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 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 OPTIMIZATION MODELS FOR RUNWAY LOCATION, ORIENTATION AND LONGITUDINAL-GRADE DESIGN(2014) Zhou, Tong; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Airfield design is challenging for several reasons. It is limited by various constraints, such as airport usability, airspace clearance standards and geometric specifications. In addition, many factors must be considered, such as environmental issues, construction cost, obstructions, winds, runway exits and airport accessibility. The conventional runway design process relies on trial-and-error. It is laborious and usually suboptimal. Thus a mathematical model can help reduce the design time and improve the design quality. In this thesis, three models are developed for runway design optimization. The first model identifies feasible runway orientations based on crosswind limitations, the second optimizes runway location and orientation, and the third optimizes runway longitudinal-grade design. Various constraints and cost components are considered in the models. Genetic algorithms (GAs) are adopted in order to solve this problem, while a "Feasible Gates" method is used to reduce the search space and enhance computation efficiency.Item Optimization Models for Improving Bus Transit Services(2013) Kim, Myungseob; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To provide efficient public transportation services in areas with high demand variability over time, it may be desirable to switch vehicles between different types of services such as conventional services (with fixed routes and schedules) for high demand periods and flexible route services during low demand periods. Thus, this dissertation analyzes and compares conventional, flexible, and variable type bus service alternatives. Optimization formulations and numerical results show how the demand variability over time and other factors affect the relative effectiveness of such services. A model for connecting one terminal and one local region is solved with analytic optimization. Then, models are extended to consider multiple regions as well as multiple periods. Numerical results of problems for multiple regions and multiple periods are also discussed. Secondly, a problem of integration of bus transit services (i.e., conventional and flexible services) with mixed fleets of buses is explored. A hybrid method combining a genetic algorithm and analytic optimization is used. Numerical analyses confirm that the total system cost can be reduced by integrating bus services with mixed fleets and switching service types and vehicles over time among regions in order to better fit actual demand densities. The solution optimality and the sensitivity of results to several important parameters are also explored. Thirdly, transit ridership may be sensitive to fares, travel times, waiting times, and access times. Thus, elastic demands are considered in the formulations to maximize the system welfare for conventional and flexible services. Numerical examples find that with the input parameters assumed here, conventional services produce greater system welfare (consumer surplus + producer surplus) than flexible services. Numerical analysis finds that conventional and flexible services produce quite acceptable trips with the zero subsidies, compared to various financially constrained (subsidized) cases. For both conventional and flexible services, it is also found that total actual trips increase as subsidies increase. When the cost is fully subsidized, conventional services produce 79.2% of potential trips and flexible services produce 81.9% of potential trips. Several methods are applied to find solutions for nonlinear mixed integer formulations. Their advantages and disadvantages are also discussed in the conclusions section.Item A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS(2013) Strunz, Richard; Herrmann, Jeffrey W.; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy.Item Applications of Genetic Algorithms, Dynamic Programming, and Linear Programming to Combinatorial Optimization Problems(2008-10-16) Wang, Xia; Golden, Bruce L.; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Combinatorial optimization problems are important in operations research and computer science. They include specific, well-known problems such as the bin packing problem, sequencing and scheduling problems, and location and network design problems. Each of these problems has a wide variety of real-world applications. In addition, most of these problems are inherently difficult to solve, as they are NP-hard. No polynomial-time algorithm currently exists for solving them to optimality. Therefore, we are interested in developing high-quality heuristics that find near-optimal solutions in a reasonable amount of computing time. In this dissertation, we focus on applications of genetic algorithms, dynamic programming, and linear programming to combinatorial optimization problems. We apply a genetic algorithm to solve the generalized orienteering problem. We use a combination of genetic algorithms and linear program to solve the concave cost supply scheduling problem, the controlled tabular adjustment problem, and the two-stage transportation problem. Our heuristics are simple in structure and produce high-quality solutions in a reasonable amount of computing time. Finally, we apply a dynamic programming-based heuristic to solve the shortest pickup planning tour problem with time windows.Item Air Express Network Design with Hub Sorting(2007-11-05) Ngamchai, Somnuk; Schonfeld, Paul M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines an innovative strategic operation for next day air package delivery. The proposed system, in which some packages are sorted twice at two distinct hubs before arriving at their destinations, is investigated for its potential savings. A two-stage sorting operation is proposed and compared to the currently operated single-stage sorting operation. By considering the endogenous optimization of hub sorting and storage capacities, cost minimization models are developed for both operations and used for performance comparison. Two solution approaches are presented in this study, namely the Column Generation (CG) approach and the Genetic Algorithm (GA) approach. The first method is implemented to optimize the problem by means of linear programming (LP) relaxation, in which the resulting model is then embedded into a branch-and-bound approach to generate an integer solution. However, for solving realistic problem sizes, the model is intractable with the conventional time-space formulation. Therefore, a Genetic Algorithm is developed for solving a large-scale problem. The GA solution representation is classified into two parts, a grouping representation for hub assignment and an aircraft route representation for aircraft route cycles. Several genetic operators are specifically developed based on the problem characteristics to facilitate the search. After optimizing the solution, we compare not only the potential cost saving from the proposed system, but also the system's reliability based on its slack. To provide some insights on the effects of two-stage operation, several factors are explored such as the location of regional hubs, single and multiple two-stage routings and aircraft mix. Sensitivity analyses are conducted under different inputs, including different demand levels, aircraft operating costs and hub operating costs. Additional statistics on aircraft utilization, hub capacity utilization, circuity factor, average transfers per package, and system slack gain/loss by commodity, are analyzed to elucidate the changes in system characteristics.Item Task-Based Mass Optimization of Reconfigurable Robotic Manipulator Systems(2006-09-01) Koelln, Nathan Thomas; Akin, David L; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work develops a method for implementing task-based mass optimization of modular, reconfigurable manipulators. Link and joint modules are selected from a library of potential parts and assembled into serial manipulator configurations. A genetic algorithm is used to search over the potential set of combinations to find mass-minimized solutions. To facilitate the automatic evaluation required by the genetic algorithm, Denavit-Hartenberg parameters are automatically generated from module combinations. Reconfigurable manipulators are shown to be lighter than fixed-topology manipulators, demonstrating the potential utility of reconfigurable robotics technology for mass reduction in space robots.