A. James Clark School of Engineering

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Subject: search.filters.subject.optimization

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    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.
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    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.
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    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.
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    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.
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    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.
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    Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications
    (2017) Kazemipour, Abbas; Wu, Min; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second.
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    OPTIMIZATION IN WIND ENGINEERING USING CYBER-PHYSICAL SYSTEMS FOR THE DESIGN OF PARAPET WALLS
    (2017) Whiteman, Michael Lee; Phillips, Brian M; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Wind-related hazards are becoming an increasing threat as vulnerable coastal locations within the United States continue to see steady population growth. The lack of a corresponding increase in evacuation route capacity means coastal cities will need to rely on shelter-in-place strategies. The significant loss of life and economic impact from windstorms coupled with the expected population increase in vulnerable areas accentuates the need to develop new economical approaches to design and construct buildings capable of surviving extreme wind events. This thesis investigates the use of cyber-physical systems to optimize the structural design of wind-sensitive structures. The proposed design framework combines the efficiency of numerically guided optimization algorithms with the accuracy of boundary layer wind tunnel testing. The focus of this thesis is the development and evaluation of a cyber-physical approach to wind engineering design and its application to the design of a parapet for a low-rise building.
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    Joint Optimization for Social Content Delivery in Wireless Networks
    (2016) Weng, Xiangnan; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement.
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    Soot Oxidation in Hydrocarbon-free Flames
    (2015) Guo, Haiqing; Sunderland, Peter B.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    There are high uncertainties in the existing models of soot oxidation rates. To ameliorate this, soot oxidation in flames was examined using a novel ternary flame system, advanced diagnostics, and a detailed examination of past studies. The ternary flame system comprises a coflowing propylene/air diffusion flame to generate a steady soot column that flows into a hydrogen ring flame. The soot is thereby oxidized in a region far separated from soot formation, which is unlike any past study of soot oxidation in diffusion flames. Nonintrusive optical diagnostics were developed using a digital color camera to measure temperature and soot volume fraction. These diagnostics were validated using a steady laminar ethylene/air diffusion flame and were then applied to the ternary flame. Also measured in the soot flame were velocity, soot primary particle diameter, and stable species concentrations along an axial distance of 45 mm. Temperatures were between 1500 to 1750 K, and O2 partial pressures were between 10-2 to 10-1 bar. The soot flame was found to be lean, and its OH (with partial pressures between 10-4 to 10-3 bar) was expected to be equilibrated owing to the catalyzed radical recombination in the presence of soot. Soot flux and soot oxidation rates (0.5 to 6 g/m2-s) were determined. Soot burnout was 90% at 55 mm height. New soot oxidation mechanisms for O2 and OH were developed from a large body of published soot oxidation measurements. The resulting O2 mechanism has an activation energy of 195 kJ/mol, and the OH mechanism has a collision efficiency of 0.10. Predictions using the new mechanisms are within ±80% of the present measurements in the ternary flame system.
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    Reverse Logistics Network Design with Centralized Return Center
    (2014) Nabaee, Sahar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Natural resources and landfills have been overused and exhausted, resulting in the necessity of product recovery. Today, as a growing number of producers engage in product recovery, the need for efficient reverse logistics networks has become more significant than ever. An optimization modeling approach is used to develop a generic integrated forward and reverse logistics network for a firm involved in product recovery. The proposed modeling framework demonstrates and compares the performance of centralized return centers (CRC) and conventional collection centers in the reverse logistics network. Several case studies are used to analyze the sensitivity of the network structures and performances to various modeling parameters including product return ratio, product disposition ratios, and processing and handling costs at collection centers. Lastly, recommendations are made to remove model limitations and improve reverse logistics network models.