Mechanical Engineering
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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 ENERGY CONSUMPTION REDUCTION OF COMMERCIAL BUILDINGS THROUGH THE IMPLEMENTATION OF VIRTUAL AND EXPERIMENTAL ENERGY AUDIT ANALYSIS(2022) Bae, Ji Han; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)According to the U.S. Energy Information Administration (EIA), about 38 quads of the total U.S. energy consumption was consumed by residential and commercial buildings in 2017, which is about 39% of the total 2017 annual U.S. energy consumption (EIA, 2018). Additionally, the building sector is responsible for about 75% of the total U.S. electricity consumption as well as for about 70% of the projected growth in the U.S. electricity demand through 2040. It is clear that the potential for energy savings and greenhouse gas emissions reduction in existing buildings today remain largely untapped and that there is still much left to explore in respect to determining the best protocols for reducing building energy consumption on a national and even a global scale. The present work investigates the effectiveness of coupling an initial virtual energy audit screening with the conventional, hands-on, energy audit processes to more quickly and less costly obtain the potential energy savings for high energy consumption buildings. The virtual screening tool takes advantage of a customized cloud-based energy efficiency management software and the readily available building energy consumption data to identify the buildings that have the highest energy savings potential and should be given priority for performing onsite walkthroughs, detailed energy audits, and the subsequent implementation of the identified energy conservation measures (ECMs). By applying the proposed procedure to a group of buildings, the results of this study demonstrated that a combination of the software-based screening tools and a detailed experimental/onsite energy audit as necessary can effectively take advantage of the potential energy consumption and carbon footprint reduction in existing buildings today and that the low-cost/no-cost energy conservation measures alone can oftentimes result in significant savings as documented in this thesis. However, selection of the appropriate software was deemed critically important, as certain software limitations were observed to hinder the obtainment of some energy savings opportunities.Item Trajectory Optimization of a Tethered Underwater Kite(2021) Alvarez Tiburcio, Miguel; Fathy, Hosam; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the challenge of optimizing the motion trajectory of a tethered marine hydrokinetic energy harvesting kite in order to maximize its average electric power output. The dissertation focuses specifically on the “pumping” kite configuration, where the kite is periodically reeled out from a floating base station at high tension, then reeled in at low tension. This work is motivated by the significant potential for sustainable electricity generation from marine currents such as the Gulf Stream. Tethered systems can increase their energy harvesting potential significantly through cross-current motion. Such motion increases apparent flow speed, which is valuable because the instantaneous maximum power that can be harvested is proportional to the cube of this apparent speed. This makes it possible for tethered systems to achieve potentially very attractive power densities and levelized costs of electricity compared to stationary turbines. However, this also necessitates the use of trajectory optimization and active control in order to eke out the maximum energy harvesting capabilities of these systems. The problem of optimizing the trajectories of these kites is highly non-linear and thus challenging to solve. In this dissertation we make key simplifications to both the modeling and the structure of the optimal solution which allows us to learn valuable insights in the nature of the power maximizing trajectory. We first do this analysis to maximize the average mechanical power of the kite, then we expand it to take into account system losses. Finally, we design and fabricate an experimental setup to both parametrize our model and validate our trajectories. In summary, the goal of this research is to furnish model-based algorithms for the online optimal flight control of a tethered marine hydrokinetic system. The intellectual merit of this work stems from the degree to which it will tackle the difficulty of solving this co-optimization problem taking into account overall system efficiency and the full range of possible system motion trajectories. From a broader societal perspective, this work represents a step towards experimentally validating the potential of pumped underwater kite systems to serve as renewable energy harvesters in promising environments such as the Gulf Stream.Item Modeling Syndromic Surveillance and Outbreaks in Subpopulations(2020) Pettie, Christa; Herrmann, Jeffrey; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research is motivated by the need to assist resource limited communities by enhancing the use of syndromic surveillance (SyS) systems and data. Public health agencies and academic researchers have developed and implemented SyS systems as a pattern recognition tool to detect a potential disease outbreak using pre-diagnostic data. SyS systems collect data from multiple types of sources: absenteeism records, over the counter medicine sales, chief complaints, web queries, and more. It could be expensive, however, to gather data from every available source; subsequently, gathering information about only some subpopulations may be a desirable option. This raises questions about the differences between subpopulation behavior and which subpopulations’ data would give the earliest, most accurate warning of a disease outbreak. To investigate the feasibility of using subpopulation data, this research will gather and organize SyS data by subpopulation (separated by population characteristics such as age or location) and identify how well the SyS data correlates to the real world disease progression. This research will study SyS how reports of Influenza-like-illness (ILI) in subpopulations represent the disease behavior. The first step of the research process is to understand how SyS is used in environments with varying levels of resources and what gaps are present in SyS modeling techniques. Various modeling techniques and applications are assessed, specifically the Susceptible Infected Recovered “SIR” model and associated modifications of that model. Through data analysis, well correlated subpopulations will be identified and compared to actual disease behavior and SyS data sets. A model referred to as ModSySIR will be presented that uses real world community data ideal for ease of use and implementation in a resource limited community. The highest level research objective is to provide a potential data analysis method and modeling approach to inform decision making for health departments using SyS systems that rely on fewer resources.Item A Control-Theoretic Model of Hemodynamic Responses to Blood Volume Perturbation(2018) Lo, Alex Kai-Yuan; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents a mathematical model to reproduce hemodynamic responses of different endpoints to the blood volume perturbation in circulation system. The proposed model includes three sub-models, which are a control-theoretic model relating blood volume response to blood volume perturbation, a physiologic-based model relating cardiac output response to blood volume perturbation, and a control-theoretic model relating mean arterial pressure response to cardiac output perturbation. Two unique characteristics of this hemodynamic model are that the model can reproduce responses accurately even with its simplicity, and can be easily understood by control engineers because of its physiological transparency. With these two advantages, closed-loop resuscitation controller evaluation can be performed in model-based approach instead of evaluating results from animal studies, which are relatively costly and time-consuming. In this thesis, the hemodynamic model was examined and evaluated by using experimental dataset collected from 11 animals. The results of system identification analysis, in-silico evaluation and parametric sensitivity analysis showed that the hemodynamic model may faithfully serve as a evaluation basis for the closed-loop resuscitation controllers.Item Simulation and Analysis of Energy Consumption for an Energy-Intensive Academic Research Building(2014) Levy, Jared Michael; Ohadi, Michael M.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The University of Maryland's Jeong H. Kim Engineering Building is a state-of-the-art academic research facility. This thesis describes an energy analysis and simulation study that serves to identify energy saving opportunities and optimum operation of the building to achieve its goals of high energy efficiency and substantial CO2 emission reduction. A utility analysis, including a benchmarking study, was completed to gauge the performance of the facility and a detailed energy model was developed using EnergyPlus to mimic current operation. The baseline energy model was then used to simulate eight energy efficiency measures for a combined energy savings of 16,760 MMBtu, reducing annual energy use by 25.3%. The simple payback period for the proposed measures as a single project is estimated to be less than one year. Due to the high-tech and unique usage of the Kim Engineering Building, including cleanrooms and research labs, this thesis also contributes to the development of energy consumption benchmarking data available for such facilities.Item PERFORMANCE ASSESSMENT OF MEMS GYROSCOPE AND SHOCK DURABILITY EVALUATION OF SAC305-X SOLDERS FOR HIGH TEMPERATURE APPLICATIONS(2014) Patel, Chandradip Pravinbhai; McCluskey, F.Patrick; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advances in MEMS technology have resulted in relatively low cost MEMS gyroscopes. Their unique features compared to macro-scale devices, such as lighter weight, smaller size, and less power consumption, have made them popular in many applications with environmental conditions ranging from mild to harsh. This dissertation aims to address a gap in the literature on MEMS gyroscopes by investigating the effects of elevated temperatures on the performance of MEMS gyroscopes. MEMS gyroscopes are characterized at room and elevated temperatures for both stationary and rotary conditions. During the test, MEMS gyroscopes are subjected to five thermal cycles at each of four temperature ranges (viz. 25degC to 85degC, 25degC to 125degC, 25degC to 150degC and 25degC to 175degC). A simulation model is developed in MATLAB Simulink to simulate the temperature effect on the MEMS gyroscope. Simulation results show good agreement with experimental results and confirm that Young's modulus and damping coefficient are the dominant factors responsible for temperature-dependent bias at elevated temperatures. Solder interconnects are one of the weakest elements in MEMS devices. Thus, the reliability of solder interconnects is separately studied in this dissertation. Though, SAC305 (96.5%Sn3.0%Ag0.5%Cu) is the industry preferred solder in combined thermal cycling and shock/drop environments, it exhibits better thermal cycling reliability than drop/shock reliability. One of the ways to improve drop/shock reliability of SnAgCu solder is by microalloy addition of various dopants such as Mn, Ce, Ti, Y, Ge, Bi, Zn, In, Ni, Co etc. Thus, the second part of this dissertation aims to evaluate the shock durability of SAC305 and SAC305-X (where X refers to two different concentrations of Mn and Ce dopants). High temperature isothermal aging tests are conducted on selected solders using QFN44, QFN32 and R2512 package types at 185degC and 200degC up to 1000 hours. Isothermal aging test results showed that interfacial IMC growth reduction can be achieved by microalloy addition of selected dopants in SAC305 on both copper and nickel leaded package types. Shock durability of selected solders is examined on as-reflowed and thermally aged test boards. Mechanical shock is performed using a custom shock machine that utilizes a shock pulse of 500G with a 1.3 millisecond duration. The shock test results showed that the mechanical shock reliability of SAC305 was significantly improved on both as-reflowed and thermally aged test boards by microalloy addition of one of the selected dopant in SAC305.Item Application of Stochastic Reliability Modeling to Waterfall and Feature Driven Development Software Development Lifecycles(2011) Johnson, David Michael; Modarres, Mohammed; Smidts, Carol S; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are many techniques for performing software reliability modeling. In the environment of software development some models use the stochastic nature of fault introduction and fault removal to predict reliability. This thesis research analyzes a stochastic approach to software reliability modeling and its performance on two distinct software development lifecycles. The derivation of the model is applied to each lifecycle. Contrasts between the lifecycles are shown. Actual data collected from industry projects illustrate the performance of the model to the lifecycle. Actual software development fault data is used in select phases of each lifecycle for comparisons with the model predicted fault data. Various enhancements to the model are presented and evaluated, including optimization of the parameters based on partial observations.Item MODELING THE PHYSICS OF FAILURE FOR ELECTRONIC PACKAGING COMPONENTS SUBJECTED TO THERMAL AND MECHANICAL LOADING(2011) Sharon, Gilad; Barker, Donald D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation presents three separate studies that examined electronic components using numerical modeling approaches. The use of modeling techniques provided a deeper understanding of the physical phenomena that contribute to the formation of cracks inside ceramic capacitors, damage inside plated through holes, and to dynamic fracture of MEMS structures. The modeling yielded numerical substantiations for previously proposed theoretical explanations. Multi-Layer Ceramic Capacitors (MLCCs) mounted with stiffer lead-free solder have shown greater tolerance than tin-lead solder for single cycle board bending loads with low strain rates. In contrast, flexible terminations have greater tolerance than stiffer standard terminations under the same conditions. It has been proposed that residual stresses in the capacitor account for this disparity. These stresses have been attributed to the higher solidification temperature of lead free solders coupled with the CTE mismatch between the board and the capacitor ceramic. This research indicated that the higher solidification temperatures affected the residual stresses. Inaccuracies in predicting barrel failures of plated through holes are suspected to arise from neglecting the effects of the reflow process on the copper material. This research used thermo mechanical analysis (TMA) results to model the damage in the copper above the glass transition temperature (Tg) during reflow. Damage estimates from the hysteresis plots were used to improve failure predictions. Modeling was performed to examine the theory that brittle fracture in MEMS structures is not affected by strain rates. Numerical modeling was conducted to predict the probability of dynamic failure caused by shock loads. The models used a quasi-static global gravitational load to predict the probability of brittle fracture. The research presented in this dissertation explored drivers for failure mechanisms in flex cracking of capacitors, barrel failures in plated through holes, and dynamic fracture of MEMS. The studies used numerical modeling to provide new insights into underlying physical phenomena. In each case, theoretical explanations were examined where difficult geometries and complex material properties made it difficult or impossible to obtain direct measurements.Item STRUCTURING A PROBABILISTIC MODEL FOR RELIABILITY EVALUATION OF PIPING SUBJECT TO CORROSION-FATIGUE DEGRADATION(2009) Alseyabi, Mohamed Chookah; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pipelines are susceptible to degradation over the span of their service life. Corrosion is one of the most common degradation mechanisms, but other critical mechanisms such as fatigue and creep should not be overlooked. The rate of degradation is influenced by many factors, such as material, process conditions, geometry, and location. Based on these factors, a best estimate for the pipeline service life (reliability) can be calculated. This estimate serves as a guide for maintenance and replacement practices. After a long period of service, however, this estimate requires reevaluation due to the new evidence gathered from monitoring the conditions of the pipeline. Several deterministic models have been proposed to estimate the reliability of pipelines. Among these models is the ASME B31G code, which is the most widely accepted method for the assessment of corroded pipelines. However, these models are highly conservative and lack the ability to estimate the true life and health of the pipeline. In addition to the limitations embedded in these deterministic models are the problems with inspection techniques and tools that may be inadequate and susceptible to error and imprecision. What is needed, therefore, is a best-estimate assessment model that estimates the true life of these pipelines and integrates the uncertainties surrounding the estimate. Hence, this dissertation proposes a probabilistic model that is capable of addressing the limitations of these models (by accounting for model uncertainties), the inspection data (by characterizing limited and uncertain evidence), and subjective proactive maintenance (by involving the decision-making process under uncertainty). The objective of this research is to propose and validate a probabilistic model based on the underlying degradation phenomena and whose parameters are estimated from the observed field data and experimental investigations. Uncertainties about the structure of the model itself and the parameters of the model will also be characterized. The proposed model should be able to capture wider ranges of pipelines rather than only the network ones, so that the proposed model will better represent the reality and can account for material and size variability. The existing probabilistic models sufficiently address the corrosion and fatigue mechanisms individually but are inadequate to capture mechanisms that synergistically interact. Given that capturing all degradation mechanisms would be a challenging task, the new model will address two of the most important mechanisms: pitting corrosion and fatigue-crack growth. The field data is very limited, and the experiments required an extensive and expensive set-up before they could produce suitable results. Hence, relying primarily in the initial stage on the generic data available from the literature facilitated the construction of the empirical degradation model and provided an order-of-magnitude estimate of the parameters of the degradation model. The proposed model in it is simplest form has the capability to estimate the degradation outputs with the least parameter inputs available. The Bayesian approach was implemented to incorporate the experimental data to further improve the proposed model and estimate the two constants' values. The proposed empirical model can be used to estimate the aging life expended, which will enable inspection and replacement strategies to be developed.