Mechanical Engineering
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Item A CAUSAL INFORMATION FUSION MODEL FOR ASSESSING PIPELINE INTEGRITY IN THE PRESENCE OF GROUND MOVEMENT(2024) Schell, Colin Andrew; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pipelines are the primary transportation method for natural gas and oil in the United States making them critical infrastructure to maintain. However, ground movement hazards, such as landslides and ground subsidence, can deform pipelines and potentially lead to the release of hazardous materials. According to the Pipeline and Hazardous Materials Safety Administration (PHMSA), from 2004 to 2023, ground movement related pipeline failures resulted in $413M USD in damages. The dynamic nature of ground movement makes it necessary to collect pipeline and ground monitoring data and to actively model and predict pipeline integrity. Conventional stress-based methods struggle to predict pipeline failure in the presence of large longitudinal strains that result from ground movement. This has prompted many industry analysts to use strain-based design and assessment (SBDA) methods to manage pipeline integrity in the presence of ground movement. However, due to the complexity of ground movement hazards and their variable effects on pipeline deformation, current strain-based pipeline integrity models are only applicable in specific ground movement scenarios and cannot synthesize complementary data sources. This makes it costly and time-consuming for pipeline companies to protect their pipeline network from ground movement hazards. To close these gaps, this research made significant steps towards the development of a causal information fusion model for assessing pipeline integrity in a variety of ground movement scenarios that result in permanent ground deformation. We developed a causal framework that categorizes and describes how different risk-influencing factors (RIFs) affect pipeline reliability using academic literature, joint industry projects, PHMSA projects, pipeline data, and input from engineering experts. This framework was the foundation of the information fusion model which leverages SBDA methods, Bayesian network (BN) models, pipeline monitoring data, and ground monitoring data to calculate the probability of failure and the additional longitudinal strain needed to fail the pipeline. The information fusion model was then applied to several case studies with different contexts and data to compare model-based recommendations to the actions taken by decision makers. In these case studies, the proposed model leveraged the full extent of data available at each site and produced similar conclusions to those made by decision makers. These results demonstrate that the model could be used in a variety of ground movement scenarios that result in permanent ground deformation and exemplified the comprehensive insights that come from using an information fusion approach for assessing pipeline integrity. The proposed model lays the foundation for the development of advanced decision making tools that can enable operators to identify at-risk pipeline segments that require site specific integrity assessments and efficiently manage the reliability of their pipelines in the presence of ground movement.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 ENERGY ANALYSIS OF A METRO TRANSIT SYSTEM FOR SUSTAINABILITY AND EFFICIENCY IMPROVEMENT(2023) Higgins, Jordan Andrew; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The industrial sector in the US accounted for 33% of the overall energy consumption and 23% of total GHG Emissions in 2022, necessitating the need for energy efficiency and decarbonization of this sector. This study identifies common opportunities and challenges while performing energy audits for the State of Maryland public transportation maintenance complex and proposes site-specific energy efficiency measures. Utilizing performance indices such as Energy Use Intensity (EUI) and load factor from end-use energy data, as well as walkthrough observations from energy audits, energy efficiency measures specific to each facility were formulated to augment the overall energy performance. Additionally, energy modeling helped pinpoint the additional scope of energy efficiency improvements that could have potential significant energy performance improvements and reduce on-site GHG emissions. Among the energy conservation measures considered, the re-sizing and decarbonization of HVAC equipment has the greatest contribution to energy and GHG savings, with a 100% decrease in natural gas, a 37% decrease in electricity use annually, and net decrease of 272 Mton CO2. This study aims to highlight the similarities and differences in existing transportation and maintenance facilities and the applicable technology(ies) that could streamline and serve as a guide for energy audits for transportation maintenance facilities by demonstrating the most common energy efficiency measures and subsequent achievable savings for these facilities.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 Modeling of HVAC Configurations for De-Carbonization in a Mid-Size Hospital(2022) Grant, Zachary; Hwang, Yunho; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As the threat of climate change becomes more imminent, there has been increasing emphasis on technologies that reduce carbon emissions in the HVAC sector. The clear path forward given existing technologies is electrification since electricity production has future potential to become cleaner. In terms of building type, high ventilation requirements and near continuous occupancy make healthcare facilities some of the highest energy users. HVAC equipment runs all day and night in these facilities with little change. Conventional HVAC equipment such as a boiler is proven to consume more energy than heat pump systems. More specifically, the Variable Refrigerant Flow (VRF) heat pump and the Ground Source Heat Pump (GSHP) are areas of ongoing research. This analysis included creating whole-building energy models using EnergyPlus and OpenStudio to compare the energy consumption for these heat pump configurations and some cheaper electrification alternatives. The results suggested that the GSHP system possessed the greatest potential for energy savings and thus decarbonization given its higher efficiency during times of extreme ambient temperatures compared to other options.Item OPTIMUM DESIGN AND OPERATION OF COMBINED COOLING HEATING AND POWER SYSTEM WITH UNCERTAINTY(2022) Gao, Lei; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems. As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%. Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system's configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art. Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 $/m2, and a potential annual cost is about 22.33 $/m2. This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system.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 NUMERICAL MODELING AND EXPERIMENTAL STUDY OF A NOVEL METAL-POLYMER COMPOSITE HEAT EXCHANGER FOR SENSIBLE AND LATENT THERMAL ENERGY STORAGE APPLICATIONS(2022) KAILKHURA, GARGI; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Compact, lightweight, and low-cost heat exchangers (HXs) have the potential to improve efficiencies and save power and carbon foot print in a wide array of applications. The present study investigates an entirely additively-manufactured novel metal-polymer composite heat exchanger, enabled by an innovative and patented cross-media thermal exchange approach, which yields an effective thermal conductivity of 130 W/m-K for the heat exchanger. This record-high thermal conductivity is more than an order of magnitude higher that the previously reported thermal conductivity for polymer and polymer composite HXs. Drawing on the concept of external flow over the tube banks, the proposed HX features a staggered arrangement of fins. This class of HXs are often used for gas-to-liquid sensible cooling applications. However, they can also be designed for latent thermal energy storage applications by employing low-cost and high energy-storage-density phase change materials (PCMs) such as salt-hydrates and alike in either the hot or cold side of the HX, depending on the application. An extensive literature survey on tube banks shows that, though numerous correlations exist in the literature for flow over tube banks, these correlations usually fall outside the range for the current HX design for low-Reynolds number applications (Re<100). Furthermore, the PCM models present in the literature are either very challenging to solve analytically or are computationally expensive. Thus, the dissertation emphasizes developing computationally-efficient and robust numerical models for sensible and latent cooling applications.The numerical models compute the overall thermal and pressure-drop performance metrics based on segment-level modeling, and they integrate the performance parameters such as Euler, Nusselt numbers, or latent thermal energy with the entire HX analytically, thus significantly reducing the computational cost. For steady-state sensible thermal energy storage applications, a realistic 3D CFD-based modeling approach is used, based on the actual dimensions of the printed HXs rather than a traditional 2D CFD-based model. It also resolves the issues due to the 3D velocity field which aren’t captured in the 2D CFD models, and are particularly important for HXs utilizing narrow/micro channels. This modeling approach is used to obtain optimized HXs for case examples of 5-40 kW air-conditioning applications and 250-W electronic cooling applications for nominal operating and flow conditions. The 250-W unit is further validated experimentally and is observed to be within 17% for waterside pressure drop, 11% for airside pressure drop, and within 8% for thermal resistance when compared against experimental measurements. For transient latent thermal storage applications, an analytical-based 1D reduced order model (ROM) for segment-level modeling is developed based on 1D radial conduction inside the PCM. It is numerically validated with commercial CFD tools to within 10% except for cases where axial conduction in PCM is possible due to the high resistance of wire embedded in the PCM. The 1D ROM is used in optimizing a 1.44-MJ TES unit for peak-load building cooling applications and a 19.2-kJ HX for pulsed-power cooling applications. The 1.44-MJ unit is experimentally tested and observed to be within 17% for the melting time of complete PCM and about 8% for the freezing time of the complete PCM. Lastly, another novel and hybrid thermal energy storage design is formulated, which utilizes two different PCMs: shape memory alloys (SMAs) instead of metal wires and salt-hydrates contained inside polymer channels similar to the reference designs. Besides the thermal energy storage design, a novel methodology on Wilson plot for finned surfaces on both fluid-sides is introduced, which is first of its kind in the literature. Ongoing and future work in both these areas is also recommended in the final chapter of the thesis.Item DEVELOPMENT OF A RELIABILITY DATA COLLECTION FRAMEWORK FOR HYDROGEN FUELING STATION QRA(2021) West, Madison; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The wider adoption of hydrogen in multiple sectors of the economy requires that safety and risk issues be rigorously investigated. Quantitative Risk Assessment (QRA) is an important tool for enabling safe deployment of hydrogen fueling stations and is increasingly embedded in the permitting process. However, QRA needs reliability data, and currently the available hydrogen safety databases are not in a format conducive for use in QRA. A review of the International Journal of Hydrogen Energy articles on hydrogen fueling station QRA found that lack of hydrogen reliability data is the most common knowledge gap in this field. This study explores what QRA and reliability data currently look like in the context of hydrogen systems. It then presents a new reliability data collection framework for hydrogen systems that overcomes gaps in existing hydrogen safety databases. Current hydrogen safety data collection tools, H2Tools, HIAD, NREL CDPs, and CHS are analyzed and compared for applicability to QRA. Lessons learned from these data collection tools are extracted and combined with best practices from reliability engineering to create an improved database framework for hydrogen reliability data. This framework aims to standardize the hydrogen fueling stations component hierarchy, failure mode taxonomy, and outline high level elements necessary for adequate reliability data collection suitable for use in QRA. This research establishes the groundwork for a collaborative hydrogen reliability database and the future development of data driven hydrogen safety tools.