of aerodynamic and propulsive forces and moments can be constructed. A six-degree-of-freedom nonlinear dynamics simulation model which incorporates this data is presented. Inner-loop longitudinal and lateral control systems are designed and analyzed utilizing the simulation model. The first is an output feedback proportional-integral linear controller designed using linear quadratic regulator techniques. The second is a model reference adaptive controller (MRAC) which augments this baseline linear controller with an adaptive element. The performance and robustness of each controller are analyzed through simulated time responses to angle-of-attack and bank angle commands, while various uncertainties are introduced. The MRAC architecture enables the controller to adapt in a nonlinear fashion to deviations from the desired response, allowing for improved tracking performance, stabili (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Michael Bolender Ph.D. (Committee Member); Elad Kivelevitch Ph.D. (Committee Member) Subjects: Aerospace Materials

Master of Science, The Ohio State University, 2024, Mechanical Engineering

The United States has made a commitment to de-carbonization of the transportation sector. Around seventeen percent of all US greenhouse gas emissions come from light-duty passenger vehicles [1]. Electric vehicles (EVs) provide a promising pathway to achieving that goal. EVs are an emerging technology in the automotive sector and are now just being produced at the same scale as internal combustion engine vehicles. During this time, rapid innovation in the EV space is possible, and unique EV architectures and designs can be explored. Further, a standardized testing and development process is needed for vehicle models and controllers. This work presents an architecture selection process that explores a large range of unique EV architectures and tabulates all of the data. Next, from that architecture selection process, a physical vehicle is constructed, and a new drivetrain controller is developed. The processes used for requirements generation and management, model development, and controller testing are presented in this work. The presented processes are all done to an industry-level standard and present a future steppingstone for further exploration of the EV design space.

Committee: Giorgio Rizzoni (Committee Member); Shawn Midlam-Mohler (Advisor) Subjects: Mechanical Engineering

Doctor of Philosophy (PhD), Wright State University, 2024, Engineering PhD

Implantable leads used in pacemakers, defibrillators, and cardiac resynchronization therapy are designed for in-vivo applications, yet their longevity is inevitably shaped by the conditions within the human body. The mechanical behavior of these leads can be affected over time, necessitating the evaluation of their residual properties. Two main insulators, silicone, and polyurethane are commonly used for the outer insulation of cardiac leads. Understanding the long-term performance of these insulators is crucial for ensuring the reliability and safety of cardiac implantable devices. The research aims to assess the long-term mechanical properties and performance of implantable leads utilized in cardiovascular implantable electronic devices (CIEDs), which are subjected to the in-vivo environment with finite lifespans. Utilizing more than 300 samples obtained from the Wright State University Anatomical Gift Program. Tests were conducted according to ASTM standard D 1708-02a and ASTM Standard D 412-06a using the Test Resources Q series system. Electromagnetic interference (EMI) from electric vehicles on CIEDs, particularly Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) were quantified within a Tesla Model 3. SolidWorks and MIMICS 25.0 were used for three-dimensional heart modeling, and were developed with CIED leads inside the heart for finite element analysis. ANSYS Workbench 2022R1 was utilized for simulating cardiac leads behavior inside the heart with specific residual properties, and used computational simulations to predict lead performance. This research found silicone insulation to show some degradation in mechanical properties after 94 months of in-vivo environment, and polyurethane insulation demonstrated consistent performance without significant degradation after 108 months of in-vivo exposure. The proposed mechanical testing and FEM provide an insight into the durability and performance of different insulation materials, and how these materia (open full item for complete abstract)

Committee: Tarun Goswami D.Sc. (Advisor); Abdul Wase MBBS (Committee Member); Vic Middleton Ph.D. (Committee Member); Jaime E. Ramirez-Vick Ph.D. (Committee Member) Subjects: Biomedical Engineering

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

We are witnessing a profound transformation within the automotive industry, propelled by the mass adoption of Electric Vehicles (EVs). As this transition unfolds at a rapid pace, it unveils security gaps within the emerging infrastructure, necessitating urgent attention and solutions. This thesis investigates the resilience to cybersecurity threats of a new standalone branch of EV charging application, which we propose terming "Fleet Charging Infrastructure" (FCI). We conducted a case study on one of the emerging leaders in EV fleet charging in the US market, Electrada. To start the study, we analyzed Electrada's operation, the structure of their charging infrastructure, and the importance of internal processes that guarantee a 99% uptime commitment to their customers. Our research questions focused on characterizing FCI, identifying cybersecurity threats specific to it, and devising strategies for enhancing its resilience against cyberattacks. The study utilized STRIDE and DREAD frameworks for threat modeling and prioritization, along with well-established industry frameworks like NIST IR8473 and CIS CSC for threat mitigation planning. As a result, we succeeded in outlining the key processes within FCI, enumerating major components and dataflows, and establishing distinctive features that later allowed us to identify 27 unique threats, propose mitigation actions for each threat, and develop a 3-step mitigation strategy plan for FCI operators based on their resource availability. Our findings highlight the distinctiveness of FCI as a standalone branch of EV charging applications, due to the uniqueness of internal processes, components, motivations, and goals compared to other iterations of EV Charging.

Committee: Isaac Kofi Nti Ph.D. (Committee Member); M. Murat Ozer Ph.D. (Committee Chair) Subjects: Information Technology

Master of Science, The Ohio State University, 2023, Electrical and Computer Engineering

Over the past decade, there has been a growing movement towards reducing the carbon footprint which involves striving for net-zero emissions and developing an infrastructure that can sustain it. Among the end-use sectors, transportation accounts for nearly a third of overall greenhouse gas (GHG) emissions, with commercial vehicles as a huge contributor, making it imperative for this industry to adapt to emerging technologies to accommodate the expectations of a green and sustainable mobility vision. Battery electric and fuel cell vehicle technologies are suitable candidates to replace the existing conventional fossil fuel powered vehicle architectures but a complete transformation is nigh realizable due to various impediments. A soft impact is vital to ease the transformation process to foster growth and acceptance among industry partners as well as prepare for any unseen hurdles along the way. The work presented in this thesis focuses on designing and operating commercial vehicle fleets, introducing a novel fleet management system (FMS) framework capable of providing energy efficient mobility solutions. The FMS uses a recommender system comprised of a Design Space Filter (DSF) module to provide a feasible set of powertrains from the vehicle configuration database and uses machine learning algorithms to estimate the energy consumption for a given drive cycle, ranking them on the basis of their freight energy efficiency metric and ultimately aiding in the fleet composition design process. Due to the usage of highly confidential data pertaining to vehicle behavior, operators and OEMs are not keen on sharing this data unless there are agreements and secure data-sharing procedures established. Aware of this data bottleneck, the FMS leverages federated learning technique to estimate vehicular performance attributes and provides inferences which can be utilized for analyzing fleet behavior and enhancing fleet operations. This is extended to learn the mobility dynamics of (open full item for complete abstract)

Committee: Qadeer Ahmed (Advisor); Parinaz Naghizadeh (Committee Member); Manfredi Villani (Other) Subjects: Artificial Intelligence; Automotive Engineering; Computer Engineering; Electrical Engineering; Sustainability; Transportation

Master of Sciences, Case Western Reserve University, 2023, EMC - Aerospace Engineering

Proving the feasibility and overall efficiency of Flapping-Wing Micro Air Vehicles (FWMAVs) over other types of MAVs is vital for their advancement. Due to their complex aerodynamics and the difficulty of building accurate models of the flying animal, assessing the flight performance and efficiency of animals and FWMAVs mimicking those animals can be a challenging task. The research presented here investigates the hawk moth (Manduca Sexta L.) forewing as inspiration for designing an optimal wing for a moth-scale FWMAV using a surrogate modeling approach. The design of experiment (DOE) assesses the variation in aerodynamic lift-to-drag ratio due to variations in the wing geometry parameters. Using results from the experiment as training data, the trained surrogate model is a quadratic Support Vector Regression model that can rapidly evaluate the aerodynamic lift-to-drag ratio based on the wing geometry input parameters, thus identifying local extrema within the design space.

Committee: Kenneth Moses (Committee Chair); Roger Quinn (Committee Member); Bryan Schmidt (Committee Member) Subjects: Aerospace Engineering; Robotics

Master of Science in Engineering, University of Akron, 2021, Mechanical Engineering

The viscoelastic properties of rubber have allowed compounds to be utilized across many different industries. Rubber is a very unique material, and the chosen manufacturing process can result in numerous variations of the polymer. With many potential outcomes, it is crucial to accurately determine the physical attributes of the polymer. For many applications, but specifically for the tire industry, one of the standard methods for determining viscoelastic properties is through dynamic mechanical analysis (DMA). The raw data from DMA is adjusted through the Williams, Landel, and Ferry (WLF) shift equation to create a master curve for the rubber specimen. This study investigates methods for the calculation of friction coefficient, and suggests a new code to predict the friction coefficient. Several discussions in the paper will be for validation of the code and its range of applications. We then implement a parametric analysis to determine which factors critically affect the friction factor results. By finding the sensitivity of the inputs to the new code for friction coefficient, the critical inputs can be identified. The parameters that are studied are the storage modulus, loss modulus, surface asperities heights, the surface asperities wavelength, and the adhesive contribution to friction. The adhesion and hysteresis contributions to the friction coefficient are also discussed in this paper. It is shown that the adhesive contribution plays a large role in determining the friction coefficient. The data from the study will determine the effect that direct DMA testing has on the friction coefficient as well as tire performance indicators. The indicators that the direct testing affects the most are the wet traction indicator, the snow traction indicator, and the ice traction indicator.

Committee: Siamak Farhad (Advisor); Alex Povitsky (Committee Member); Shing-Chung (Josh) Wong (Committee Member) Subjects: Automotive Materials; Materials Science; Mathematics; Polymers

Master of Science (M.S.), University of Dayton, 2021, Civil Engineering

The environmental impact attributed to vehicular emissions at interchanges and other roadway designs should be considered as significant as their traffic safety and operational performances. In today's world, we all are aware of the importance of climate change and global warming. As freight and passenger travel demands increase, so do congestion and emissions from the transportation sector, especially on-road vehicles, which have drawn significant attention in recent years. With an estimated 29% contribution to the total U.S. greenhouse gas (GHG) emissions by economic sectors, transportation is the highest contributor to GHGs in the nation. PTV Vissim emission calculator provides an opportunity for us to perform a comparative emissions analysis. This thesis involves a case study of an existing service interchange, a conventional diamond interchange (CDI) at Austin Blvd on I-75 located about 12 miles south of downtown Dayton Ohio, with other two alternative designs, a diverging diamond interchange (DDI) and a single-point urban interchange (SPUI), in terms of fuel consumptions, emissions, and traffic operations for similar traffic conditions, and roadway characteristics through microsimulation. In this research study, we focused on carbon dioxide (CO2) and two other critical pollutant gases emitted from vehicles' exhaust pipes, carbon monoxide (C.O.) and nitrogen oxides (NOx), including fuel consumption. The signal optimization for each interchange was conducted utilizing PTV Vistro and traffic simulation and emissions analysis using PTV Vissim. The results indicate that the existing CDI design results in much higher emission rates than the other two alternative designs, the SPUI and the DDI, for each traffic level condition considered. A reduction of 85% on average in emissions rates and fuel consumption for both alternative designs compared to the existing CDI was observed. Although the SPUI's and DDI's performances were very close, some significant difference was (open full item for complete abstract)

Committee: Deogratias Eustace Dr (Committee Chair); Philip Appiah-Kubi Dr (Committee Member); Samwel Oyier Zephaniah Dr (Committee Member) Subjects: Civil Engineering; Transportation

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The increasing penetration of renewable sources of energy has resulted in an increased likelihood of power over-generation and ramp rate requirements at the electricity supplier end. By incorporating temporally varying costs of electricity provided to the customer, the grid supplier may choose to offer demand-response programs that encourage the customer to defer high load activities to periods of low grid load, effectively overcoming these challenges and increasing machine life. Smart homes optimally activate appliances at the appropriate time with an objective to minimize load at high-price periods, so that at the user end, the total electricity price is lowered. The work presented in this thesis focuses first on the development of models for energy demand and generation associated with electric vehicle (EV) charging and solar power generation, and their integration in an existing residential energy modeling framework. For this enhanced residential power demand model, machine learning (ML) techniques are used to develop a prediction of the user activities for single-resident and multi-resident households. The predicted power demand can be integrated into the smart home algorithm to enhance the optimal activation of appliances to minimize electricity cost and inconvenience.

Committee: Stephanie Stockar (Advisor); Manoj Srinivasan (Committee Member) Subjects: Alternative Energy; Artificial Intelligence; Energy; Engineering; Mechanical Engineering

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

Tires play a critical role in influencing vehicle behavior. Determining vehicle handling, implementation of active safety systems such as anti-lock braking systems and electronic stability control systems rely on robust understanding of tire behavior. Tire behavior changes with operating conditions such as normal load, camber angle, slip angle and temperature. These operating conditions were identified with on track testing using wheel force transducers, slip angle sensors and an array of data acquisition systems. Data from 20 days of driving on public roads with data acquisition provided guidance to design the on track testing. Data from on track testing was used to design high slip angle and low slip angle tire testing on the FlatTrac III tire test rig at Smithers Rapra in Akron, Ohio. Correlation between wheel force transducer and FlatTrac III was presented. In addition, correlation between the test track surface and FlatTrac III belt surface was also presented by using the same physical tires between track testing and FlatTrac III testing. The on track data can be used to reduce testing time and cost by about 40% by utilizing an asymmetric tire test matrix. Further, Magic Formula 6.1 tire models are fit to the data from FlatTrac III and scaling factors are developed which can further reduce testing time and cost by 50%. The correlation between lateral force predicted by these scaled models to measured lateral force on the FlatTrac III for a combination of normal load, slip angle and camber is provided.

Committee: Levent Guvenc (Advisor); Gary Heydinger (Committee Member); Jeffrey Chrstos (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

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

Unmanned Aerial Vehicles (UAV) have continued to receive attention from corporations and governmental agencies due to their wide range of potential applications and hybrid nature. More Electric Aircraft (MEA) promise many benefits (e.g., reduced weight, decreased fuel consumption, and high reliability) and their development continues to be the trend. Hybrid UAVs are an ideal prototype to implement concepts of aircraft electrification due to their small size and the DC nature of their power systems. However, papers addressing the energy optimization UAV electric power systems fail to consider the importance of high accuracy and computational speed. This thesis proposes an energy optimization method to enhance the energy durability of a UAV through a novel approach integrating an optimization formulation and a detailed UAV simulation model, with physical circuitry characteristics. This approach allows for increased computation efficiency while still capturing physical system constraints experienced during real world flight, which are complex and highly nonlinear due to aerial, thermal, and electrical dynamics. Optimization formulations created within this work are based on dynamic programming and moving-horizon model predictive control (MPC). The efficacy of this method is proven on a realistic UAV system. Within the MPC formulation, various charge strategies are implemented and fuel consumption is calculated to provide insight into the trade-offs inherent within the UAV system, wherein battery discharging is required for high demand dash periods, but additional charge can only be supplied via increased output engine power. That is, minimal fuel consumption must be considered in light of the need for non-optimal output engine power to charge the battery such that a total mission can be completed. Algorithmic considerations regarding horizon size for MPC and algorithmic enhancements, considering random loads and renewable generation capacity on-board the UAV are pre (open full item for complete abstract)

Committee: Jiankang Wang (Advisor); Mahesh Illindala (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2016, Mechanical Engineering

Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with the human driver to increase vehicle driving safety and performance. Such a cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver's driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This dissertation presents several lateral and longitudinal driver models developed based on human subject driving simulator experiments that are able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver's velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver's ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver's steering wheel angle and velocity for a variety of highway maneuvers. The lateral driver model is capable of predicting the infrequent collision avoidance behavior of the driver from only the driver's daily driving habits. This is important due to the fact that these collision avoidance maneuvers require high control skills from the driver (open full item for complete abstract)

Committee: Junmin Wang (Advisor); Haijun Su (Committee Member); Gary Heydinger (Committee Member); Richard Jagacinski (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2016, Mechanical Engineering

Automobiles affect growth of humanity. Vehicles affect everything from economy to our environment. One of the advancements to ensure the energy consumed by vehicles is sustainable are Hybrid-electric vehicles. Hybrid-electric vehicles cover various domains under its umbrella. They are a combination of different systems including Electrical, Mechanical, Chemical, and Thermal. One of the tools used to model the dynamics of Hybrid-electric vehicles is MATLAB/Simulink which is a general purpose modelling tool. The language of Modelica and its simulation environments allows multi-domain modeling while modeling from the physical perspective. The focus of this thesis was to model components of Hybrid–electric vehicles in a Modelica based simulation environment, in the process learning the advantages and disadvantages of using the Modelica language and the tools which act as the front ends for the language. A model was created to simulate the electrical dynamics of a battery cell and pack. A model of an electric drive was taken from a commercial library and its dynamics were observed by integrating the model of the battery pack along with the electric drive. The models created using Dymola simulated in Dymola and were exported to Simulink to compare results and simulation times. The differences in creating a model in Simulink and a Modelica Simulation environment were examined and noted along with the benefits and shortcomings of the different modeling methodologies.

Committee: Giorgio Rizzoni PhD (Advisor); Marcello Canova PhD (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2016, Industrial and Systems Engineering (Engineering and Technology)

This dissertation aims to fulfill the gap of designing the supply chain system as a whole and looking at overall design across the supply chain of the company in the long term rather than short term. This dissertation is inspired from the window manufacturer which manufactures and distributes vinyl windows to meet new construction and replacement/remodeling sector demand. In this dissertation, complementary analytical models are discussed to determine efficient way to design a supply chain network. Mainly, design aspect and operational aspect of a supply chain system are considered. In the design aspect, number of manufacturing facilities, location/allocation decisions are determined. Then, the number of distribution centers, location and allocation decisions are made. Continuing with that, manufacturing configuration of each individual manufacturing facility is designed in detail and analyzed. In the proposed layered cellular manufacturing system design, based on the demand and processing requirements, products are grouped into product families and assigned to dedicated, shared and remainder cells. In the operational aspect, based on the designed manufacturing system, cell loading and product sequencing are performed. Moreover, vehicle routing system is designed to reach out the end customers in the supply chain system. All in all, this dissertation is unique in the sense of covering different levels of supply chain planning and decisions with nested approaches of facilities location, manufacturing system design, network design and vehicle routing design. New mathematical models and various new heuristic approaches are proposed to design a supply chain system in the presence of high-volume and low-volume windows demand.

Committee: Gürsel A. Süer PhD (Advisor); Faizul Huq PhD (Committee Member); M. Khurrum Bhutta PhD (Committee Member); Dale Masel PhD (Committee Member); Diana Schwerha PhD (Committee Member) Subjects: Engineering; Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2015, Electrical and Computer Engineering

The Dual Mechanical Port (DMP) electric machine has two rotors that can be set to rotate at different speeds and directions. Compared to conventional electric machines with only one rotor, the DMP machine provides higher torque density and much better control flexibility. However, the DMP machine has a relatively complex structure, which makes it a challenge to model and control. In addition, the existing model and control algorithms for single rotor machines cannot directly be applied to the DMP machine. In this study, it has been explore that how the DMP machine can be applied to hybrid electric vehicles as an avenue for explaining the electromagnetic characteristics and functionality of this more complex mechanism. The model and the control algorithms for two different DMP machines are also investigated. The first DMP machine, which is called the PMDMP, uses two layers of permanent magnets within the outer rotor. The second one, which is referred to as the SCDMP machine, uses a single layer of squirrel cage within the outer rotor, The study of the modeling and control for the SCDMP machine is the major contribution of this work. Compared to other DMP machines, the PMDMP machine stands out for its high torque density and high efficiency. A detailed model derivation for the PMDMP is presented later in the work. The independent control of its two rotors is investigated and verified by simulations and experiments. To overcome the problems brought about by the position sensors, the effectiveness of position sensorless control algorithms for the PMDMP is investigated. High frequency injection and sliding mode sensorless control algorithms are applied to the PMDMP machine at low speed and high speed, respectively. The performance of the sensorless control algorithms in experiments matches well with the simulation results. To verify the functionality of the DMP machine in power split hybrid application, the power flow pattern in various operational modes are discusse (open full item for complete abstract)

Committee: Longya Xu Dr. (Advisor); Jin Wang Dr. (Committee Member); Mahesh Illindala Dr. (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Energy

Master of Science, The Ohio State University, 2015, Mechanical Engineering

As part of EcoCAR 3, an Advanced Vehicle Technology Competition, The Ohio State University is among sixteen teams challenged with the task of re-engineering a stock 2016 Chevrolet Camaro into a hybrid electric vehicle. Part of the Ohio State design process entails using model based design to develop a full vehicle model of the hybrid Camaro that can simulate energy consumption for various testing purposes. This thesis describes the design process behind developing the plant and control models, integrating everything together in a full vehicle model, performing fault insertion testing for mitigation development, and constructing the model architecture such that transfer between In-the-Loop platforms is easier than conventional methods. The full vehicle model developed will be refined and used by the Ohio State team throughout the four year EcoCAR 3 competition.

Committee: Shawn Midlam-Mohler (Advisor); Giorgio Rizzoni (Committee Member) Subjects: Automotive Engineering

Doctor of Philosophy, The Ohio State University, 2013, Mechanical Engineering

According to NHTSA's 2011 Traffic Safety Facts, large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. Given the high accident involvement rate of heavy trucks, the research presented in this dissertation focussed on methods of improving and testing heavy truck ESC performance. The first part of the research, aimed at estimating trailer parameters and states using sensors on the tractor to enhance the capabilities of the tractor based ESC unit. The mass of the vehicle and road grade are first estimated using recursive least square estimation. The trailer CG position is then estimated using the load on the tractor drive axels. This is followed by a planar model of an articulated vehicle to calculate the lateral acceleration, longitudinal acceleration and yaw rate of the trailer CG. Finally a Dual Extended Kalman Filter is developed to estmate trailer roll angle and roll parameters. The second phase of the research involved the development of a state of the art Hardware in the Loop simulation setup to test heavy truck ESC systems. The design of the HIL system is briefly discussed followed by the modeling of the vehicles in TruckSim. This is followed by a rigorous validation of the vehicle models and the HIL setup. Finally some of the applications of the validated HIL setup is discussed. This includes an indepth study of the Sine with Dwell maneuver and effects of vehicle speed, surface friction and CG height on the vehicle stability. This is followed by the design of a steering controller which is used to study the advantages afforded by the ESC system in an actual crash sc (open full item for complete abstract)

Committee: Dennis Guenther Dr. (Advisor); Gary Heydinger Dr. (Committee Member); Ahmet Kahraman Dr. (Committee Member); Junmin Wang Dr. (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science in Engineering (MSEgr), Wright State University, 2007, Human Factors Engineering

In many current agent-based modeling systems, it is difficult for a domain-expert user to define and implement agent behaviors without possessing extensive programming knowledge. MUAVES is an existing simulation environment that serves as a research testbed for examining command and control issues with unmanned aerial vehicle (UAV) systems containing many vehicle agents. In its previous form, defining agent behaviors required knowledge of the C# programming language that some MUAVES users did not have. This thesis presents a new methodology for the definition and implementation of UAV agent behaviors in MUAVES. The new methodology is based on diagramming an agent's controller state. No programming knowledge is required to reuse modular behaviors and trigger conditions specified by previous researchers. The definition of novel behaviors has also been improved by placing behavioral code in external library files, away from the main simulation code. These novel behaviors can be implemented at any desired level of abstraction. After describing the methodology, some sample scenarios are presented as proofs-of-concept.

Committee: Raymond Hill (Advisor) Subjects:

Doctor of Philosophy (PhD), Wright State University, 2006, Human Factors Engineering

Many real-world applications are complex, dynamic, and uncertain. Human operators play an important role in ensuring the safety and in achieving operational effectiveness in such systems. During task performance, both humans and computerized processes bring in varying strengths and limitations. Research on human-centered automation in aviation, satellite ground control, and nuclear power plant control has resulted in broad guidelines on system design involving human and computerized processes in supervisory control. However, problems such as increased human error, lack of situational awareness, and opacity from poorly automated systems remain, particularly in scenarios where human operators must make decisions in time-pressured planning. While anecdotal evidence does exist that interactive systems are better than completely manual or completely automated systems, there is a lack of systematic studies of human-centered modeling in joint cognitive systems. This research addresses the issue of joint cognitive problem solving for a class of problems related to supervisory control of vehicle routing. The key research question addressed by this study is whether a human integrated approach helps in better generation of alternatives and better evaluation of alternatives that would potentially lead to better solutions to problems. Empirical results from a simulated military mission indicate that the human integrated approach resulted in better overall performance when compared to purely automated solutions for vehicle routing problems considered in this research study. Specifically, significantly more high priority targets were covered in the human integrated approach compared to the automated solution without any significant degradation with respect to all the other dependent measures including percentage of total targets covered, low priority targets covered, total targets covered in threat zone, high priority targets covered in threat zone, and low priority targets covere (open full item for complete abstract)

Committee: S Narayanan (Advisor) Subjects: