Insulation system is an inseparable part of electrical equipment. In this study, one of the most important aging factors in insulation systems known as partial discharge (PD) is targeted. PD phenomenon has been studied for more than a century and yet new technologies still demand the investigation of PD impact. Nowadays, electrification is penetrating into various fossil-fuel-based industries such as transportation system that demands the reliability of electrical equipment under various harsh environmental conditions. Due to the lack of knowledge on the behavior of insulation systems, research in this area is intensively needed. The current study probes into the partial discharge phenomenon from two aspects and the groundwork for both aspects are provided by experimentation of multiple PD types. In the first goal, a finite-element analysis (FEA) approach is developed based on measurement data to estimate electric field distribution. The FEA model is coupled with a programming scheme to evaluate PD conditions, calculate PD metrics, and perform statistical analysis of the results. For the second target, it is aimed to use deep neural networks to identify and discriminate different sources of PD. The measurement data are used to generate thousands of phase-resolved PD (PRPD) images that will be used for training deep learning models. To meet the characteristics of the dataset, a deep residual neural network is designed and optimized to discriminate PD sources in an accurate, stable, and time-efficient way. The outcome of this research enhances the reliability of electrical apparatus through a better understanding of the PD behavior and lays a foundation for automatic monitoring of PD sources. / Doctor of Philosophy / Electrical equipment functions properly when its conductive elements are electrically insulated. The science of dealing with insulation systems has become more prominent in recent years due to the novel challenges and circumstances introduced by the rapid electrification trend. As an instance, the electrification trend in transportation systems can impose a multitude of environmental, thermal, and mechanical constraints which were not traditionally considered. These new challenges have led to an accelerated deterioration rate of insulation materials. To address this concern, this study targets the experimentation and modeling of the main aging mechanism in electrical equipment known as partial discharge (PD). A numerical model based on finite-element analysis (FEA) is developed that agrees with the test results and can accurately predict the aging of insulating materials due to the PD phenomenon. Moreover, the growing interest toward electrification of the aviation industry (as a response to the climate change crisis) requires the study of insulating materials under low-pressure (high-altitude) conditions. Theoretical and experimental data confirm the more frequent occurrence of PDs and their higher intensity under low-pressure conditions. Safety of operation is the highest priority in airborne transportation, yet no study has addressed the condition monitoring system as a necessary asset of the electric aircraft. To address this research gap, this work develops a dielectric online condition monitoring system (DOCMS) that actively monitors the deterioration level of insulation using deep learning methods. Based on standardized measurements under low-pressure conditions, the data are preprocessed to train the deep neural network with the pattern of PD activities. The proposed scheme can achieve >82% with short-term signals emitted measured from the system.

This research is a proof-of-concept study to develop a surrogate model, using deep learning (DL), to predict solution error for a given model with a given mesh. For this research, we have taken the von Mises stress contours and have predicted two different types of error indicators contours, namely (i) von Mises error indicator (MISESERI), and (ii) energy density error indicator (ENDENERI). Error indicators are designed to identify the solution domain areas where the gradient has not been properly captured. It uses the spatial gradient distribution of the existing solution for a given mesh to estimate the error. Due to poor meshing and nature of the finite element method, these error indicators are leveraged to study and reduce errors in the finite element solution using an adaptive remeshing scheme. Adaptive re-meshing is an iterative and computationally expensive process to reduce the error computed during the post-processing step. To overcome this limitation we propose an approach to replace it using data-driven techniques. We have introduced an image processing-based surrogate model designed to solve an image-to-image regression problem using convolutional neural networks (CNN) that takes a 256 × 256 colored image of von mises stress contour and outputs the required error indicator. To train this model with good generalization performance we have developed four different geometries for each of the three case studies: (i) quarter plate with a hole, (b) simply supported plate with multiple holes, and (c) simply supported stiffened plate. The entire research is implemented in a three phase approach, phase I involves the design and development of a CNN to perform training on stress contour images with their corresponding von Mises stress values volume-averaged over the entire domain. Phase II involves developing a surrogate model to perform image-to-image regression and the final phase III involves extending the capabilities of phase II and making the surrogate model more generalized and robust. The final surrogate model used to train the global dataset of 12,000 images consists of three auto encoders, one encoder-decoder assembly, and two multi-output regression neural networks. With the error of less than 1% in the neural network training shows good memorization and generalization performance. Our final surrogate model takes 15.5 hours to train and less than a minute to predict the error indicators on testing datasets. Thus, this present study can be considered a good first step toward developing an adaptive remeshing scheme using deep neural networks. / Master of Science / This research is a proof-of-concept study to develop an image processing-based neural network (NN) model to solve an image-to-image regression problem. In finite element analysis (FEA), due to poor meshing and nature of the finite element method, these error indicators are used to study and reduce errors. For this research, we have predicted two different types of error indicator contours by using stress images as inputs to the NN model. In popular FEA packages, adaptive remeshing scheme is used to optimize mesh quality by iteratively computing error indicators making the process computationally expensive. To overcome this limitation we propose an approach to replace it using convolutional neural networks (CNN). Such neural networks are particularly used for image based data. To train our CNN model with good generalization performance we have developed four different geometries with varying load cases. The entire research is implemented in a three phase approach, phase I involves the design and development of a CNN model to perform initial level training on small image size. Phase II involves developing an assembled neural network to perform image-to-image regression and the final phase III involves extending the capabilities of phase II for more generalized and robust results. With the error of less than 1% in the neural network training shows good memorization and generalization performance. Our final surrogate model takes 15.5 hours to train and less than a minute to predict the error indicators on testing datasets. Thus, this present study can be considered a good first step toward developing an adaptive remeshing scheme using deep neural networks.

Experimental Spatial Dynamics Modeling (ESDM) is the new process of constructing a three dimensional, complex-valued dynamic model of a harmonically vibrating structure using numerical models and laser-based experimental data obtained from a Scanning Laser Doppler Vibrometer (SLDV). In ESDM process, a finite element formulation is used to construct a numerical model of a structure. A conventional finite element such as rod, beam, or plate element, can be used to construct the numerical model of a structure from its mid-plane. In this research, the damped flat shell element is developed to construct the numerical models of a cantilever beam and a simply supported flat plate. The velocity transformation function developed in this research will make possible to use the FE model, constructed by the damped flat shell element, and the laser-based experimental data within a framework of ESDM in the consistent manner. / Master of Science

Deployable structures serve a large number of space missions. They are vital since spacecraft are launched by placing them inside launch vehicle payload fairings of limited volume. Traditional spacecraft design often involves large components. These components could have power, communication, or optics applications and include booms, masts, antennas, and solar arrays. Different stowing methods are used in order to reduce the overall size of a spacecraft. Some examples of stowing methods include simple articulating, more complex origami inspired folding, telescoping, and rolling or wrapping. Wrapping of a flexible component could reduce the weight by eliminating joints and other components needed to enable some of the other mechanisms. It also is one of the most effective methods at reducing the compaction volume of the stowed deployable. In this study, a generic unfurlable structure is optimized for maximum natural frequency at its fully deployed configuration and minimal strain energy in its stowed configuration. The optimized stowed structure is then deployed in simulation. The structure consists of a rectangular panel that tightly wraps around a central cylindrical hub for release in space. It is desired to minimize elastic energy in the fully wrapped panel and hinge to ensure minimum reaction load into the spacecraft as it deploys in space, since that elastic energy stored at the stowed position transforms into kinetic energy when the panel is released and induces a moment in the connected spacecraft. It is also desired to maximize the fundamental frequency of the released panel as a surrogate for the panel having sufficient stiffness. Deployment dynamic analysis of the finite element model was run to ensure satisfactory optimization formulation and results. / Master of Science / Spacecraft, or artificial satellites, do not fly from earth to space on their own. They are launched into their orbits by placing them inside launch vehicles, also known as carrier rockets. Some parts or components of spacecraft are large and cannot fit in their designated space inside launch vehicles without being stowed into smaller volumes first. Examples of large components on spacecraft include solar arrays, which provide power to the spacecraft, and antennas, which are used on satellite for communication purposes. Many methods have been developed to stow such large components. Many of these methods involve folding about joints or hinges, whether it is done in a simple manner or by more complex designs. Moreover, components that are flexible enough could be rolled or wrapped before they are placed in launch vehicles. This method reduces the mass which the launch vehicle needs to carry, since added mass of joints is eliminated. Low mass is always desirable in space applications. Furthermore, wrapping is very effective at minimizing the volume of a component. These structures store energy inside them as they are wrapped due to the stiffness of their materials. This behavior is identical to that observed in a deformed spring. When the structures are released in space, that energy is released, and thus, they deploy and try to return to their original form. This is due to inertia, where the stored strain energy turns into kinetic energy as the structure deploys. The physical analysis of these structures, which enables their design, is complex and requires computational solutions and numerical modeling. The best design for a given problem can be found through numerical optimization. Numerical optimization uses mathematical approximations and computer programming to give the values of design parameters that would result in the best design based on specified criterion and goals. In this thesis, numerical optimization was conducted for a simple unfurlable structure. The structure consists of a thin rectangular panel that wraps tightly around a central cylinder. The cylinder and panel are connected with a hinge that is a rotational spring with some stiffness. The optimization was solved to obtain the best values for the stiffness of the hinge, the thickness of the panel, which is allowed to vary along its length, and the stiffness or elasticity of the panel's material. The goals or objective of the optimization was to ensure that the deployed panel meets stiffness requirement specified for similar space components. Those requirements are set to make certain that the spacecraft can be controlled from earth even with its large component deployed. Additionally, the second goal of the optimization was to guarantee that the unfurling panel does not have very high energy stored while it's wrapped, so that it would not cause large motion the connected spacecraft in the zero gravity environments of space. A computer simulation was run with the resulting hinge stiffness and panel elasticity and thickness values with the cylinder and four panels connected to a structure representing a spacecraft. The simulation results and deployment animation were assessed to confirm that desired results were achieved.

[ES] Las cargas de impacto son la fuente primaria de lesiones en la cabeza y pueden resultar en un rango de traumatismo desde leve hasta severo. Debido a la existencia de múltiples entornos en los que se pueden desencadenar lesiones por impacto (accidentes automovilísticos, deportes, caídas accidentales, violencia), éstas pueden afectar potencialmente a toda la población independientemente de su estado de salud. Pese al creciente esfuerzo en investigación para comprender la biomecánica de las lesiones por traumatismo en la cabeza, todavía no es del todo posible realizar predicciones precisas ni prevenir estos eventos. En esta Tesis, se han estudiado algunos aspectos del comportamiento ante impacto de los diferentes tejidos biológicos involucrados mediante el desarrollo de un modelo numérico de cabeza humana a partir de imágenes de tomografía computerizada (TAC). Se han realizado simulaciones en elementos finitos (EF) de ensayos experimentales de la literatura con el fin de validar el modelo numérico desarrollado, estableciendo unas propiedades mecánicas adecuadas para cada uno de sus constituyentes. De esta manera se puede adquirir una predicción adecuada del riesgo de sufrir daños. Parte de esta Tesis se centra en el entorno balístico, específicamente en cascos de combate antibalas, los cuales son susceptibles de causar traumatismo craneoencefálico debido a la elevada deformación que sufren durante el impacto. Previamente al estudio de estos fenómenos de alta velocidad, se han realizado ensayos experimentales y numéricos para caracterizar la respuesta mecánica de algunos materiales compuestos ante impacto de baja velocidad. Al principio de esta Tesis se ha realizado una revisión del estado del arte acerca de los criterios existentes para cuantificar el trauma craneoencefálico.Este es un aspecto clave para las simulaciones numéricas, ya que la idoneidad de algunos de estos criterios para la predicción de lesiones cerebrales todavía es un debate abierto. Mediante EF se han realizado simulaciones de impactos balísticos en una cabeza protegida con un casco de combate. Mediante la posterior aplicación de diferentes criterios de daño sobre los resultados obtenidos se ha evaluado el nivel de protección que aseguran los protocolos de aceptación de cascos de combate, así como las estrategias para determinar su tallaje. Se ha demostrado que las normativas existentes para cascos de combate son capaces de mitigar algunos mecanismos de trauma pero no logran prevenir otros como los gradientes de presión intracraneales. Además, se ha demostrado que algunas de las estrategias de tallaje más comúnmente adoptadas por los fabricantes, como producir un solo tamaño de calota, deberían ser reconsideradas ya que existe un mayor riesgo de traumatismo cuando la distancia entre la cabeza y la calota del casco no es suficiente. Siguiendo la línea de protecciones personales, algunos de los materiales compuestos comúnmente empleados en la industria armamentística se han combinado para crear distintas configuraciones de calota para optimizar la relación entre peso del casco y protección para la cabeza. Materiales ligeros como el UHMWPE han resultado en un comportamiento menos eficiente que el de los apilados de tejido de aramida a la hora de limitar la BFD (deformación máxima en la calota del casco en la zona de impacto). Hacia el final de la Tesis se presenta un modelo numérico de cabeza humana detallado, que incluye treinta y tres de las estructuras anatómicas principales. Dicho modelo se ha desarrollado para la simulación de un accidente ecuestre en el que aparecen múltiples lesiones craneoencefálicas. Principalmente, se pretende establecer un criterio mecánico para predecir el hematoma subdural (HS) basado en la ruptura de los vasos sanguíneos intracraneales. Se ha propuesto un valor umbral de ruptura en tensiones de 3.5 MPa, pero tanto este límite como la localización del vaso dañado son altamen / [CA] Les càrregues d'impacte son la font primària de lesions al cap i poden resultar en un rang de severitat des de lleu a greu. Degut als múltiples entorns en que poden desencadenar-se lesions per impacte (accidents automobilístics, esports, caigudes accidentals, violència), aquestes poden afectar potencialment a tota la població independentment del seu estat de salut. Malgrat el creixent esforç en investigació per comprendre la biomecànica de les lesions per traumatisme al cap, encara no és del tot possible realitzar prediccions precises ni prevenir aquestos esdeveniments. En aquesta Tesi, s'han estudiat alguns aspectes del comportament a impacte dels diferents teixits biològics involucrats mitjançant el desenvolupament d'un model numèric de cap humà a partir d'imatges de tomografia computeritzada (TAC). S'han realitzat simulacions en elements finits (EF) d'assajos experimentals de la literatura amb la finalitat de validar el model numèric desenvolupat, establint unes propietats mecàniques adequades per a cadascun dels seus constituents. D'aquesta manera es pot aconseguir una predicció del risc de sofrir danys traumàtics. Part d'aquesta Tesi es centra en l'entorn balístic, específicament en cascs de combat antibales, els quals són susceptibles de causar traumatisme degut a l'elevada deformació que sofrixen durant l'impacte. Previament a l'estudi d'aquests fenòmens d'alta velocitat, s'han realitzat assajos experimentals i numèrics per a caracteritzar la resposta mecànica d'alguns materials compostos en condicions d'impacte a baixa velocitat. Al començament d'aquesta Tesi s'ha realitzat una revisió de l'estat de l'art sobre els criteris existents per quantificar el trauma cranioencefàlic. Aquest és un aspecte clau per a les simulacions numèriques, ja que l'utilitat d'alguns d'aquestos criteris per a la predicció de lesions cerebrals és encara un debat obert. Mitjançant EF s'han realitzat simulacions numèriques d'impactes balístics en un cap protegit amb un casc de combat. Gràcies a la posterior aplicació de diferents criteris de dany sobre els resultats obtinguts s'ha evaluat el nivell de protecció que asseguren els protocols d'acceptació de cascs de combat, així com les estratègies per a determinar les seues talles. S'ha demostrat que les normatives existents són capaces de mitigar alguns mecanismes de trauma però no aconseguixen prevenir altres com els gradients de pressions intracranials. A més, s'ha demostrat que algunes estratègies per determinar les talles més comunament adoptades pels fabricants (com produir només un tamany de calota i adaptar el gruix de les escumes interiors a les diferents dimensions dels subjectes) haurien de ser reconsiderades ja que existeix un major risc de traumatisme quan la distància entre el cap i la calota del casc no és suficient. Seguint la línia de proteccions personals, alguns dels materials compostos comunament utilitzats en la indústria de l'armament s'han combinat per a crear distintes possibles configuracions de calota amb la finalitat d'optimitzar la relació entre pes i protecció. Materials lleugers com l'UHMWPE han resultat en un comportament menys eficient que el d'apilats de teixit d'aramida a l'hora de limitar la BFD (deformació màxima a la calota del casc a la zona d'impacte). Cap al final de la Tesi es presenta un model numèric detallat de cap humà, que inclou trenta-tres de les estructures anatòmiques principals. Aquest model s'ha desenvolupat per a la simulació d'un accident eqüestre en el qual apareixen múltiples lesions cranioencefàliques. Principalment, es pretén establir un criteri mecànic per a la predicció de l'hematoma subdural (HS) basat en la ruptura dels vasos sanguinis intracranials. S'ha proposat un valor umbral de ruptura en tensions de 3.5 MPa, pero tant aquest límit com la ubicació del vas danyat són altament dependents de l'anatomia específica de cada subjecte. / [EN] Impact loading is the primary source of head injuries and can result in a range of trauma from mild to severe. Because of the multiple environments in which impact-related injuries can take place (automotive accidents, sports, accidental falls, violence), they can potentially affect the entire population regardless of their health conditions. Despite the increasing research effort on the understanding of head impact biomechanics, accurate prediction and prevention of traumatic injuries has not been completely achieved. In this Thesis, some aspects of the impact behaviour of the different biological tissues involved have been analysed through the development of a numerical human head model from Computed Tomography (CT) images. FE simulations of experimental tests from the literature have been performed and enhanced the validation of the head model through the establishment of proper material laws for its constituents, which enable adequate prediction of injury risks. Part of this Thesis focuses on the ballistic environment, especifically in bulletproof composite helmets, which are susceptible to cause blunt injuries to the head because of their large deformation during impact. Prior to the study of these high-speed impacts, experimental tests and finite element (FE) models have been performed to characterise the mechanical response of composite materials subjected to low velocity impact. The implementation of a continuum damage mechanics approach coupled to a Hashin failure criterion and surface-to-surface cohesive relations to the numerical model provided a good matching with the impact behaviour obtained experimentally, capturing the principal damage mechanisms. A review of the head injury criteria currently available in the literature has been performed at the beginning of this Thesis. This is a key issue for the numerical simulations, as the suitability of some criteria to predict head injuries is still an open question. Numerical simulation of ballistic impacts on a human head protected with a combat helmet has been conducted employing explicit FE analysis. The level of protection ensured by helmet acceptance protocols as well as their sizing strategies have been studied and discussed by means of the application of different mechanical-based head injury criteria. It has been demonstrated that current helmet testing standards do mitigate some specific forms of head trauma but fail to prevent other injury mechanisms such as the intracranial pressure gradients within the skull. Furthermore, it has been demonstrated that some well-established helmet sizing policies like manufacturing one single composite shell and adapting the thickness of the interior pads to the different head dimensions should be reconsidered, as there is a great risk of head injury when the distance between the head and the helmet shell (stand-off distance) is not sufficient. Following the line of personal protections, some composite materials commonly employed in the soft body armour industry have been combined into different helmet shells configurations to optimise the ratio of weight-to-head protection. Light materials like UHMWPE appear to be less efficient than integral woven-aramid lay-ups in the limitation of the backface deformation (BFD), the maximum deformation sustained by the helmet at the impact site. A detailed head numerical model including thirty-three of its main anatomical structures has been developed for the simulation of an equestrian accident that resulted in many head injuries. Above all, the establishment of a mechanical criterion for the prediction of subdural hematona (SDH) based on the rupture of the head blood vessels is intended. A stress threshold for vein rupture has been set on 3.5 MPa, but both this limit and the location of vessel failure are highly dependent on the specific anatomy of the subject's vascularity. / Palomar Toledano, M. (2019). Assessment of head injury risk caused by impact using finite element models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/135254

<p dir="ltr">Energetic materials are substances with considerable amounts of energy that can detonate under shock, pressure, or high temperature conditions, making them acceptable candidates for applications such as explosives, propellants, and fuels. One example of an energetic material is the explosive known as cyclotetramethylene tetranitramine (HMX). When subjected to impact, HMX can undergo thermo-mechanical responses that may lead to deflagration or, in the most severe cases, detonation. Due to the multiscale nature of these phenomena and the varying impact velocity magnitudes, replicating such responses can be challenging or even unattainable in an experimental setting. Consequently, computational models capable of predicting real-world conditions beyond experimental reach are highly valuable to the explosives research community.</p><p dir="ltr">This study continues the work from previous analyses (Duarte 2021) by developing a finite element model of HMX combined with an aluminum rod, predicting damage evolution and dynamic response under shock compression. The impact velocities applied in the model ranged from 0.1 km/s to 0.6 km/s using three different crystal orientations to investigate their corresponding effects. The results indicate that impacting in the direction normal to the HMX plane [110], which exhibited high levels of plastic energy, had the most resistant to cracking near the HMX-aluminum interface. Furthermore, these findings show that elastic energy accumulation is the primary driver in this analysis of crack propagation and bulk damage in HMX crystals.</p><p dir="ltr">Additionally, the HMX and aluminum results were compared against two additional models: a homogeneous HMX sample without discontinuities and an HMX sample with a void in place of the aluminum rod. Comparisons of the models show that the most severe damage field occurs in the void model, while the shock wave accelerated through the aluminum rod but also decelerated significantly in the presence of a void due to wave refraction at traction free boundaries. These results provide another level of understanding into the role of material interfaces and voids in the dynamic response of HMX under shock loading. Experimental validation of these findings is recommended for future studies, assuming the conditions are feasible for testing.</p>

Non-ductile Reinforced Concrete Frames (RCF) with and without Unreinforced Masonry (URM) infills can be found in many places around the world including the Western United States, Eastern Europe, Asia and Latin America. These structures can have an unsatisfactory seismic performance which may even lead to collapse due to brittle failure modes. Furthermore, the effect of the infills on the seismic response of the structural system is not always accounted for in analysis and design. At present, there is no consensus on whether masonry infills are beneficial (by increasing the resistance of the system) or detrimental (by leading to brittle failure modes) for RCF construction. This study focuses on the development of a simplified modeling approach for non-ductile RCF with URMI that combines the simplicity of strut-and-tie models with the accuracy of Nonlinear Finite Element Analysis (NLFEA). Despite the fact that NLFEA procedures are the most advanced way to address the structural analysis of RCF with URM infills, their conceptual complexity and computational cost may hinder their widespread adoption as an analysis and design tool. At the same time, simplified methods, such as those based on the equivalent strut concept, may be overly crude and neglect essential aspects of the nonlinear response. To address the need for an adequately accurate, but computationally and conceptually efficient analysis method, this study establishes a novel method for planar RCF with URM infills subjected to lateral loads. The method, which is based on the Nonlinear Truss Analogy (NLTA) is shown to have an accuracy comparable to that of NLFEA. Specifically, the method is shown to adequately capture the strength and stiffness degradation and the damage patterns while entailing a reduced computational cost (compared to that of NLFEA). The proposed method is expected to bridge the gap between overly crude equivalent strut models and computationally expensive NLFEA. / Ph. D.

This thesis presents a comprehensive investigation into the feasibility and optimization of self-piercing riveting (SPR) for joining high-ductility die-cast aluminum alloy Nemalloy HE700 in F temper (as-cast) condition to dissimilar sheet materials, namely wrought aluminum alloy 6082-T6 and dual-phase steel DP600. The study demonstrates successful SPR joining of HE700 to these materials, with optimized process parameters and joint quality meeting automotive industry standards. Systematic experimental studies were conducted to investigate the effects of key SPR process parameters, including die geometry, ring groove depth, rivet hardness, and length, on joint quality and performance. Microstructural characterization revealed distinct patterns of grain flow and localized hardening in HE700 around the rivet and die features, providing insights into its deformation characteristics. Finite element simulations, incorporating advanced material models such as Johnson-Cook plasticity and failure for AA6082 and DP600, and Voce hardening with Gurson-Tvergaard-Needleman void damage model for HE700, were developed and extensively validated against experimental results. The simulations accurately predicted potential failure sites in HE700, aligning with experimental observations of crack initiation. Numerical parametric studies demonstrated the intricate effects of process parameters and material properties on the stress and strain distributions, material flow, and damage accumulation during SPR. The research contributes to the growing body of knowledge on advanced joining techniques for dissimilar materials, supporting vehicle lightweighting efforts. It establishes a comprehensive methodology integrating experiments, microstructural characterization, and simulations for studying and optimizing SPR processes for low ductility casting alloys, serving as a blueprint for future research and industrial implementation. The findings demonstrate the viability and potential of SPR technology for integrating high-ductility die-cast aluminum alloy HE700 into lightweight automotive body structures, paving the way for its wider industrial adoption. / Thesis / Master of Applied Science (MASc) / This research explores the potential of using a novel high-ductility aluminum alloy, Nemalloy HE700, in self-piercing riveting (SPR) - a modern joining technique for automotive manufacturing. The study aims to optimize the SPR process for joining HE700 to other commonly used automotive materials, such as aluminum alloys and high-strength steels, without compromising joint quality. By conducting practical experiments and computer simulations, the research identifies the best process parameters, such as rivet design and die shape, that result in strong, reliable joints meeting automotive industry standards. The findings demonstrate the successful use of HE700 in SPR, offering a promising solution for creating lighter, more fuel-efficient vehicles. This work contributes to the development of advanced joining technologies for sustainable transportation, making vehicles more environmentally friendly while maintaining high performance and safety standards.

Monitoring accurate temperature and pressure profiles in harsh environments is currently in high demand in aerospace gas turbine engines and nuclear reactor simulators. Having the ability to measure both quantities continuously over a region, without thermal coupling, using a sensor with a small size (envelope) is also highly desirable. Currently available MEMS (microelectromechanical systems) provide effective small scale pressure and temperature measurement devices, however, they have only been shown to be effective up to 600C and lack the ability to perform distributed measurements unless combined with fiber-optic techniques. In general, fiber-optics provide many advantages over electrical based sensors and are the ideal choice for high temperature regimes and distributed sensing. In this thesis, preliminary designs and suggested future work are presented for a sensor built within an 3.175 mm radius envelope and capable of distributed pressure and temperature sensing up to temperatures reaching 800C. Finite element analysis via ANSYS, along with analytical verification models have been used for the design evolution. Diaphragm based designs, seem to provide easy fabrication methods and good sensitivity, however, for this design to be realized at high temperature operation, a robust bonding method must be chosen to avoid unwanted deformation due to misfit strains. / Master of Science

Transparent laminates are widely used for body armor, goggles, windows and windshields. Improved understanding of their deformations under impact loading and of energy dissipation mechanisms is needed for minimizing their weight. This requires verified and robust computational algorithms and validated mathematical models of the problem. Here we have developed a mathematical model for analyzing the impact response of transparent laminates made of polymeric materials and implemented it in the finite element software LS-DYNA. Materials considered are polymethylmethacrylate (PMMA), polycarbonate (PC) and adhesives. The PMMA and the PC are modeled as elasto-thermo-visco-plastic and adhesives as viscoelastic. Their failure criteria are stated and simulated by the element deletion technique. Values of material parameters of the PMMA and the PC are taken from the literature, and those of adhesives determined from their test data. Constitutive equations are implemented as user-defined subroutines in LS-DYNA which are verified by comparing numerical and analytical solutions of several initial-boundary-value problems. Delamination at interfaces is simulated by using a bilinear traction separation law and the cohesive zone model. We present mathematical and computational models in chapter one and validate them by comparing their predictions with test findings for impacts of monolithic and laminated plates. The principal source of energy dissipation of impacted PMMA/adhesive/PC laminates is plastic deformations of the PC. In chapter two we analyze impact resistance of doubly curved monolithic PC panels and delineate the effect of curvature on the energy dissipated. It is found that the improved performance of curved panels is due to the decrease in the magnitude of stresses near the center of impact. In chapter three we propose constitutive relations for finite deformations of adhesives and find values of material parameters by considering test data for five portions of cyclic loading. Even though these values give different amounts of energy dissipated in the adhesive, their effect on the computed impact response of PMMA/adhesive/PC laminates is found to be minimal. In chapter four we conduct sensitivity analysis to identify critical parameters that significantly affect the energy dissipated. The genetic algorithm is used to optimally design a transparent laminate in chapter five. / Ph. D.