Mechanical Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2795
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Item Simulation and Analysis of Energy Consumption for Two Complex and Energy intensive Buildings on UMD Campus(2019) Kelly, Jason; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Physical Sciences Complex and Eppley Recreational Center are multi-purpose buildings which are complex in functionality and are among the highest consumers of energy on the UMD campus. Building energy analyses used to identify energy efficiency measures to optimize energy efficiency in the buildings. Detailed building energy models were developed in EnergyPlus and OpenStudio that sought to mimic current operations of the buildings. PSC model results deviated respectively -1.05%, 1.19%, and 5.27% for electricity, steam, and chilled water. ERC model results deviated respectively 0.47%, 5.3%, and 2.2% from annual electricity, hot water, and gas. Four energy efficiency measures for the Physcial Sciences Complex provided energy model predicted energy savings of 3,757 MMBtu or 7.5% of the building’s energy consumption. Four efficiency measures were identified for the Eppley Recreation Center with energy model predicted energy savings of 3,390 MMBtu or 8.4% of the building’s energy consumption.Item Moisture Transport through Housing Materials Enclosing Critical Automotive Electronics(2019) Roman, Artur; Han, Bongtae; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In automotive electronics, humidity-sensitive electronics are encapsulated by protective housings that are attached to the car body. Typical housing materials are comprised of polymer composites, through which moisture transport occurs. The objective of this paper is to provide a predictive capability for moisture transport through automotive housings enclosing a cavity with electronic modules. The temperature-dependent moisture properties including moisture diffusivity, solubility, and saturated concentration of three housing material candidates are characterized first. Then, the analogy between heat transfer and the mass transfer is implemented to model the moisture transport into the cavity enclosed by the housing materials. To cope with the transient boundary condition at the housing material and the cavity interface, the effective volume scheme is used, treating the cavity as an imaginary polymer with an extremely large diffusivity and “equivalent solubility.” The prediction is subsequently validated through an experimental setup designed to monitor the in-situ humidity condition inside the cavity sealed by the housing materials. The prediction and experimental results agree well with each other, which corroborates the validity of the FEA modeling and the measured moisture properties.Item ADVANCED MODELING AND REFRIGERANT FLOW PATH OPTIMIZATION FOR AIR-TO-REFRIGERANT HEAT EXCHANGERS WITH GENERALIZED GEOMETRIES(2019) Li, Zhenning; Radermacher, Reinhard K; Aute, Vikrant C; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Air-to-refrigerant heat exchangers are key components of the heating, ventilation, air-conditioning and refrigeration systems. The evolving simulation and manufacturing capabilities have given engineers new opportunities in pursuing complex and cost-efficient heat exchanger designs. Advanced heat exchanger modeling tools are desired to adapt to the industrial transition from conventional refrigerants to low Global Warming Potential (low-GWP) refrigerants. This research presents an advanced heat exchanger performance prediction model which distinguishes itself as a cutting-edge simulation tool in the literature to have capabilities, such as to (i) model heat exchangers with variable tube shape and topology, (ii) improved numerical stability, (iv) multiple dehumidification models to improve evaporator prediction, and (v) CFD-based predictions for airflow maldistribution. Meanwhile, HX performance is significantly influenced by the refrigerant flow path arrangements. The refrigerant flow path is optimized for various reasons such as to (i) mitigate the impact of airflow maldistribution, (ii) reduce material/cost, (iii) balance refrigerant state at the outlet of each circuit, and (iv) ensure overall stable performance under a variety of operating conditions. This problem is particularly challenging due to the large design space which increases faster than n factorial with the increase in the number of tubes. This research presents an integer permutation based Genetic Algorithm (GA) to optimize the refrigerant flow path of air-to-refrigerant heat exchangers. The algorithm has novel features such as to (i) integrate with hybrid initialization approaches to maintain the diversity and feasibility of initial individuals, (ii) use effective chromosome representations and GA operators to guarantee the chromosome (genotype) can be mapped to valid heat exchanger designs (phenotype), and (iii) incorporate real-world manufacturability constraints to ensure the optimal designs are manufacturable with the available tooling. Case studies have demonstrated that the optimal designs obtained from this algorithm can improve performance of heat exchangers under airflow maldistribution, reduce defrost energy and assure stable heat exchanger performance under cooling and heating modes in reversible heat pump applications. Comparison with other algorithms in literature shows that the proposed algorithm exhibits higher quality optimal solutions than other algorithms.Item MULTI-VEHICLE ROUTE PLANNING FOR CENTRALIZED AND DECENTRALIZED SYSTEMS(2019) Patel, Ruchir; Herrmann, Jeffrey W; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Multi-vehicle route planning is the problem of determining routes for a set of vehicles to visit a set of locations of interest. In this thesis, we describe a study of a classical multi-vehicle route planning problem which compared existing solutions methods on min-sum (minimizing total distance traveled) and min-max (minimizing maximum distance traveled) cost objectives. We then extended the work in this study by adapting approaches tested to generate robust solutions to a failure-robust multi vehicle route planning problem in which a potential vehicle failure may require modifying the solution, which could increase costs. Additionally, we considered a decentralized extension to the multi-vehicle route planning problem, also known as the decentralized task allocation problem. The results of a computational study show that our novel genetic algorithm generated better solutions than existing approaches on larger instances with high communication quality.Item Data-Driven Geometric Design Space Exploration and Design Synthesis(2019) Chen, Wei; Fuge, Mark D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A design space is the space of all potential design candidates. While the design space can be of any kind, this work focuses on exploring geometric design spaces, where geometric parameters are used to represent designs and will largely affect a given design's functionality or performance (e.g., airfoil, hull, and car body designs). By exploring the design space, we evaluate different design choices and look for desired solutions. However, a design space may have unnecessarily high dimensionality and implicit boundaries, which makes it difficult to explore. Also, if we synthesize new designs by randomly sampling design variables in the high-dimensional design space, there is high chance that the designs are not feasible, as there is correlation between feasible design variables. This dissertation introduces ways of capturing a compact representation (which we call a latent space) that describes the variability of designs, so that we can synthesize designs and explore design options using this compact representation instead of the original high-dimensional design variables. The main research question answered by this dissertation is: how does one effectively learn this compact representation from data and efficiently explore this latent space so that we can quickly find desired design solutions? The word "quickly" here means to eliminate or reduce the iterative ideation, prototyping, and evaluation steps in a conventional design process. This also reduces human intervention, and hence facilitates design automation. This work bridges the gap between machine learning and geometric design in engineering. It contributes new pieces of knowledge within two topics: design space exploration and design synthesis. Specifically, the main contributions are: 1. A method for measuring the intrinsic complexity of a design space based on design data manifolds. 2. Machine learning models that incorporate prior knowledge from the domain of design to improve latent space exploration and design synthesis quality. 3. New design space exploration tools that expand the design space and search for desired designs in an unbounded space. 4. Geometrical design space benchmarks with controllable complexity for testing data-driven design space exploration and design synthesis.Item ERROR ESTIMATION, GRID SELECTION AND CONVERGENCE VERIFICATION IN LARGE EDDY SIMULATION(2019) Toosi, Siavash; Larsson, Johan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Large eddy simulation (LES) is a modeling approach to simulation of turbulence, in which the large and energy containing eddies are directly resolved, while the smaller scales are modeled. The ``coarse-graining'' length scale (the length scale below which the turbulent eddies are modeled) is an important modeling parameter that is directly tied to the computational grid. As a result, the LES grid controls both the numerical and modeling errors and in most cases (given that the LES model is consistent) becomes the most important factor in determining the accuracy of the solution. The main goal of this dissertation is to enable a systematic approach to grid selection and convergence-verification in LES. Systematic grid selection consists of five essential ingredients: (i) an ``error-indicator'' that identifies the regions of error generation, (ii) some knowledge of the directional structure of error generation (i.e., an anisotropic measure of error generation at each location), (iii) a model that describes the connection between the error generation and the filter/grid resolution (i.e., how it changes with a change in the resolution), (iv) criteria that describe the most ``optimal'' distribution of the error-indicator in space and in direction, and (v) a robust method for convergence-verification. Items (i), (ii), (iv) and (v) are all addressed in this dissertation, while item (iii) has not been a subject of extensive research here (because of its somewhat lower importance compared to the other four). Three error-indicators are introduced that are different in terms of their underlying assumptions, complexity, potential accuracy, and extensibility to more complex flows and more sophisticated formulations of the problem of ``optimal'' grid selection. Two of these error-indicators are inherently anisotropic, while the third one is only a scalar but can be combined with either of the other two to enable anisotropic error-estimation. The ``optimal'' distributions of these error-indicators are discussed in detail, that, combined with a model to connect the error-indicator and the grid/filter resolution, describe our ``optimal'' grid selection criteria. Additionally, a more robust approach for convergence-verification in LES is proposed, and is combined with error-estimation and ``optimal'' grid selection/adaptation to form a systematic algorithm for large eddy simulation. The proposed error-estimation, grid selection, and convergence-verification methods are tested on the turbulent channel flow and the flow over a backward-facing step, with good results in all cases, and grids that are quite close to what is know as ``best practice'' for LES of these flows.Item Turbulent Transport and Mixing of Unconfined and Sloped Fire-Induced Flows Using a Laser-Assisted Saltwater Modeling Technique(2019) Maisto, Pietro; Gollner, Michael J.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The present work investigates turbulent, buoyant fire-induced flows using an experimental scaling technique known as saltwater modeling — a methodology enabling quantitative analysis of fire plumes built upon the analogy with saltwater (plume) flowing into the ambient water (air). The investigation, conducted by means of velocimetry (PIV) and concentration (PLIF) laser-based techniques, concerns unconfined plume mixing and transport, characterization of ceiling jet flows under sloped ceilings and activation of suppression devices in these sloped configurations. Flow imaging provides detailed measurements of velocity and saltwater concentration within the entire spatial and temporal domain of a planar section of the plume. In analogy with low-pass filtering in large eddy simulation (LES), a virtual, pixel-binning grid of varying size is overlaid on images to compute statistical moments representative of the larger and smaller scales. By leveraging actual measurements, converged statistics (first, second, higher-order) enables selection of cutoff resolutions, useful for validation and development of computational fluid dynamics (CFD) simulations. The saltwater plume's subsequent impingement onto a sloped plate generates a ceiling jet flowing both streamwise (up- and downslope) and spanwise with respect to the impingement point. Such flow is investigated to first build correlations predicting velocity and temperature along a sloped ceiling and second to analyze slope-related suppression device (sprinkler) activation. For the first task, single-planar, streamwise measurements are employed; for the second, multiple orthogonal laser sheets crossing the plate are used to generate a virtual grid of measured points. Transport characteristics are implemented into an activation model, modified to predict a dimensionless response time spatial distribution. At increasing slopes, the delay in the activation between upslope (faster) and downslope (delayed) devices progressively increases at increasing ceiling angles. This also occurs between sprinklers symmetrically located upslope and spanwise. From the response spatial distribution, the streamwise-to-spanwise correlation for the delay time (thermal responsiveness) is determined using the saltwater front arrival times. The analysis for the lag time reveals that the delay in thermal responsiveness between two sprinklers with the same activation time located up- and downslope, respectively, increases exponentially compared to that found for sprinklers located spanwise, at a quadratic rate with increasing angles.Item META-STRUCTURE ENHANCEMENT OF RESONANT ACOUSTIC MIXING VIA EMBEDDED ADDITIVE MANUFACTURING(2019) Reach, William Alexander; Sochol, Ryan D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The performance of energetic materials is founded on a wide range of material and mixing parameters. Resonant acoustic mixing (RAM) is advantageous as a scalable, contactless energetics mixing method; however, challenges remain in connecting process parameters to post-mix performance. In this thesis, we analyzed the influence of the structural arrangement of pre-mixture ingredients (i.e., the pre-mix “meta-structure”) on post-mix properties. We utilized an embedded additive manufacturing strategy for ingredient loading to realize two distinct pre-mix meta-structures: (i) a consolidated (control) configuration, and (ii) a novel distributed arrangement. Following identical RAM processing, post-mix products were sectioned and optically characterized using scanning electron microscopy and electron-dispersive electron dispersive spectroscopy, revealing significant reductions in void content corresponding to the distributed meta-structure designs. Mechanical testing of post-mix products revealed distributed meta-structure specimens elongated up to 147% more than consolidated specimens prior to fracture, suggesting a critical role for pre-mix ingredient architecture in post-RAM performance.Item PRIVACY IN DISTRIBUTED MULTI-AGENT COLLABORATION: CONSENSUS AND OPTIMIZATION(2018) Gupta, Nirupam; Chopra, Nikhil; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Distributed multi-agent collaboration is an interactive algorithm that enables agents in a multi-agent system (MAS) to achieve pre-defined collaboration objective in a distributed manner, such as agreeing upon a common value (commonly referred as distributed consensus) or optimizing the aggregate cost of the MAS (commonly referred as distributed optimization). Agents participating in a typical distributed multi-agent collaboration algorithm can lose privacy of their inputs (containing private information) to a passive adversary in two ways. The adversary can learn about agents' inputs either by corrupting some of the agents that are participating in the collaboration algorithm or by eavesdropping the communication links between the agents during an execution of the collaboration algorithm. Privacy of the agents' inputs in the former case is referred as internal privacy, and privacy of the agents' inputs in the latter case is referred as external privacy. This dissertation proposes a protocol for preserving internal privacy in two particular distributed collaborations: distributed average consensus and distributed optimization. It is shown that the proposed protocol can preserve internal privacy of sufficiently well connected honest agents (agents that are not corrupted by the adversary) against adversarial agents (agents that are corrupted by the adversary), without affecting the collaboration objective. This dissertation also investigates a model-based scheme, as an alternative to cryptographic encryptions, for external privacy in distributed collaboration algorithms that can be modeled as linear time-invariant networked control systems. It is demonstrated that the model-based scheme preserves external privacy, without affecting the collaboration objective, if the system parameters of the networked control system, that equivalently models the distributed collaboration algorithm, satisfy certain conditions. Unlike cryptographic encryptions, the model-based scheme does not rely on secure generation and distribution of keys amongst the agents for guaranteeing external privacy.Item DATA-DRIVEN ANALYSIS OF INDIVIDUAL THERMAL COMFORT WITH PERSONALIZED COOLING(2018) Dalgo, Daniel Alejandro; Srebric, Jelena; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation presents numerical and experimental results on the effects of Personal Cooling Devices (PCDs) on the energy consumption of buildings and the thermal comfort of occupants. The objective of this analysis was to quantify the tradeoffs of thermal comfort and energy savings associated with PCD technology. Furthermore, this investigation included an electrical cost analysis associated with PCDs at the building level for different cities across the United States. The results of energy and cost analyses, at the building level, indicated the potential for cooling energy and cost savings associated with shifting the electricity consumption during the peak hours to the off-peak hours of the day. The numerical analysis of human thermal comfort demonstrated the potential for PCDs to regulate human thermal comfort at warm environmental conditions. The thermal comfort level achieved in the numerical simulations were within the limits recommended by ASHRAE Standard 55. In addition, the numerical simulations permitted the evaluation of PCD performance based on thermal comfort, and the amount of sensible heat remove from the human body. The experimental work evaluated the performance of PCDs using both subjective and objective measurements of thermal comfort for 14 human subjects. The results demonstrated the ability of a PCD to change and maintain acceptable thermal comfort micro-environments for human subjects under warm conditions. Furthermore, the results showed that a PCD had measurable effects on physiological variables that control the thermoregulatory process of the human body. Specifically, variables such as skin temperature and heart rate variability in the time and frequency domain responded to the micro-environment created by the PCD. This research established a relationship between skin temperature, heart rate variability, and thermal comfort. Overall, this investigation performed a comprehensive analysis of the interaction of PCDs with: building energy consumption, human subjects, and human physiological processes; and demonstrated the potential to recognize human subjects’ thermal comfort based on physiological signals.