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  Analysis of Salt Deposition and Deliquescence in Environmental Test Chambers

  Olivia Marie Schmelzer, Gregory P. Wolters

  With corrosion costs in the US reaching approximately $725 billion (1) in 2019, understanding and preventing corrosion is vital. Salt fog chambers have been used in the laboratory to analyze the phenomenon of corrosion for years, but standardized exposure tests have been primarily developed for use with chromate based primers, which are being phased out for environmental and health reasons. This study aims to understand crystallization of aerosolized salt water on various substrates, as well as the effect of successive periods of high humidity on crystal size and distribution. Creating a laboratory environment that accurately represents corrosion in the outside world is imperative for the field of corrosion science and would allow for better screening of non-chrome corrosion protection methods. An understanding of the deposition of salt from an atomized spray solution onto a metal surface in an environmental chamber, and the effect of humidity cycles on the deposition process is essential to this development. To accomplish this, laser microscope image and corrosion sensor data were collected for a variety of fog cycle times, salt mixtures, and humidity exposure times, with the goal being the calibration of a chamber to match the deposition rate and morphology of salt crystals seen on metal surfaces in field studies.1.Koch, Gerhardus. “1 - Cost of Corrosion.” Trends in Oil and Gas Corrosion Research and Technologies, Jan. 2017

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  Characterization of a Novel Phosphorus-Based Flame Retardant in a Mixed Epoxy Resin System

  Mustafa Mukhtar

  This poster summarizes the results of a series of ongoing experimental investigations into the curing reactions between a novel Phosphorus-Diglycidyl Ether of Bisphenol A (P-DGEBA) flame retardant, Diglycidyl Ether of Bisphenol A (DGEBA) epoxy resin, and aliphatic amine curing agent. Epoxy resins are one of the most widely used thermosetting polymers. Epoxy resin has wide applications in the fields of composites, adhesives, coatings, microelectronic materials, and printed circuit boards, due to its excellent mechanical properties, chemical resistance, and electrical insulation. However, epoxy thermosets can be flammable, which threatens human health and survivability of composite structures that catch fire. The primary motivation for this study was the promising preliminary experimental results obtained recently on a novel organophosphorus flame retardant (P-DGEBA) synthesized by the UD Chemistry Department. This research aims to identify the feasibility of reactive organophosphate compounds that could be integrated into existing curing epoxy (DGEBA) formulations to provide fire-resistant composites with little or no compromise in processing, treatment, and mechanical properties. Consequently, a series of experimental mixing formulations and curing conditions were investigated to provide further insight. Curing conditions were characterized by various physical and thermal properties using Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). Cured samples were also tested using microscale combustion calorimetry (MCC) to investigate the flammability and decomposition characteristics of cured epoxy resins.

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  Cloud Connected Real-Time Oil Condition Monitoring of UtilityTransformers using Impedance Spectroscopy

  Birhanu Desta Alemayehu

  We present the use of impedance spectroscopy to diagnose the oil condition inutility transformers for condition monitoring. Actual transformer oil sampleshaving different dissolved fault gas and moisture concentrations are obtained andcharacterized by analysing their impedance spectrum over a range of frequenciesfrom 1 kHz to 100 kHz using AD5933 from Analog Devices. From theexperimental results, it has been shown that the impedance spectrum of atransformer oil sample is related to its relative saturation percentage. Here, wepropose an integrated cloud-connected smart system which can continuouslymonitor the condition of oil present inside a utility transformer in real-time. As aproof of concept, the impedance values of oil samples are measured. Theimpedance data obtained is transmitted to a cloud computing interface where thedata is logged and processed. The proposed integrated system is reliable,inexpensive and suitable for implementation on utility transformers.

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  CNN-based Machine Learning Approaches to Skin Lesion Classification for Skin Cancer Detection and Diagnosis.

  Supun Samudika De Silva

  Skin cancer is a cancer type with a very high mortality rate and an incidence rate. It is also a cancer type that is known to be treatable if detected early. However, the diagnosis accuracy of a human expert is highly dependent on their experience in visual inspection of skin pigmentation. An automated detection of skin cancer based on the analysis of an image of the suspected affected area would be helpful to physicians or dermatologists in order to present a fast and reliable diagnosis. Presently, Convolutional Neural Networks (CNNs) are one of the Artificial Intelligence techniques used widely for computer aided detection and diagnosis of skin lesions. In some cases, the images that are intended to be used towards training a CNN are preprocessed by segmenting the lesion area, correcting illuminations, applying color constancy, removing attention to artefacts around the lesion, etc. Dermoscopy images are a type of images that are being used with CNNs other than standard photographed clinical images. Most of the time, classification of the images is completely based on features generated using CNNs. Transfer learning is one heavily utilized approach that uses pre-trained networks that are mostly very deep and are able to be fine-tuned for skin lesion images to generate features. This presentation introduces common approaches followed to preprocess images and learning techniques that are used with CNNs followed by descriptions of two current methods that utilize CNNs to classify skin lesions for skin cancer diagnosis.

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  Designing Energy Efficient & High-Speed Mechanical Presses for Improved Ram Motion using Advanced Algebraic Techniques

  Tianze Xu

  A mechanical press is a machine that shapes parts by driving a ram into metal and deforming the material into a desirable shape. As this is an incredibly common process for forming metal parts, from pop cans to car fenders, presses see significant use in industry on a global level. Two local companies, Aida Press and Nidec Minster, are serious contenders in this global market. The objective of the proposed research is to generate alternative drivetrain designs for mechanical presses that produce specialized ram motions, which is appealing to industry. The focus of this work is on mechanical presses due to their faster speeds, lower cost, greater accuracy, higher precision and energy efficient operation as compared to other pressing options. Due to their ubiquity, even small improvements yield huge savings in terms of processing time and energy consumed. The research work under this proposal is formulated to generate designs with practical dimensions and encountering forces in line with industry expectations. Moreover, these new designs will either improve dwell or improve the range of constant forming velocity, both strongly desired in industry.

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  Design of a Performance Tricycle for Persons with Paraplegia Powered By Functional Electrical Stimulation of Leg Muscles

  Nicholas Andrew Lanese

  The goal of this project is to design a performance tricycle for paraplegics whose leg muscles are stimulated to pedal via Functional Electrical Stimulation (FES). FES stimulates muscle contraction with small electrical currents and has proven useful in building muscle in patients while relieving soreness and promoting cardiovascular health. An FES-stimulated cyclist produces approximately 25 Watts of power, nearly 20 times less than a typical rider. At these reduced power levels, the challenges of pedaling are amplified. For example, as the pedal follows the traditional circular path, there are portions referred to as inactive zones, where neither FES-stimulated leg actively propels the cycle forward. One possibility for reducing or eliminating inactive zones is to redesign the circular path of the pedaling motion. Bicycles have recently been marketed that feature mechanisms that employ alternate pedaling motions. In addition to addressing inactive zones, these bikes also optimize the muscle capacity of the rider to deliver torque to the wheels. The alternative pedaling paths are achieved in our tricycle design optimization by developing quasi-static models to explore traditional, crank rocker, and coupler-driver mechanisms. These mechanisms allow for a comparison of torque generation which facilitates selecting the optimal design. Rider comfort and muscle capability are future steps taken for FES riders on the optimal design. Such a tricycle is seen to be beneficial for the health, mobility, and independence of the end user.

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  Determination of a Freeze Point Blending Rule for Jet Fuel Range Hydrocarbons

  Allison Ann Coburn

  Sustainable aviation fuels are the near term solution for greenhouse gas emission reduction associated with the aviation sector. There are extensive safety requirements established by an ASTM committee that the alternative aviation fuel must meet in order to achieve approval. Freeze point is one of the safety requirements that allow fuel to remain in liquid state under severe weather conditions. Methods and models to predict the freeze point of hydrocarbon blends are scarce in current literature. In the model that is currently being used, the validated temperature range for freeze point prediction is higher than the typical range for the jet fuel hydrocarbons. For other existing prediction models, an interaction coefficient determined by an experimental result is needed in the calculation to improve the accuracy of the prediction. The goal of this study is to develop an accurate freeze point blending rule for the jet fuel range hydrocarbons to evaluate eligibility for sustainable aviation fuel purposes. Here, a wide range of hydrocarbons with various freeze points were tested. Binary and ternary blends containing Bicyclohexyl, cis1-2 Dimethylcyclohexane, and an alternative jet fuel were tested. The experimental values obtained from varying compositions of each component for the binary and ternary blends were compared with linearly predicted values by volume percent and mole percent. While the linear prediction was comparable to the experimental values, there is still an aspect hindering more accurate predictions. The speculated missing aspect is the molecular structure. From other sources, it is known that molecules with the same chemical composition but varying structure can exhibit starkly different freezing points. Due to this, further testing is being conducted on molecules with these traits.

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  Dissipative Particle Dynamics (DPD) Simulation to understand the Nanoparticle Dispersion and Aggregation behavior in Polymer Nanocomposites

  Ashish Gogia

  Polymeric systems such as natural rubber used in car and truck tires require the addition of suitable additives for the enhancement of numerous properties, including reinforcement and durability. The behavior of such fillers, (carbon black, silica, and metal oxides and some combination thereof), and their influence on nanocomposite effectiveness, depends on the filler structure, the interaction between filler-polymer matrix as well as the processing history. To understand this problem, we perform Dissipative Particle Dynamics (DPD) simulation of these blends, varying polymer-polymer, filler-filler, and polymer-filler interaction energy. We will discuss the effects of interaction strength, the scaling of polymer chains, and methods to quantify the filler percolation threshold and mesh size as a function of filler concentration. The simulation results are also validated against small angle x-ray scattering data. Additionally, the effect of such agglomerates on the structural and dynamical properties of the nanocomposites, measured via the radial distribution, mean square displacement, and autocorrelation function are also explored.

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  ECE568 Detection and Estimation Final Project Presentation Live Poster Presentations

  Prathiksha Chikkamadal Manjunatha, Cory L. Heatwole, Jeremy Michael Olivar Hill, Achour Idoughi, Ranjani Kripashankar, Hsuan Lin, Sarah Miller, Luc Luc Tinch, Zhiyang Zhang

  The final project is an undertaking of a detection or estimation task of the student's choice. It may involve telecommunication, signal processing, or anything else so long as it is relevant to statistical analysis we studied in class and makes use of real world data.

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  Experimental Validation of Low Temperature Viscosity Predictions for Sustainable Aviation Fuel Blends

  Franchesca Rose Hauck

  With the rise of focus and funding in sustainable initiatives, the transportation sector has identified Sustainable Aviation Fuels (SAFs) as a response to reduce carbon dioxide and other greenhouse gas outputs into the atmosphere. Before SAFs can be used by airlines, they have to pass an approval process to make sure fuels operate within industry standards. The approval processes is very time and material expensive. To lower overall costs to this process, a pre-screening process has been developed to predict physical and chemical properties of the prospective fuels. Viscosity has been identified as one of the key properties as it lends itself to is ignition probability prediction.The focus of this study is to validate different viscosity extrapolation and blending models at low temperatures. The blends tested are ternary blends of current fuels and key molecules found within approved SAFs. Four different sets of blends were tested to see how other physical or chemical properties affect the viscosity when blended and measured at -40°C and -20°C. Of the six models tested, the Arrhenius Blending Model results in the least amount of error compared to experimental values. As molecules were introduced into the blend sets, errors increased. Overall low error suggests the utility of this blend model in property prediction. To further lower error, future work can investigate the effects of molecular size and interactions within blends.

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  Graphene Development for Removal of Bisphenol-S from Water

  Ismail Salem Alibrahim

  Bisphenol-A (BPA) and its analogues (BPs) are diphenylmethane derivatives with two hydroxyl groups on aromatic rings. About 3.5 million tons of BPA is produced annually for its usage in many applications such as a monomer primarily to synthesis polycarbonate plastic and epoxy resins and in the thermal receipt papers. BPs can be transported to water bodies through several routes such as degradable plastics, products manufactured with BPs, and after wastewater treatment. For example, the BPA levels in sewage sludge was found to measure between 0.5 and 5.1 mg/mL after wastewater treatment. BPs act as endocrine-disrupting chemical (EDC). United States Food and Drug Administration (FDA) banned the use of BPA in baby bottles in 2012. One feasible replacement of BPA is Bisphenol S (BPS). BPS, 4,4’-sulphonyl diphenol, was found to have a lesser impact on the endocrine activities and poses lower aquatic toxicity than BPA. Since the substitution of BPS for BPA in products and materials is a recent occurrence, there are still only a few studies investigating the impact of BPS on human health. However, some studies already suggested that BPS poses a human health and environmental contamination risk as well. Since concern has started to grow, methods of water treatment for BPs, such as adsorption processes, have been developed. This study investigated the performance Graphene oxide (GO) and reduced graphene oxide (rGO) to adsorb BPS from water at low concentration (~10 PPM). The GO synthesized by different methods (GO prepared by Hummer method and by newly developed method) shows a maximum percentage removal of BPS from water between 30% to 60%. On the other hand, reducing the GO by ascorbic acid increases the sorption capacity of the rGO to up to

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  Inspirational Women Stories in STEM

  Alekhya Dontham, Lauren Drankoff, Noel Mathew Lnu, Melissa K. McCabe, Claudia Swinson

  This session will involve a set of posters focused on inspirational women in STEM. The presentation will be about a combination of women that inspired us in different fields of STEM by breaking all stereotypes and gender barriers. The five women we are going to present about will be:

  • Kalpana Chawla
  • Rosalind Franklin
  • Mae Jemison
  • Cynthia Breazeal
  • Hedy Lamarr

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  LandNET: A Multi-Modal Fusion Network for Classification

  Jonathan Paul Schierl

  There is a need for classifying land coverage by usage. As these classes are somewhat abstract, this provides a challenge in classifying them and a need for as much information as possible. We propose an architecture capable of classify such scenes, using 2D aerial imagery and 3D point clouds. This is done by fusing the learned feature space of each modality, to be classified with fully connected layers. This method provides a high degree of accuracy for each modality and then learns the benefits of data type, for more accurate classification.

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  Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security

  Md Shahanur Alam

  In this work, we present an one shot learning system capable of online learning for network intrusion detection. Adaptive resonance theory is implemented in custom low power memristor-based neuromorphic hardware. The system is capable of discriminating with existing knowledge to learn incrementally. To determine the winning neuron, the winner takes all circuit is implemented with CMOS and a capacitor. The timing of charging the winning capacitor was found in nanosecond range. The performance of the system was evaluated on both previously known and zero-day datasets. The detection accuracy using zero-day packets is 99.97%, and 99.99% for the known attacks. Furthermore, the system was tested using various vigilance parameters and learning rates. The variation of threshold voltage across the capacitor was also investigated to observe the effect on learning and detection accuracy.

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  Optimization of Solar Array Positioning Actuators for Small Satellites

  Mohamed Ali Alsadig Mohamed

  CubeSats are standard and modularized satellites that have gained widespread implementation among the scientific research community due to their low cost of manufacture and launch. The only source of energy for CubeSat missions are from solar arrays, which are coupled to rechargeable batteries that provide power during the shaded portion of the orbit. The goal of this research is to maximize the energy per weight ratio of solar array designs for a 3U CubeSat. The solar array configurations investigated include rigidly mounted to the CubeSat sides, and deployed with zero, one, and two degree of freedom, active positioning actuation schemes. Numerical models are created for multiple variations of geo-synchronous and sun-synchronous orbits, which are common for CubeSat missions. The results for orbit parameters and energy acquisition for rigid-mounted solar arrays are validated with commercially available orbital mechanics software (SDK). The various solar cell designs are evaluated based on their energy acquisition potential and actuation complexity and weight of design.

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  Propeller and Propeller-in-Wing Thrust Vectoring

  Grace Gabrielle Culpepper

  In this study, we investigate the efficiency of a thrust vectoring system utilizing a set of vanes designed to create forward force at a minimum loss in net thrust, the system itself placed in both a stand-alone propeller configuration and a propeller-in-wing configuration. Both static and wind-on force-based experiments were conducted at the University of Dayton Low Speed Wind Tunnel (UDLSWT) with off-the-shelf R/C propellers. A square propeller sheath was incorporated for static testing and a propeller-wing integrated setup was the focus of wind-on experiments. Sensitivity analysis was conducted to determine the effect on thrust vectoring of vane tilt angle and propeller placement with respect to the upper surface of the integrated wing. Static test results indicated notable improvement in vane performance when placing the vane system in a wing as compared to the stand-alone sheathed design. Thrust vectoring was achieved, along with subsequent changes in pitching moment, by increasing vane deflection angle. Wind tunnel test results of the integrated propeller-in-wing system for the standard 90° pitch orientation indicated successful thrust vectoring below the advance ratio of 0.3, which is practical for most relevant applications. The 75° pitch orientation of the propeller-vane system observed increased thrust vectoring capabilities extending to an advance ratio of 0.7. Sensitivity analysis results revealed that the case of the propeller exposed to the flow freestream outperformed that of the propeller embedded in the test wing in overall efficiency, though the embedded featured a better thrust vectoring capability.

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  Silver Beachfront Exposure: Chloride Film Growth as Corrosion Indicator

  David Rubino

  Atmospheric silver corrosion was examined by galvanostatic reduction, as part of a larger study attempting to model corrosion damage on various metals based on exposure location, time, and conditions. Silver coupons were exposed for a specific amount of time (3-18 months) in different beachfront locations in Florida. These coupons were brought back and the resulting corrosion films were characterized with regards to their chemical composition. The presence of a specific chemical compound (e.g. silver chloride) on the silver coupon is determined through an electrochemical reduction of the coupon. The reduction is the result of the application of a constant current applied to the silver coupon, resulting in a measured voltage that is specific for each chemical compound on the coupon surface. A three-electrode cell was used for the reduction process, with the silver coupon as the working electrode, a platinum mesh as the counter electrode, and a mercurous sulfate electrode (MSE) as the reference, all immersed in a sodium sulfate electrolyte. Using a computer controlled potentiostat, a graphical plot of voltage vs. time was generated. The amount of time that the potential remains at a constant value indicates the reduction of a single chemical species on the surface. This time is then converted to film thickness and is analyzed across the different exposure times and locations. Increasing exposure times of the coupons in the field locations showed increasing chloride film thickness, and exposure at different field sites showed different silver chloride film growth rates. Additionally, seasonal exposure condition changes at each site were observed to result in changes in the chloride film growth rate. These findings will assist in the development of accelerated exposure testing and corrosion modeling for other metals.

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  Simulating Deflection of a Compliant Bistable Mechanism

  Jared L. Dunn

  This research involves the simulation and physical testing of a novel compliant bistable mechanism. Bistable mechanisms are commonly used in switches and other devices that operate in two distinct modes. The mechanism being developed is a single monolithic structure with simple geometry and does not require external components or post-manufacturing at large, or micro, scales. The goal of this research is to develop and refine a simulation process for this mechanism that accurately reflects the internal friction and large displacement caused by this compliant style of actuation. A prototype is presented to facilitate force and displacement measurements to compare against simulation results. The simulation and experimentation will be used to refine a set of scalable design equations for the compliant bistable mechanism.

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  Simulation of the Resin Infusion Process for a Composite T-Joint

  Khalid Saleh Aldhahri

  Resin transfer modeling (RTM) is increasingly used to produce composite materials for several applications. One of the challenges with the manufacturing process is how to fully impregnate the fiber preform in the mold with resin. Limited research has been conducted to investigate the resin flow behavior in critical regions of complex geometry such as the junction between the flange and web in T-joints, referred to as a deltoid (based on shape) or the noodle region (based on the approach used to fill the region with rolled-up fabric). Various approaches taken with respect to manufacturing T-joints include filling such critical regions which insert metal or use rolled-up fabric. However, by using tightly rolled-up fabric a new issue is introduced which relates to fabric permeability differences between the noodle region and main composite structure. This can lead to resin short-circuiting the noodle region prior to its filling, leaving voids in this area. One of the solutions to this problem before designing the mold and selecting process conditions is using computer simulation. This allows for initial viewing of the expected flow patterns and cure profiles before the actual resin injection. In this research, numerical modeling was conducted using PAM-RTM software included a carbon fiber fabric preform, a carbon fabric noodle with various permeability values, and Hexcel RTM6 epoxy resin. The software was used to simulate the isothermal infusion process, as well as the temperature distribution of the mold during infusion and subsequent curing process. Finally, the PAM-Distortion module was used to predict the distortion of the T-Joint after cure due to cure shrinkage and thermal contraction. This is important because in the processing of composite materials, the final geometry is often slightly different than the mold shape after removal due to process induced distortions, which is referred to as either spring-in or spring-back.

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  Six Degrees of Freedom (6DOF) Robotic Additive Manufacturing

  Ajith Kumar Veeraboina

  Additive manufacturing (AM), popularly known as 3D printing, is a technology used to produce a physical part directly from its corresponding digital 3D model design. Existing 3D printing techniques are based on the gantry system and are limited to only three degrees of freedom. The printing is possible only in uni-directional and is anisotropic when force is applied to the printed part. Complex 3D models with overhanging features need support structures in uni-directional printing. In this work, we develop a novel process that addresses the limitations of conventional 3D printing by using two 6DOF manipulators. A simulation model of the manipulators is designed in the Motosim software and build an experimental setup. By replacing the gantry system with one or two 6DOF industrial robotic arms, it will have additional degrees of freedom for multidirectional printing. Furthermore, the support structures can be avoided, and the printed part mechanical properties can be improved.

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  The Potential Benefits of Sustainable Aviation Fuels with High Thermal Stability

  Lily Carolyn Behnke

  Sustainable Aviation Fuels (SAF’s) have proven to be a near term solution to minimizing net anthropogenic gas emissions produced by the aviation sector. While SAFs have the potential to achieve reductions in greenhouse gas emissions, their adoption is currently limited in part by the approval process (ASTM D4054) of developing fuels. Total energy content and thermal stability metrics of a potential SAF can add value and performance benefits. The metric of thermal-oxidative stability within the approval process measures the ability of a fuel to absorb heat without producing undesirable deposits. These coke deposits cause increased spread in exhaust gas temperature around the circumference of the combustor which in turn causes increased combustor emissions that negatively impacts turbine efficiency, and drives up CO2 emissions and fuel cost. Therefore, understanding the thermal stability metrics for SAF candidates is essential to reducing coking related airline maintenance costs, greenhouse gas emissions, and illuminating the full benefit of SAFs.

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  Thermal Energy Production and Heat Exchange between an Electrochemical Cell and Its Surroundings

  Shane Kosir

  Thermal energy production in an electrochemical cell must be controlled to avoid its excessive heating and rupture due to the cell internal pressure rise; especially if the cell electrolyte is a solution of a salt in a liquid solvent. The scheme, used to develop the theoretical formulation presented in this work to predict cell temperature during its discharge, incorporates both the reversible production of thermal energy due to changes in enthalpy of the reactive system and the irreversible production of thermal energy due to cell voltage losses associated with the species transport in the cell electrolyte, electrode components, current collectors, and the electrochemical reactions involving charge transfer at the electrolyte-electrode interfaces. The developed theoretical formulation predicts the cell temperature as a function of time during the cell discharge period under adiabatic and nonadiabatic conditions for a given cell discharge current and its initial temperature. The computed cell temperature versus time data for an ideal (i.e., model) button cell are presented in the form of plots for some discharge currents and are discussed in the light of cell component thermal stability and its safe discharge operation.

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  Topology Optimization Frame Interpreter

  Braeden Jay Windham

  Frames used in aircraft and automotive structures must be rigid and lightweight. With modern software, frame designs that are optimized for stiffness with respect to weight can be readily generated. Manufacturing these frames, however, can be costly and difficult. The purpose of this research is to accept the optimized frame results from the design software and pass them through an interpreter to create a frame that is akin the optimized result, but manufacturable with off-the-shelf components. Along with being more manufacturable, this process also eliminates variation in the final design associated with the frame being interpreted differently by different engineers. This optimization process, called topology optimization, begins with a specified design space, applied loads, and constraints. Within the design space, material is strategically removed in order to maintain the optimal stiffness with respect to weight. From there, the generalized shape is interpreted as an arrangement of members and nodes, which are places that two or more members meet. This information is then passed to a second optimization process that changes the size and geometry of the member and node locations to maintain an optimal shape. With the frame now optimized for stiffness as well as being manufacturable, an automated process generates a design model within SolidWorks with structural tubing and welds so that the physical frame can be created.

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  Toward DLP 3D-Printed Soft Robots: A Stereo DIC Investigation of the Mechanics of Ultra-Stretchable Self-Healing UV-Curable Photopolymers

  Joseph G. Beckett

  Digital light processing (DLP) additive manufacturing (AM) is a recent development in 3D printing where full layers of photo-curable polymers (photoresins) are irradiated and cured with projected ultraviolet (UV) light to create a three-dimensional part layer-by-layer. Recent breakthroughs in polymer chemistry have led to a growing number of ultra-stretchable, self-healing UV-curable elastomeric photoresins, some capable of over 450% elongation at fracture. Coupled with the practical manufacturing advantages of DLP AM, these novel elastomeric photoresins are compelling candidates for numerous exciting applications, ranging from regenerative medicine (e.g., vascular grafts and tissue scaffolds) to soft robotics (the focus of this research). In general, soft robotics refers to the use of “soft” materials (i.e., those with a high degree of flexibility, stretchability, and conformability, such as natural rubber) in robotic devices, producing conformal mechanisms that safely interact with humans and are adept at grasping and manipulating assorted objects. To advance the role of DLP AM in this novel and promising technological space, a fundamental understanding of the mechanical behavior (i.e., deformation and fracture) of UV-curable elastomeric materials over a broad range of loading conditions is requisite. At present, however, this remains an open problem. Thus, the research described herein takes a first step toward addressing this critical technological gap by (a) designing and implementing a stereo digital image correlation (DIC) system optimized for large-deformation soft materials testing; (b) conducting an inaugural experimental test program on a novel self-healing UV-curable elastomer synthesized at the Air Force Research Laboratory; (c) using the resulting mechanical test data to develop working analytical and computational models that facilitate the design, optimization, control, and virtual testing of a prototype soft robot; and (d) validating the models using 3D DIC strain measurements of a full-scale soft robotic actuator.