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

The Ohio State EcoCAR team is a student project team at The Ohio State University providing real-world engineering experience and learning opportunities to engineering students. The EcoCAR Mobility Challenge is sponsored by the U.S. Department of Energy, General Motors, and The Mathworks and challenges twelve universities across the United States and Canada to redesign and reengineer a 2019 Chevrolet Blazer into a hybrid-electric vehicle. The goal of the competition is for students to develop and implement technologies to reduce the vehicle's environmental impact while maintaining performance and to enhance the vehicle with connected and automated technologies for a future in the mobility-as-a-service market. The transition from conventional to hybrid vehicle requires the addition of several hybrid powertrain components, including electric motors, power inverters, and a high voltage battery. These new components have thermal cooling requirements and require the integration of a dedicated thermal management system to prevent components from overheating and to maintain optimal operating temperature. This work models the thermal systems of the internal combustion engine and hybrid powertrain components to provide estimates for component temperatures during steady-state operation and predetermined drive cycles. The GT-Suite modeling software package from Gamma Technologies was chosen to model the two thermal systems because of its extensive library of pre-validated automotive grade component models. This library allowed component models to be built quickly and without extensive data collection. The thermal system models were integrated with a full-vehicle model of the OSU EcoCAR team's vehicle in Simulink. This work seeks to provide a reasonable approximation of the integrated thermal systems in the OSU EcoCAR vehicle, with provisions to update and calibrate the model in the future. The model provides both steady-state and drive cycle feedb (open full item for complete abstract)

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.

Master of Science, The Ohio State University, 2023, Horticulture and Crop Science

Winter rye (Secale cereale L.) is a popular cover crop in the U.S. due to its winter hardiness, ease of establishment, and positive contributions to soil conservation, but research on winter hybrid rye grain production for human consumption and animal feed is severely lacking. Niche markets in distilling, bread and baking products, and livestock feed are currently available and in demand in the U.S. For farmers to integrate winter hybrid rye into their cropping systems to supply these markets, they need basic agronomic information on planting dates, seeding rates, and potential grain yield and quality. Study objective was to determine the influence of planting date and seeding rate on winter hybrid rye grain yield in five locations (OH, KY, and WI, and two in MN) over two years (2021-2022 & 2022-2023). The experimental design was a split-plot randomized complete block design with four replications. Whole plot factor was planting date (ranging from September to November) and sub-plot factor was seeding rate (0.4, 0.6, 0.8, 1.0, 1.2 million seeds/acre). Maximum grain yields of >100 bu/acre were obtained for all locations in the 2021-2022 season, while yields of >120 bu/acre were harvested from all locations except Crookston, MN, as the Crookston 2022-2023 site-year was deemed unharvestable due to drought. Agronomic optimum planting dates and agronomic optimum seeding rates (AOSR) varied among locations, but no recommendation of 0.4 million seeds/acre was given for any planting date across locations. Recommended AOSR for all locations was generally 0.8 million seeds/acre or higher, though Lexington, KY, had an AOSR of 0.6 – 0.8 million seeds/acre for the week before the fly-free date, and Arlington, WI, had a recommended AOSR of 1.0 million seeds or less. Overall, optimal planting dates fell within the two weeks following the established fly-free date across locations. Growing hybrid rye was possible in many environments across the Midwestern U.S., and excellent yields (open full item for complete abstract)

Master of Fine Arts (MFA), Bowling Green State University, 2022, Creative Writing/Fiction

With[in]out is an artistic experimentation and liberation of hybrid stories that mirror thought processes and memory through an array of characters from an array of genres. Each piece utilizes form, genre, diction, white space, and style to best exhibit the inner worlds of characters on the page as well as the worlds the characters themselves inhabit. With[in]out creates a space for characters to go on a complex, internal journey involving difficult decisions, mental illness, trauma, isolation, and recovery. The collection asserts that memory is but a collage of images and sensory experiences and asks the reader to consider this tenet, as well as the stylistic choices within each piece, to gain a deeper understanding of how each character operates, to viscerally immerse oneself beyond prose conventions.

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

The increase in the usage of Unmanned Aerial Vehicles or UAVs for surveillance, aid and other purposes has compounded the detrimental impact of the aviation industry on the environment. To counter its increasing contribution to the climate crisis, the industry needs timely energy efficient solutions. Combined optimal design and control study or co-design aims to lower the energy consumption of UAVs through various propulsion systems, one of them being a power-split hybrid model. However, this approach is limited in its consideration of uncertain losses or changes in these systems. In this thesis, we address these uncertain parameters of a Group 5 UAV using a power-split Hybrid Electric Propulsion System (HEPS) architecture. We will be investigating several random variations which such an aircraft could encounter during its flight. The thesis explores the outcome of the application of a stochastic dynamic optimization technique, called Robust-Multidisciplinary Dynamic System Design Optimization (R-MDSDO), to the power split HEPS architecture. This helps to ascertain the optimal value of design, state trajectories and control trajectories to minimize the energy utilized by the Group 5 UAV. The end result, which is achieved through a comparison of the robust and deterministic solution, indicates that accounting for system uncertainties has a significant impact on the power-split HEPS design.

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

With the on-going electrification and data-intelligence trends in logistics industries, enabled by the advances in powertrain electrification, and connected and autonomous vehicle technologies, the traditional ways vehicles are designed by engineering experience and sales data are to be updated with a design for operation notion that relies intensively on operational data collection and large scale simulations. In this work, this design for operation notion is revisited with a specific combination of optimization and control techniques that promises accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pick-up and delivery truck that is limited to a given driving mission. A Gaussian Process (GP) based statistical learning approach is used to refine the search for the most accurate, optimal designs. Five hybrid powertrain architectures are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. A cross-architecture performance and cost comparison is performed and the selected architecture is developed further in the form of a forward simulator with a dedicated ECMS controller. In the end, a traffic-in-the-loop simulation is performed by integrating the selected powertrain architecture with a SUMO traffic simulator to evaluate the performance of the developed controller against varying driving conditions.

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.

Master of Science (M.S.), University of Dayton, 2013, Materials Engineering

This study investigated the effect of carbon fiber placed in different amount at different location in a square box beam. In total eight designs were selected and three beams were fabricated for each design using hand layup and vacuum bagging technique. The beams were tested using a four point bending test. The stiffness were calculated and compared with all glass fiber beams. The beams were analyzed using finite element method in Abaqus. It was found that the location of the carbon fiber has an effect on the increase in the stiffness of the beam. Beam with 29.6% carbon fiber by volume gave maximum increase in stiffness. The maximum load carried by the beams showed a different trend. It was observed that the maximum load carrying capacity decreased with increase in the amount of carbon fiber. Carbon fiber effectiveness index (ratio of percentage increase in stiffness of beam and volume percent of carbon fiber) was calculated for each design and it was found that the design D3; which has one layer of carbon fiber on the top and bottom face utilized carbon fiber most effectively.

MA, University of Cincinnati, 2006, Arts and Sciences : Communication

The controversial documentaries by Michael Moore have provoked public debate on social and political matters since the end of the 1980s. This study analyzed Michael Moore's Fahrenheit 9/11 from a dualistic approach. Utilizing media scholar Nichols' documentary modes of representation, this study identified three emergent modes: expository, participatory, and politically reflexive. This project also examined the extent to which these three modes reflected a particular view toward the rhetorical situation. Often times situations arise that call for a dual response. Jamieson and Campbell's generic hybrid was also used to assess the extent in which Fahrenheit 9/11 responded to the needs of the situation and the audience. The hybrid blend Moore used was comprised of the conspiracy and diatribe genres. Not only did these two genres fulfill the perceived needs of the audience and situation, but they also worked together in such a way that elements of each genre buttress the weaknesses of the other.

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)

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

Compact stars (CSs) are the remnants of “dead” stars that were too small to form black holes; the category includes both white dwarfs (WDs) and neutron stars (NSs). To produce a full description of any magnetized compact star requires solving Einstein's equations in unison with Maxwell's equations. However, when putting these two sets of equations together, there is an additional degree of freedom that requires the inclusion of the equation of state (EOS) of the stellar matter in question. The most notable difference between CSs and other stars is that CSs consist of degenerate fermion matter. Fermionic matter exists in a degenerate state when the temperature is low compared to the Fermi energy. Such states arise due to the Pauli exclusion principle, which states that no two identical fermions (particles with half integer spin) in the same quantum system may inhabit the same quantum state. In the case of WDs, this degeneracy is caused solely by electrons; whereas, in NSs, the degeneracy is in several species of particles including neutrons and protons, but also more “exotic” baryons, such as Lambdas, Sigmas, and Cascades. In the grand canonical ensemble, the stellar EOS is typically expressed as the relation between the total energy density of a gas of particles and their pressure. It is calculated using thermodynamics with, in the NS case, an additional contribution from the strong nuclear force, which must be modeled. Due to computational difficulty, the EOS is often calculated in a simplified way, assuming that one aspect or another is not significant. As such, EOSs exist with temperature effects or with magnetic field effects, but not with both. For example, higher temperatures (without additional degrees of freedom) lead to higher pressures at the same energy density; the EOS is “stiffer.” Magnetic fields lead to a pressure anisotropy and Landau quantization, which gives rise to De Haas-Van Alphen oscillations in the EOS. This thesis breaks new ground by sim (open full item for complete abstract)

Master of Science in Mechanical Engineering, Cleveland State University, 2021, Washkewicz College of Engineering

Recent developments in aircraft propulsion electrification are motivated by economic and environmental factors such as lowering greenhouse gas emissions, reducing noise, and increasing fuel efficiency. This thesis focuses on a hybrid gas-electric propulsion concept combining a gas turbine jet engine with an electromechanical (EM) system. An optimal control system allows energy to be recovered from the gas turbine engine or injected into it from an electric storage unit. Energy extraction or injection can be obtained by selecting a performance weight in the optimization function that trades off fuel consumption with stored electrical energy utilization. The goal of this research is to validate the effectiveness and plausibility of the optimal controller during representative acceleration and deceleration maneuvers and at steady state. To accomplish this, the gas turbine engine dynamics are simulated using NASA's T-MATS package and used in a hardware co-simulation approach along with physical hardware representative of the EM system, namely motors, power converter, and an energy storage device. A time scaling methodology was used to reconcile the power levels of the physical EM system (in the order of a kilowatt) with those of the engine simulation (in the order of megawatts). Multiple steady state missions were represented within a full simulation environment and in the lab test environment that covered a wide range of fuel-electric optimization weights. In addition, a chop-burst study was conducted to ensure the readiness of the system to handle flight missions. Based upon captured data, specifically that of shaft torque, supercapacitor voltage, and fuel flow measurements, it was determined that the optimal control objective was met. An increase in fuel-electric optimization weight corresponded to a desired change in torque to the engine and voltage to the energy storage device.

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

The electrification of commercial medium- and heavy-duty (MD/HD) vehicles presents new challenges in the design and control of the hybrid powertrain. Advances in Li-Ion battery technology have increased the capabilities of the battery pack, providing more electric range and increased power characteristics. However, new approaches and tools are necessary to improve the overall system efficiency, where a single cell technology may not meet the vocation demand. This dissertation explores the use of a convex optimization framework for the optimal design, energy, and thermal management of Li-Ion battery packs. Convex optimization methods provide advantages in terms of problem formulation, computation speed, and the guarantee of a globally optimal solution. Leveraging a convex framework, new hybrid energy storage systems (HESS) are investigated, where high-energy and high-power batteries are combined in a single system to capitalize on the benefits of each technology. The implementation of a HESS introduces complexities in the pack design and energy management, which are presented through a Design Space Exploration comparing single chemistry and hybrid chemistry battery packs. In addition, the optimal control of the battery thermal management system (TMS) is developed using convex optimization, where new methodologies are explored to mitigate the propagation of approximation error that arise during the convexification process.

Bachelor of Science of Media Arts and Studies (BSC), Ohio University, 2021, Media Arts and Studies

In my creative project and associated paper, I explore a hybrid medium, songcasting, that combines the most compelling elements of podcasts with the most compelling elements of songs. For the creation of this specific songcast, I interviewed 7 talented storytellers to capture audio recordings of them telling stories. From these, I chose a story about a Minnesotan teenager and his sister exploring Australia in 1979, and I built my songcast around it. This story explores coming of age, what it means to live in the modern world, cross-cultural relations, and more. The music and narration are carefully arranged and fused together to provide an immersive listening experience. While this songcast highlights the medium's strengths, it is only one example of the many possibilities of songcasting. By synthesizing music, an emphasis on parasocial relationships, and the storytelling modes of both songs and podcasts, songcasts stand apart as unique audio format.

Master of Science in Materials Science and Engineering (MSMSE), Wright State University, 2020, Materials Science and Engineering

Hybrid organic-inorganic perovskite solar cell is an emerging technology which has shown the fastest advancement in power conversion efficiency within a few years since introduction, thus making it one of the clean energy breakthroughs. These cells are based on thin-film technology which makes them suitable to manufacture using low-cost solution processing methods. As these types of cells are easily tunable with the selection of different materials, interfacial engineering is an important approach to increasing their efficiency. One of the main hurdles in this regard is the loss caused by the recombination of separated charges. An approach to tackle these issues is to incorporate organic monolayers between the charge (electron/hole) transport layers and the perovskite active layer. Such interface engineering has experimentally shown to improve the overall efficiency and stability of the cell. The current research focuses on the study of TiO2/HOOC-Ph-SH interface in order to understand the improved efficiency. Using ab initio quantum mechanical approach, we investigate the monolayer (HOOC-Ph-SH) adsorption onto the TiO2 surface to determine structural and electronic properties of the interface and discuss the connection of the results to solar cell performance.

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

The automotive industry is driving towards electrification. As the emission and fuel economy standards get more stringent, manufactures are electrifying their vehicle platforms by developing more hybrid electric vehicles. Although new technology boosts the fuel economy, it also brings new challenges. One of them is that customers often find discrepancies between the rated fuel economy number and the number they get during real world operation. Therefore, there is a need to investigate the issue and develop a new calibration process for optimizing the HEV fuel economy over both certification and real-world operation. In this research, a model-based calibration process is developed. The process uses meta-heuristic algorithms to optimize five look-up tables that are relevant to fuel economy of the HEV. Four different meta-heuristic algorithms, namely PSO, GA and two hybrids, are investigated and compared. It is found that PSO has reasonably good performance and can deliver its performance consistently under different conditions. Other algorithms may have better performance under certain scenarios, but they are sensitive to constraints in test problems and fail to get rational solutions in the real problem. The research also investigates methods to reduce number of parameters to optimize, the initialization of the optimization set and ways to generate representative drive cycles based on real-world driving data. The important thing is that these methods are not vehicle-specific and therefore can be migrated to calibration of other HEVs easily.

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

In the fast-growing field of energy storage devices, using Carbon Nanotubes (CNTs), as part of the electrode structures brings significant advantages due to the superior electrochemical performance of devices made from them. This has been ascribed to their high surface area, electrical conductivity, charge transport capability, mesoporosity, and electrolyte accessibility. Furthermore, CNT can be assembled into sheets, ribbons, and fibers amongst other types of assemblages, which made these nanotube form materials of interest in the field of wearable electronics. In this work, we have explored new design and fabrication of CNT based fiber supercapacitors (FSC) with increased energy and power densities and have investigated the approaches by which their electrochemical performances can be improved. Increasing the surface area and tuning of the pore sizes have been identified as some of the ways to enhance the electrochemical properties of supercapacitors. By employing a dry spinning process of Chemical Vapor Deposition (CVD) synthesized CNT arrays, we created CNT fibers, which can be used as electrodes for fiber-based supercapacitors. We successfully achieved enhanced surface area and alteration of pore width in our CNT fibers by employing an atmospheric pressure oxygen plasma functionalization process (OPFCNT). The presence of functional groups on the surface of the fibers is proved by X-Ray Photoelectron Spectroscopy (XPS) and Raman Spectroscopy, while the increase of their surface area is demonstrated by Brauner-Emmett-Teller (BET) measurements, UV-Vis Spectroscopy, and Randles Sevcik method. We housed active materials (polyaniline (PANI)) in our OPFCNTs. The CNT-PANI electrodes were produced by employing chemical oxidation polymerization of PANI on oxygen plasma functionalized CNT (OPFCNT) fibers. We also investigated the use of ionic liquid electrolytes in our fiber supercapacitor devices to improve the energy and power densities of the fabricated devices. (open full item for complete abstract)

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)

Doctor of Philosophy, The Ohio State University, 2018, Mechanical Engineering

Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. They use both electrical and traditional fossil-fuel based energy sources, which makes them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, energy management systems are designed, prototyped, and validated in a software simulation environment before implementation on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and cost. Traditionally, the design of energy management systems is based on statutory drive-cycles. Drive-cycle based solutions to energy management systems improve fuel economy of the vehicle and are well suited for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identifies and solves three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed. The simulation framework includes traffic simulation and powertrain simulation capabilities. It is termed traffic integrated powertrain co-simulation. Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. Finally, through the use of optimal c (open full item for complete abstract)

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

Although Motor vehicle accidents (MVAs) are reported as the leading cause of death and injury for children over the age of 3, life threatening injuries from the head, neck, and thorax are decreasing due to the advancement of car safety research. Such advancement in car safety brings focus to research on non-fatal, but detrimental injuries that cause life-long disability such as lower extremity injuries (Durbin, 2011, NHTSA, 2012). National Highway Traffic Safety Administration (NHTSA) (2010) reported that lower extremity injury in children between ages 4-7 years old is second in prevalence at 17% following head injury at 38%. In order to gain better understanding of both low and high velocity trauma mechanisms, more pediatric biomechanics research of lower extremity is necessary. However, lack of biofidelity in the lower extremities of the child anthropomorphic test device (ATD) pose challenges to studying the interaction between the body and the interior of the vehicle. Previous studies by Boucher, et al. (2013) evaluated the range of motion and stiffness of the ankle on child volunteers, and unpublished work by Boucher, et al. (2014) developed a new prototype Hybrid III 6-year-old ATD lower extremity. The present project continues to finalize and validate this new instrument. This document is divided into two studies. The first study evaluated the stiffness and impact repeatability of the prototype ATD ankle. Using the standard hand-held universal goniometer and an Isokinetic Dynamometer (Biodex System III), range of motion (ROM) and stiffness of the ankle with various stiffness bumpers in dorsiflexion and plantarflexion were determined. The bumper with the most biofidelic response was chosen for further testing. The ATD ankle was then tested for impact repeatability in the tibia and the ankle using a ram impactor. Results of the dynamometer testing indicated that bumper 80A was most comparable with the child volunteers (Boucher, et al., 2013) with the stiffness o (open full item for complete abstract)