UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Working in Reverse: Advancing Inverse Optimization in the Fields of Equilibrium and Infrastructure Modeling(2022) Allen, Stephanie Ann; Gabriel, Steven A; Dickerson, John P; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation and infrastructure modeling allows us to pursue societal aims such as improved disaster management, traffic flow, and water allocation. Equilibrium programming enables us to represent the entities involved in these applications such that we can learn more about their dynamics. These entities include transportation users and market players. However, determining the parameters in these models can be a difficult task because the entities involved in these equilibrium processes may not be able to articulate or to disclose the parameterizations that motivate them. The field of inverse optimization (IO) offers a potential solution to this problem by taking observed equilibria to these systems and using them to parameterize equilibrium models. In this dissertation, we explore the use of inverse optimization to parameterize multiple new or understudied subclasses of equilibrium problems as well as expand inverse optimization's application to new infrastructure domains. In the first project of our dissertation, our contribution to the literature is to propose that IO can be used to parameterize cost functions in multi-stage stochastic programs for disaster management and can be used in disaster support systems. We demonstrate in most of our experiments that using IO to obtain the hidden cost parameters for travel on a road network changes the protection decisions made on that road network when compared to utilizing the mean of the parameter range for the hidden parameters (also referred to as ``uniform cost''). The protection decisions made under the IO cost parameterizations versus the true cost parameterizations are similar for most of the experiments, thus lending credibility to the IO parameterizations. In the second project of our dissertation, we extend a well-known framework in the IO community to the case of jointly convex generalized Nash equilibrium problems (GNEPs). We demonstrate the utility of this framework in a multi-player transportation game in which we vary the number of players, the capacity level, and the network topology in the experiments as well as run experiments assuming the same costs among players and different costs among players. Our promising results provide evidence that our work could be used to regulate traffic flow toward aims such as reduction of emissions. In the final project of our dissertation, we explore the general parameterization of the constant vector in linear complementarity problems (LCPs), which are mathematical expressions that can represent optimization, game theory, and market models (Gabriel et al., 2012). Unlike the limited previous work on inverse optimization for LCPs, we characterize theoretical considerations regarding the inverse optimization problem for LCPs, prove that a previously proposed IO solution model can be dramatically simplified, and handle the case of multiple solution data points for the IO LCP problem. Additionally, we use our knowledge regarding LCPs and IO in a water market allocation case study, which is an application not previously explored in the IO literature, and we find that charging an additional tax on the upstream players enables the market to reach a system optimal. In sum, this dissertation contributes to the inverse optimization literature by expanding its reach in the equilibrium problem domain and by reaching new infrastructure applications.Item AN INTEGRATED, MULTI-PHYSICS ANALYSIS AND DESIGN OPTIMIZATION FRAMEWORK FOR AIR-TO-REFRIGERANT HEAT EXCHANGERS WITH SHAPE-OPTIMIZED TUBES(2022) Tancabel, James M; Radermacher, Reinhard; Aute, Vikrant; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Air-to-fluid Heat eXchangers (HX) are fundamental components of many systems we encounter in our daily lives, from Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems to electronics cooling, automotive, power plants, and aviation applications. The importance of HXs is evident in the level of investment devoted to HX innovation in recent years. While current state-of-the-art HXs have adequately addressed past challenges, ever-increasing energy demands and increasingly stringent global energy standards require novel tools and methodologies which can quickly and efficiently develop the next generation of high-performance HXs. In recent years, advancements in computational tools and advanced manufacturing technologies have enabled engineers to consider small characteristic diameter HX tubes with novel shapes and topologies which were not feasible even a decade ago. These small diameter, shape-optimized tubes have been shown to perform the same job as existing HXs while offering significant and desirable improvements in performance metrics such as envelope volume, face area, weight, and refrigerant charge. However, the structural integrity of shape-optimized tubes was often guaranteed by utilizing conservative tube thicknesses to ensure equipment safety, prevent refrigerant leakages, and satisfy product qualification requirements, resulting in increased material consumption and manufacturer costs while reducing the likelihood of industry acceptance for the new technology. Additionally, the actual HX operating conditions are often different from design conditions, resulting in significant performance degradations. For example, novel HX design is typically assumes uniform normal airflow on the HX face area even though HXs in HVAC&R applications rarely experience such flows, and compact HXs have been shown to experience water bridging under dehumidification conditions, which greatly impacts HX performance. This research sheds light on the next generation of air-to-refrigerant HXs and aims to address several practical challenges to HX commercialization such as novelty, manufacturing, and operational challenges through the use of comprehensive multi-physics and multi-scale modeling. The novelty of this research is summarized as follows: i. A new, comprehensive and experimentally validated air-to-refrigerant HX optimization framework with simultaneous thermal-hydraulic performance and mechanical strength considerations for novel, non-round, shape- and topology-optimized tubes capable of optimizing single and two-phase HX designs for any refrigerant choice and performance requirement with significant engineering time savings compared to conventional design practices. The framework was exercised for a wide range of applications, resulting in HXs which achieved greater than 20 improved performance, than 20% reductions in size, and 25% reductions in refrigerant charge. ii. Development of a fundamental understanding of performance degradation for HXs with shape- and topology-optimized tubes under typical HX installation configurations in practical applications such as inclined and A-type configurations. New modeling capabilities were integrated into existing HX modeling tools to accurately predict the airflow maldistribution profiles for HXs with shape- and topology-optimized tubes without the need for computationally-expensive CFD simulations. iii. Development of a framework to model and understand the impact of moist air dehumidification on the performance of highly compact HX tube bundles which utilize generalized, non-round tubes. Correlations for Lewis number were developed to understand whether traditional HX dehumidification modeling assumptions remained valid for new HXs with generalized, non-round tube bundles. Such an understanding is critical to accurately and efficiently modeling HX performance under dehumidifying (i.e., wet-coil) conditions.Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item COST-EFFECTIVE PROGNOSTICS AND HEALTH MONITORING OF LOCALLY DAMAGED PIPELINES WITH HIGH CONFIDENCE LEVEL(2020) Aria, Amin; Modarres, Mohammad; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Localized pipeline damages, caused by degradation processes such as corrosion, are prominent, can result in pipeline failure and are expensive to monitor. To prevent pipeline failure, many Prognostics and Health Monitoring (PHM) approaches have been developed in which sensor network for online, and human inspection for offline data gathering are separately used. In this dissertation, a two-level (segment- and integrated-level) PHM approach for locally damaged pipelines is proposed where both of these degradation data gathering schemes (i.e., detection methods) are considered simultaneously. The segment-level approach, in which the damage behavior is considered to be uniform, consists of a static and a dynamic phase. In the static phase, a new optimization problem for the health monitoring layout design of locally damaged pipelines is formulated. The solution to this problem is an optimal configuration (or layout) of degradation detection methods with a minimized health monitoring cost and a maximized likelihood of damage detection. In the dynamic phase, considering the optimal layout, an online fusion of high-frequency sensors data and low-frequency inspection information is conducted to estimate and then update the pipeline’s Remaining Useful Life (RUL) estimate. Subsequently, the segment-level optimization formulation is modified to improve its scalability and facilitate updating layouts considering the online RUL estimates. Finally, at the integrated-level, the modified segment-level approach is used along with Stochastic Dynamic Programming (SDP) to produce an optimal set of layouts for a long pipeline consisting of multiple segments with different damage behavior. Experimental data and several notional examples are used to demonstrate the performance of the proposed approaches. Synthetically generated damage data are used in two examples to demonstrate that the proposed segment-level layout optimization approach results in a more robust solution compared to single detection approaches and deterministic methods. For the dynamic segment-level phase, acoustic emission sensor signals and microscopic images from a set of fatigue crack experiments are considered to show that combining sensor- and image-based damage size estimates leads to accuracy improvements in RUL estimation. Lastly, using synthetically generated damage data for three hypothetical pipeline segments, it is shown that the constructed integrated-level approach provides an optimal set of layouts for several pipeline segments.Item ANALYSIS OF OBJECTIVES AND CONSTRAINTS TOWARDS PREDICTIVE MODELING OF COMPLEX METABOLISM(2020) Boruah, Navadeep; Sriram, Ganesh; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The central theme of this dissertation is predictive modeling of metabolism in complex biological systems with genome-scale stoichiometric metabolic models to (i) gain nontrivial insight on cellular metabolism and (ii) provide justifications for hitherto unexplained metabolic phenomena. The crux of high-quality predictive modeling with genome-scale stoichiometric metabolic models is appropriate selection of (i) a biologically relevant objective function and (ii) a set of constraints based on experimental data. However, in many complex systems, like a plant tissue with its wide array of specialized cells, a biological objective is not always apparent. Additionally, generation of experimental data to develop biochemically relevant constraints can be nearly impossible in systems that cannot be cultured under a controlled environment for the duration of an experiment. Such limitations necessitate careful reformulation of the biological question, development of novel methods and data analysis strategies. Here, we push the boundaries of predictive modeling by demonstrating its first application in deciphering hitherto unexplained metabolic phenomena and in developing novel hypotheses on metabolism. Towards achieving this goal, we developed several novel approaches and employed them in diverse biological systems. Firstly, we investigated the selection of carbohydrate degrading pathway employed by Saccharophagus degradans, an aerobic cellulosic marine bacterium. Flux balance analyses of its growth in nutrient rich hypoxic marine environment predicted that the selection of carbohydrate degrading pathway is possibly influenced by inorganic nutrient availability. Secondly, multi-tissue genome-scale metabolic modeling of Populus trichoparpa, a perennial woody tree, and analyses with a novel strategy based on multiple biologically relevant metrics provided a metabolic justification for the predominance of glutamine as the predominant nitrogen transport amino acid for internal nitrogen recycling. Thirdly, predictive modeling of maize grain filling predicted amino acid fermentation as a mechanism for expending excess reductant cofactors for continual starch synthesis in the hypoxic environment of endosperm. Finally, we developed bilevel optimization framework to integrate publicly available transcriptome datasets with metabolic networks. This framework predicted accumulation of specific classes of maize endosperm storage protein at distinct stages of grain filling. We anticipate that the employment of these aforementioned approaches in other biological systems will lead to the generation of a wide array of nontrivial hypothesis on cellular metabolism and to develop targeted experiments to validate them.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 MULTI-AGENT UNMANNED UNDERWATER VEHICLE VALIDATION VIA ROLLING-HORIZON ROBUST GAMES(2019) Quigley, Kevin J; Gabriel, Steven A.; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomy in unmanned underwater vehicle (UUV) navigation is critical for most applications due to inability of human operators to control, monitor or intervene in underwater environments. To ensure safe autonomous navigation, verification and validation (V&V) procedures are needed for various applications. This thesis proposes a game theory-based benchmark validation technique for trajectory optimization for non-cooperative UUVs. A quadratically constrained nonlinear program formulation is presented, and a "perfect-information reality" validation framework is derived by finding a Nash equilibrium to various two-player pursuit-evasion games (PEG). A Karush-Kuhn-Tucker (KKT) point to such a game represents a best-case local optimum, given perfect information available to non-cooperative agents. Rolling-horizon foresight with robust obstacles are incorporated to demonstrate incomplete information and stochastic environmental conditions. A MATLAB-GAMS interface is developed to model the rolling-horizon game, and is solved via a mixed complementarity problem (MCP), and illustrative examples show how equilibrium trajectories can serve as benchmarks for more practical real-time path planners.Item CONTROLLER SYNTHESIS UNDER INFORMATION AND FINITE-TIME LOGICAL CONSTRAINTS(2018) Maity, Dipankar; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In robotics, networks, and many related fields, a typical controller design problem needs to address both logical and informational constraints. The logical constraints may arise due to the complex task description or decision making process, while the information-related constraints emerge naturally as a consequence of the limitations on communication and computation capabilities. In the first part of the thesis, we consider the problem of synthesizing an event-based controller to address the information-related constraints in the controller design. We consider dynamical systems that are operating under continuous state feedback. This assumes that the measurements are continuously transmitted to the controller in order to generate the input and thus, increases the cost of communication by requiring huge communication resources. In many situations, it so happens that the measurement does not change fast enough that continuous transmission is required. Taking motivation from this, we consider the case where instead continuous feedback we seek an intermittent-feedback. As a result, the system trajectory will deviate from its ideal behavior. However, the question is how much would it deviate? Given the allowed bound on this deviation, can we design some controller that requires fewer measurements than the original controller and still manages to keep the deviation within this prescribed bound? Two important questions remain: 1) What will be the structure of the (optimal) controller? 2) How will the system know the (optimal) instances to transmit the measurement? When the system sends out measurement to controller, it is called as an ``event". Thus, we are looking for an event-generator and a controller to perform event-based control under the constraints on the availability of the state information. The next part focuses on controller synthesis problems that have logical, spatio-temporal constraints on the trajectory of the system; a robot motion planning problem fits as a good example of these kind of finite-time logically constrained problems. We adopt an automata-based approach to abstract the motion of the robot into an automata, and verify the satisfaction of the logical constraints on this automata. The abstraction of the dynamics of the robot into an automata is based on certain reachability guarantee of the robot's dynamics. The controller synthesis problem over the abstracted automata can be represented as a shortest-path-problem. In part III, we consider the problem of jointly addressing the logical and information constraints. The problem is approached with the notion of robustness of logical constraints. We propose two different frameworks for this problem with two different notions of robustness and two different approaches for the controller synthesis. One framework relies on the abstraction of the dynamical systems into a finite transition system, whereas the other relies on tools and results from prescribed performance control to design continuous feedback control to satisfy the robust logical constraints. We adopt an hierarchical controller synthesis method where a continuous feedback controller is designed to satisfy the (robust) logical constraints, and later, that controller is replaced by a suitable event-triggered intermittent feedback controller to cope with informational constraints.Item Approximation Algorithms for Geometric Clustering and Touring Problems(2018) Bercea, Ioana Oriana; Khuller, Samir; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Clustering and touring are two fundamental topics in optimization that have been studied extensively and have ``launched a thousand ships''. In this thesis, we study variants of these problems for Euclidean instances, in which clusters often correspond to sensors that are required to cover, measure or localize targets and tours need to visit locations for the purpose of item delivery or data collection. In the first part of the thesis, we focus on the task of sensor placement for environments in which localization is a necessity and in which its quality depends on the relative angle between the target and the pair of sensors observing it. We formulate a new coverage constraint that bounds this angle and consider the problem of placing a small number of sensors that satisfy it in addition to classical ones such as proximity and line-of-sight visibility. We present a general framework that chooses a small number of sensors and approximates the coverage constraint to arbitrary precision. In the second part of the thesis, we consider the task of collecting data from a set of sensors by getting close to them. This corresponds to a well-known generalization of the Traveling Salesman Problem (TSP) called TSP with Neighborhoods, in which we want to compute a shortest tour that visits at least one point from each unit disk centered at a sensor. One approach is based on an observation that relates the optimal solution with the optimal TSP on the sensors. We show that the associated bound can be improved unless we are in certain exceptional circumstances for which we can get better algorithms. Finally, we discuss Maximum Scatter TSP, which asks for a tour that maximizes the length of the shortest edge. While the Euclidean version admits an efficient approximation scheme and the problem is known to be NP-hard in three dimensions or higher, the question of getting a polynomial time algorithm for two dimensions remains open. To this end, we develop a general technique for the case of points concentrated around the boundary of a circle that we believe can be extended to more general cases.Item HYBRID ROUTING MODELS UTILIZING TRUCKS OR SHIPS TO LAUNCH DRONES(2018) Poikonen, Stefan Allan; Golden, Bruce L; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Technological advances for unmanned aerial vehicles, commonly referred to as drones, have opened the door to a number of new and interesting applications in areas including military, healthcare, communications, cinematography, emergency response, and logistics. However, limitations due to battery capacity, maximum take-off weight, finite range of wireless communications, and legal regulations have restricted the effective operational range of drones in many practical applications. Several hybrid operational models involving one or more drones launching from a larger vehicle, which may be a ship, truck, or airplane, have emerged to help mitigate these range limitations. In particular, the drones utilize the larger vehicle as both a mobile depot and a recharging or refueling platform. In this dissertation, we describe routing models that leverage the tandem of one or more drones with a larger vehicle. In these models, there is generally a set of targets that should be visited in an efficient (usually time-minimizing) manner. By using multiple vehicles, these targets may be visited in parallel thereby reducing the total time to visit all targets. The vehicle routing problem with drones (VRPD) and traveling salesman problem with a drone (TSP-D) consider hybrid truck-and-drone models of delivery, where the goal is to minimize the time required to deliver a set of packages to their respective customers and return the truck(s) and drone(s) to the origin depot. In both problems, the drone can carry one homogeneous package at a time. Theoretical analysis, exact solution methods, heuristic solution methods, and computational results are presented. In the mothership and drone routing problem (MDRP), we consider the case where the larger launch vehicle is free to move in Euclidean space (the open seas) and launch a drone to visit one target location at a time, before returning to the ship to pick up new cargo or refuel. The mothership and high capacity drone routing problem (MDRP-HC) is a generalization of the mothership and drone routing problem, which allows the drone to visit multiple targets consecutively before returning to the ship. MDRP and MDRP-HC contain elements of both combinatorial optimization and continuous optimization. In the multi-visit drone routing problem (MVDRP), a drone can visit multiple targets consecutively before returning to the truck, subject to energy constraints that take into account the weight of packages carried by the drone.