Mechanical Engineering
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Item FATIGUE DEGRADATION SENSING WITH SURFACE MOUNTED CONJUGATE-STRESS (CS) SENSOR(2024) Bascolo, Manuel; Dasgupta, Abhijit AD; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study advances a unique dual-stiffness mechanical sensor concept in the literature (termed Conjugate-stress sensor), to evaluate and validate the effectiveness of surface-mounted versions of the Conjugate Stress (CS) sensor in detecting cyclic fatigue progression under both quasi-static axial cycling and dynamic flexural loading conditions. The CS sensor’s capability to recognize fatigue was examined by observing the correlation between its readings and the host material's stiffness. Low carbon steel dog bone coupons and welded cruciform specimens were subjected to quasi-static cyclic fatigue testing. Additionally, dynamic flexural tests were performed on low carbon steel cantilever beams and welded cruciform specimens, which underwent random vibration fatigue testing. The results demonstrated that CS sensors consistently track fatigue damage, offering a promising potential for in-situ structural health monitoring and for providing continuous, real-time estimations of the remaining useful life (RUL) of materialsItem TUBE-LOAD MODEL AS A DIGITAL TWIN FOR ABDOMINAL AORTIC ANEURYSM PATIENTS(2024) Kim, Donghyeon; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the abnormal dilation of the aorta, which can lead to vessel rupture and high mortality rates (>80%). Alarmingly, AAA is often asymptomatic and can remain undetected until it reaches a critical size or ruptures. Current methods for diagnosing and monitoring AAA, such as ultrasound, CT, and MRI, are effective but expensive for regular use and require specialized operators. These limitations hinder the widespread use of imaging-based techniques for regular AAA screening and surveillance. Therefore, creating a need for more accessible, affordable, and convenient tools to detect AAA in its early stages, monitor its progression, and assess treatment efficacy. This thesis explores the potential of tube-load (TL) model to non-invasively monitor AAA progression by analyzing arterial pressure waveforms, which change in response to aneurysm-induced alterations in aortic geometry and mechanical properties. These changes are captured and revealed by the parameters of the TL model. To evaluate the TL model’s capability to monitor AAA, we applied it to carotid and femoral artery tonometry waveforms collected from 79 subjects, including both controls and AAA subjects, as well as a subset of 35 AAA subjects before and after endovascular repair (EVAR) surgery. Our analysis showed that the TL model could fit the waveforms from pre-EVAR AAA subjects as accurately as those from controls and post-EVAR. Moreover, the TL model parameters exhibited physiologically explainable changes consistent with the structural changes of the aorta associated with AAA and its treatment. These findings suggest that the TL model has the potential as a digital twin to enable convenient and cost-effective personalized AAA monitoring.Item MOLD PROCESS INDUCED RESIDUAL STRESS PREDICTION USING CURE EXTENT DEPENDENT VISCOELASTIC BEHAVIOR(2024) Phansalkar, Sukrut Prashant; Han, Bongtae; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Epoxy molding compounds (EMC) are widely used in encapsulation of semiconductor packages. Encapsulation protects the package from physical damage or corrosion due to harsh environments. Molding processes produce residual stresses in encapsulated components. They are combined with the stresses caused by the coefficient of thermal expansion (CTE) mismatch to dictate the final warpage at room and reflow temperatures, which must be controlled for fabrication of redistribution layer (RDL) as well as yield during assembly. During molding process, EMC is continuously curing and the mechanical properties continue to evolve; more specifically, the equilibrium modulus and the relaxation modulus. The former defines behavior after complete relaxation while the latter describes the transient behavior. It is thus necessary to measure cure-dependent viscoelastic properties of EMC to be able to determine mold induced residual stresses accurately. This is the motivation for this thesis. In this thesis, a set of novel methodologies are developed and implemented to quantify a complete set of cure-dependent viscoelastic properties, including the fully cured properties. Firstly, an advanced numerical scheme has been developed to quantify cure kinetics of thermosets with both single and dual-reaction systems. Secondly, a unique methodology is proposed to measure the viscoelastic bulk modulus -K(t,T) of EMC using hydrostatic testing. The methodology is implemented with a unique test setup based on inert gas. The results of viscoelastic testing along with the shear modulus (G) and bulk modulus (K) master curves and temperature-dependent shift factors (a(T)) of fully-cured EMC are presented. Thirdly, a novel test methodology utilizing monotonic testing has been developed to measure two sets of equilibrium moduli of EMC as a function of cure extent (p). The main challenge for the measurements is that the equilibrium moduli could only be measured accurately only when the EMC has fully relaxed. The temperatures for complete relaxation typically occur above the glass transition temperature, Tg (p), where the curing rate is also high. A special measurement procedure is developed, through which the EMC moduli above Tg can be determined quickly at a near isocure state. Viscoelastic testing of partially-cured EMC is followed to determine the cure-dependent shift factors of EMC. The test durations have to be very long (several hours) and it is conducted below Tg (p) of the EMC where the reaction is slow (under diffusion-control) Finally, a numerical scheme that can utilize the measured cure-dependent viscoelastic properties is developed. It is implemented to predict the residual stress evolution of molded packages during and after molding processes.Item VOLUMETRIC SOLAR ABSORBING FLUIDS AND THEIR APPLICATIONS IN TWO-PHASE THERMOSYPHON(2024) Zhou, Jian; Yang, Bao; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A two-phase thermosyphon is a passive system utilizing gravity to transfer working fluids. The working fluids of a two-phase thermosyphon must undergo vaporization and condensation in the same system. Two-phase thermosyphons can also be used as solar collectors. Traditional solar collectors utilize surface absorbers to convert incident solar radiation into thermal energy, but those systems feature a large temperature difference between the surface absorbers and heat transfer fluids, resulting in a reduction in the overall thermal efficiency. Volumetric solar absorbing fluids serve both as solar absorbers and heat transfer fluids, therefore significantly improving the overall efficiency of solar collectors. Comparing to pure fluids, nanofluids possess both enhanced thermal conductivity and solar absorption capacity as volumetric absorbing fluids. Nanofluids, when serving as volumetric solar absorbing fluids, are so far reported to work only at relatively low temperatures and in a single-phase heat transfer regime due to stability issue. This research investigates the possibility of using nanofluids, especially graphene oxide (GO) nanofluids, as volumetric solar absorbing fluids in two-phase thermosyphons. Despite their reputation as both stable and solar absorptive among nanofluids, graphene oxide nanofluids still deteriorate quickly under boiling-condensation processes (~100 °C). The solar transmittance of the GO nanofluids declines from 38 to 4%, during the first 24 h of testing. Further investigation shows that the stability deterioration is caused by the thermal reduction of GO nanoparticles, which mainly featured with de-carboxylation and de-hydroxylation. A commercial dye named acid black 52, when dissolved in water, exhibits great broadband solar absorption properties and excellent stability. It remains stable for over 199 days in two-phase thermosyphon, and their transmittance in solar spectral region varies less than 9%. The stability of acid black 52 aqueous solution is further confirmed with the 191-day enhanced radiation test, as it shows less than 5% transmittance change in solar spectral region.Item OPTIMAL PROBING OF BATTERY CYCLES FOR MACHINE LEARNING-BASED MODEL DEVELOPMENT(2024) Nozarijouybari, Zahra; Fathy, Hosam HF; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the problems of optimizing the selection of the datasets and experiments used for parameterizing machine learning-based electrochemical battery models. The key idea is that data selection, or “probing” can empower such models to achieve greater fidelity levels. The dissertation is motivated by the potential of battery models to enable theprediction and optimization of battery performance and control strategies. The literature presents multiple battery modeling approaches, including equivalent circuit, physics-based, and machine learning models. Machine learning is particularly attractive in the battery systems domain, thanks to its flexibility and ability to model battery performance and aging dynamics. Moreover, there is a growing interest in the literature in hybrid models that combine the benefits of machine learning with either the simplicity of equivalent circuit models or the predictiveness of physics-based models or both. The focus of this dissertation is on both hybrid and purely data-driven battery models. Moreover, the overarching question guiding the dissertation is: how does the selection of the datasets and experiments used for parameterizing these models affect their fidelity and parameter identifiability? Parameter identifiability is a fundamental concept from information theory that refers to the degree to which one can accurately estimate a given model’s parameters from input-output data. There is substantial existing research in the literature on battery parameter identifiability. An important lesson from this literature is that the design of a battery experiment can affect parameter identifiability significantly. Hence, test trajectory optimization has the potential to substantially improve model parameter identifiability. The literature explores this lesson for equivalent circuit and physics-based battery models. However, there is a noticeable gap in the literature regarding identifiability analysis and optimization for both machine learning-based and hybrid battery models. To address the above gap, the dissertation makes four novel contributions to the literature. The first contribution is an extensive survey of the machine learning-based battery modeling literature, highlighting the critical need for information-rich and clean datasets for parameterizing data-driven battery models. The second contribution is a K-means clustering-based algorithm for detecting outlier patterns in experimental battery cycling data. This algorithm is used for pre-cleaning the experimental cycling datasets for laboratory-fabricated lithium-sulfur (Li-S) batteries, thereby enabling the higher-fidelity fitting of a neural network model to these datasets. The third contribution is a novel algorithm for optimizing the cycling of a lithium iron phosphate (LFP) to maximize the parameter identifiability of a hybrid model of this battery. This algorithm succeeds in improving the resulting model’s Fisher identifiability significantly in simulation. The final contribution focuses on the application of such test trajectory optimization to the experimental cycling of commercial LFP cells. This work shows that test trajectory optimization is s effective not just at improving parameter identifiability, but also at probing and uncovering higher-order battery dynamics not incorporated in the initial baseline model. Collectively, all four of these contributions show the degree to which the selection of battery cycling datasets and experiments for richness and cleanness can enable higher-fidelity data-driven and hybrid modeling, for multiple battery chemistries.Item DESIGN NOVELTY EVALUATION THROUGH ORDINAL EMBEDDING: COMPARISON OF NOVELTY AND TRIPLET ERRORS(2024) Keeler, Matthew Garrett; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A practical and well-studied method for computing the novelty of a design is to construct an embedding via a collection of pairwise comparisons between items (called triplets), and use distances within that embedding to compute which designs are farthest from the center. These triplet comparisons are posed in the form of "Is Design A closer to Design B or Design C?'', and inform the placement of designs in the similarity-space embedding. This method of creating an embedding from non-metric relationship comparisons is known as ordinal embedding. Unfortunately, ordinal embedding methods can require a large number of triplets before their primary error measure--the proportion of violated triplet comparisons--converges. But if our goal is accurate novelty estimation, is it really necessary to fully minimize all triplet violations? Can we extract useful information regarding the novelty of all or some items using fewer triplets than existing convergence rates on the saturation of triplet violations might imply? This thesis addresses this question by studying the relationship between triplet violation error and novelty score error when using ordinal embeddings. We find that estimating the novelty of a set of items via ordinal embedding can require significantly fewer human-provided triplets than is needed to converge the triplet error, and that this effect is modulated by the type of triplet sampling method (random versus uncertainty-informed active sampling). Having learned this, we propose the use of a custom metric we call the 'Expected Model Change' (EMC) which we use to observe when novelty information in the embedding has stopped updating under newly labeled triplets, so that conservative bounding functions need not be used. Moreover, to avoid the dangers of low accuracy in selecting the dimension of the ordinal embedding, we propose use of the Expected Model Change for tuning the embedding dimension to an appropriate value. In this framework, we explore the convergence properties of ordinal embeddings reconstructed from triplets taken from a variety of synthetic and real-world design spaces.Item DIRECT LASER WRITE PROCESSES FOR SPIDER INSPIRED MICROHYDRAULICS AND MULTI-SCALE LIQUID METAL DEVICES(2023) Smith, Gabriel Lewis; Bergbreiter, Sarah; Sochol, Ryan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Direct Laser Write (DLW) through two-photon polymerization (2PP) empowers us to delveinto the realm of genuine three-dimensional design complexity for microsystems, enabling features smaller than a single micrometer. This dissertation develops two novel fabrication processes that leverage DLW for functional fluidic microsystems. In the first process, we are inspired by arachnids that use internal hemolymph pressure to actuate extension in one or more of their leg joints. The inherent large foot displacement-to-body length ratio that arachnids can achieve through hydraulics relative to muscle-based actuators is both energy and volumetrically efficient. Until recent advances in nano/microscale 3-D printing with 2PP, the physical realization of synthetic complex ‘soft’ joints would have been impossible to replicate and fill with a hydraulic fluid into a sealed sub-millimeter system. This dissertation demonstrates the smallest scale 3D-printed hydraulic actuator 4.9 × 10^−4 mm^3 by more than an order of magnitude. The use of stiff 2PP polymers with micron-scale dimensions enable compliant membranes similar to exoskeletons seen in nature without the requirement for low-modulus materials. The bio-inspired system is designed to mimic similar hydraulic pressure-activated mechanisms in arachnid joints utilized for large displacement motions relative to body length. Using variations on this actuator design, we demonstrate the ability to transmit forces with relatively large magnitudes (milliNewtons) in 3D space, as well as the ability to direct motion that is useful towards microrobotics and medical applications. Microscale hydraulic actuation provides a promising approach to the transmission of large forces and 3D motions at small scales, previously unattainable in wafer-level 2D microelecromechanical systems (MEMS). The second fabrication process focuses on incorporating functionality through the use of liquid metals in 3D DLW structures. Room temperature eutectic Gallium Indium (eGaIn)- based liquid metal devices with stretchable, conductive, and reconfigurable behavior show great promise across many areas of technology, including robotics, communications, and medicine. Microfluidics provide one means of creating eGaIn devices and circuits, but these devices are typically limited to larger feature sizes. Developments in 3D printing via DLW have enabled sub-100 µm complex microfluidic devices, though interfacing microfluidic devices manufactured with DLW to larger millimeter-scale systems is difficult. The reduced channel diameter creates challenges for removing resist from the channels, filling microchannels with eGaIn, and electrically integrating them to larger channels or other circuitry. These challenges have prevented microscale liquid metal devices from being used more widely. In this dissertation, we demonstrate a facile, low-cost multiscale process for printing DLW microchannels and devices onto centimeter-scale custom fluidic channel substrates fabricated via stereolithography (SLA). This work demonstrates a robust interface between the two independently printed materials and greatly simplifies the filling of eGaIn microfluidic channels down to 50 µm in diameter, with the potential to achieve even smaller feature sizes of liquid metals. This work also demonstrates eGaIn coils with resistance of 43-770 mΩ and inductance of 2-4 nH. As a result, this process empowers us to manufacture interfaces that are not only low-temperature but also conductive and flexible. These interfaces find their application in connecting with sensors, actuators, and integrated circuits, thereby opening new avenues in the field of 3D electronics. Furthermore, our approach extends the lower limits of size-dependent properties for passive electronic components like resistors, capacitors, and inductors crafted from liquid metal, expanding the frontiers of possibilities in miniature electronic design.Item DEVELOPMENT OF VARIABLE TUBE GEOMETRY HEAT EXCHANGERS USING ADJOINT METHOD WITH PERFORMANCE EVALUATION OF AN ADDITIVELY MANUFACTURED PROTOTYPE(2023) Klein, Ellery; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Air-to-refrigerant heat exchangers are a key component for heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems. The performance of these heat exchangers is limited by their air-side thermal resistance. Finless non-round bare tube designs have the potential to improve the air-side thermal-hydraulic performance over their finned counterparts and consequently improve the coefficient of performance (COP) of air-conditioning systems. Previous researchers have used heuristic methods such as multi-objective genetic algorithms (MOGA) with approximation-assisted optimization (AAO) methods utilizing computational fluid dynamics (CFD) based metamodels to shape and topology optimize non-round bare tube heat exchangers. A rather unexplored optimization technique used for heat exchanger optimizations is the gradient based adjoint method. CFD solvers utilizing discrete adjoint methods can be used to shape optimize bare tube heat exchangers and can reveal unintuitive, organic, and potentially superior designs. Additionally, additive manufacturing technology has the capability of building these previously unrealizable heat exchanger designs.The objectives of this dissertation are to experimentally evaluate the performance of shape and topology optimized compact bare tube heat exchangers with non-round bare tubes on a 1) component level, and 2) system level integrated into an air conditioner. Plus, 3) develop new shape optimized variable geometry compact bare tube heat exchangers using discrete adjoint methods for HVAC&R applications. First, a comprehensive experimental investigation of multiple shape and topology optimized compact non-round bare tube heat exchangers was conducted under dry and wet evaporator, condenser, and radiator conditions. For all heat exchangers, air-side pressure drop and heat transfer capacity were predicted within 37% and 15%, respectively. Next, an experimental test facility capable of evaluating the system level performance of a 7.03-8.79 kW commercial packaged air conditioning unit was designed and constructed. The performance of the air conditioning unit was evaluated before and after its conventional tube-fin evaporator was replaced with a shape and topology optimized bare tube evaporator. Results are presented and discussed. Lastly, an ε-constraint and penalty method optimization scheme was implemented which utilizes a commercial CFD software with a built-in discrete adjoint solver to perform multi-objective shape optimizations of 2D bare tube heat exchangers. Critical solver/mesh set-up to best optimize heat exchangers with 0.5-10.0 mm diameter bare tubes were identified and established. The optimized designs can achieve a 40-50% reduction in air-side pressure drop with at least the same heat transfer capacity compared to the initial circular bare tube geometry. An adjoint shape optimized 500 W bare tube radiator was additively manufactured in polymer and experimentally tested. Air-side pressure drop and heat transfer capacity were predicted within 15% and 10%, respectively. The experimental performance confirms the adjoint method shape optimized designs improve the thermal-hydraulic performance over the initial circular bare tube geometry.Item THREE ESSAYS ON OPTIMIZATION, MACHINE LEARNING, AND GAME THEORY IN ENERGY(2023) Chanpiwat, Pattanun; Gabriel, Steven A.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation comprises three main essays that share a common theme: developing methods to promote sustainable and renewable energy from both the supply and demand sides, from an application perspective. The first essay (Chapter 2) addresses demand response (DR) scheduling using dynamic programming (DP) and customer classification. The goal is to analyze and cluster residential households into homogeneous groups based on their electricity load. This allows retail electric providers (REPs) to reduce energy use and financial risks during peak demand periods. Compared to a business-as-usual heuristic, the proposed approach has an average 2.3% improvement in profitability and runs approximately 70 times faster by avoiding the need to run the DR dynamic programming separately for each household. The second essay in Chapter 3 analyzes the integration of renewable energy sources and battery storage in energy systems. It develops a stochastic mixed complementarity problem (MCP) for analyzing oligopolistic generation with battery storage, taking into account both conventional and variable renewable energy supplies. This contribution is novel because it considers multi-stage stochastic MCPs with recourse decisions. The sensitivity analysis shows that increasing battery capacity can reduce price volatility and variance of power generation. However, it has a small impact on carbon emissions reduction. Using a stochastic MCP approach can increase power producers' profits by almost 20 percent, as proposed by the value of stochastic equilibrium solutions. Higher battery storage capacity reduces the uncertainty of the system in all cases related to average delivered prices. Nevertheless, investing in enlarging battery storage has diminishing returns to producers' profits at a certain point restricted by market limitations such as demand and supply or pricing structure. The third essay (Chapter 4) proposes a new practical application of the stochastic dual dynamic programming (SDDP) algorithm that considers uncertainties in the electricity market, such as electricity prices, residential photovoltaic (PV) generation, and loads. The SDDP model optimizes the scheduling of battery storage usage for sequential decision-making over a planning horizon by considering predicted uncertainty scenarios and their associated probabilities. After examining the benefits of shared battery storage in housing companies, the results show that the SDDP model improves the average objective function values (i.e., costs) by approximately 32% compared to a model without it. The results also indicate that the mean objective function values at the end of the first stage of the proposed SDDP model with battery storage and the deterministic LP model equivalent (with perfect foresight) with battery storage differ by less than 30%. The models and insights developed in this dissertation are valuable for facilitating energy policy-making in a rapidly evolving industry. Furthermore, these contributions can advance computational techniques, encourage the use and development of renewable energy sources, and increase public education on energy efficiency and environmental awareness.Item INTERPRETABLE AND SPEED ADAPTIVE CONVOLUTIONAL NEURAL NETWORK FOR PROGNOSTICS AND HEALTH MANAGEMENT OF ROTATING MACHINERY(2023) Lee, Nam Kyoung; Pecht, Michael; Azarian, Michael H; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Faulty rotating machines exhibit vibrational characteristics that can be distinguished from healthy machines using prognostics and health management methods. These characteristics can be extracted using signal processing techniques. However, these techniques require certain inputs, or parameters, before the desired characteristics can be extracted. Setting the parameters requires skill and knowledge, as they should reflect the component geometries and the operational conditions. Using convolutional neural networks for diagnosing faults on a rotating machine eliminates the need for parameter setting by replacing signal processing with mathematical operations in the networks. The parameters that affect the outcomes of the operations are learned from data during the training of the neural networks. The networks can capture characteristics that are related to the health state of a machine, but their operations are not interpretable. Unlike signal processing, the internal operations of the networks have no constraints that guide the networks to transform vibrations into certain information, that is, vibrational characteristics. Without the constraints, there is no basis for understanding the characteristics in terms that can be associated with the physics of failure. The lack of interpretability impedes the physical validation of vibrational characteristics captured by the networks.This dissertation presents a method for changing the internal operations of a convolutional neural network to emulate a specific type of signal processing known as envelope analysis. Envelope analysis demodulates vibrations to extract vibrational signatures associated with mechanical impact on a defective rolling component. An understanding of envelope analysis, along with knowledge of the geometries of machine components and operational speeds, allows for a physical interpretation of the signatures. The dissertation develops speed adaptive convolutional layers and a rotational speed estimation algorithm to identify defect signatures whose frequency components change as the speed changes. The characteristics that are captured by the developed convolutional neural network are verified through a feature selection process that is designed to filter out physically implausible features. Case studies on three different systems demonstrate the feasibility of using the developed convolutional neural network for the diagnosis.