A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
Browse
6 results
Search Results
Item Design and Characterization of Additively Manufactured Lightweight Metal Structures with Equivalent Compliance and Fatigue Resistance(2021) Santos, Luis S; Bruck, Hugh A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Additive Manufacturing (AM) has been a disruptive manufacturing technology allowing for control of geometric features and material distributions, potentially starting at the atomistic level, to realize structures with lighter weights. However, it is still begin used primarily as a rapid prototyping tool due to challenges arising from various issues that need to be addressed before commercial parts can be deployed. Three of those issues are: (1) characterization of mechanical properties that may vary spatially, (2) identification of novel defects in the parts, and (3) new design approaches that account for the unique capabilities of AM processes and their impact on fatigue resistance.This dissertation addresses these three issues by developing a cyclical indentation technique to characterize the fatigue properties of geometric features only capable with AM. The method produces the degradation of the material stiffness as the number of cyclic loads increases and is capable of generating an entire S-N curve with a single test at sub-millimeter scales. Geometric features are then analyzed by running a thermal and mechanical simulation of a Direct Metal Laser Sintering (DMLS) printing process. The new simulation can account for buckling of features with high aspect ratios, such as low percentage infills or high levels of unit cell porosity, and predicts distortions with less than 5% error. This computational approach is useful for analyzing parts before printing and informs designers about regions in the part that may need modification to prevent buckling. Finally, the experimental and computational techniques are combined to design structures with macroscale topological features and microscale unit cell features that are fatigue resistant.Item AVIATION CONGESTION MANAGEMENT IMPROVEMENTS IN MODELING THE PREDICTION, MITIGATION, AND EVALUATION OF CONGESTION IN THE NATIONAL AIRSPACE SYSTEM(2014) Vlachou, Kleoniki; Lovell, David J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The air transportation system in the United States is one of the most complex systems in the world. Projections of increasing air traffic demand in conjunction with limited capacity, that is volatile and affected by exogenous random events, represent a major problem in aviation system management. From a management perspective, it is essential to make efficient use of the available resources and to create mechanisms that will help alleviate the problems of the imbalance between demand and capacity. Air traffic delays are always present and the more air traffic increases the more the delays will increase with very unwanted economic impacts. It is of great interest to study them further in order to be able to more effectively mitigate them. A first step would be to try to predict them under various circumstances. A second step would be to develop various mechanisms that will help in reducing delays in different settings. The scope of this dissertation is to look closer at a threefold approach to the problem of congestion in aviation. The first effort is the prediction of delays and the development of a model that will make these predictions under a wide variety of distributional assumptions. The work presented here is specifically on a continuum approximation using diffusion methods that enables efficient solutions under a wide variety of distributional assumptions. The second part of the work effort presents the design of a parsimonious language of exchange, with accompanying allocation mechanisms that allow carriers and the FAA to work together quickly, in a Collaborative Decision Making environment, to allocate scarce capacity resources and mitigate delays. Finally, because airlines proactively use longer scheduled block times to deal with unexpected delays, the third portion of this dissertation presents the assessment of the monetary benefits due to improvements in predictability as manifested through carriers' scheduled block times.Item Predictive Analytics Lead to Smarter Self-Organizing Directional Wireless Backbone Networks(2013) Coleman, David M.; Davis, Christopher C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Directional wireless systems are becoming a cost-effective approach towards providing a high-speed, reliable, broadband connection for the ubiquitous mobile wireless devices in use today. The most common of these systems consists of narrow-beam radio frequency (RF) and free-space-optical (FSO) links, which offer speeds between 100Mbps and 100Gbps while offering bit-error-rates comparable to fixed fiber optic installations. In addition, spatial and spectral efficiencies are accessible with directional wireless systems that cannot be matched with broadcast systems. The added benefits of compact designs permit the installation of directional antennas on-board unmanned autonomous systems (UAS) to provide network availability to regions prone to natural disasters, in maritime situations, and in war-torn countries that lack infrastructure security. In addition, through the use of intelligent network-centric algorithms, a flexible airborne backbone network can be established to dodge the scalability limitations of traditional omnidirectional wireless networks. Assuring end-to-end connectivity and coverage is the main challenge in the design of directional wireless backbone (DWB) networks. Conflating the duality of these objectives with the dynamical nature of the environment in which DWB networks are deployed, in addition to the standardized network metrics such as latency-minimization and throughput maximization, demands a rigorous control process that encompasses all aspects of the system. This includes the mechanical steering of the directional point-to-point link and the monitoring of aggregate network performance (e.g. dropped packets). The inclusion of processes for topology control, mobility management, pointing, acquisition, and tracking of the directional antennas, alongside traditional protocols (e.g. IPv6) provides a rigorous framework for next-generation mobile directional communication networks. This dissertation provides a novel approach to increase reliability in reconfigurable beam-steered directional wireless backbone networks by predicating optimal network reconfigurations wherein the network is modeled as a giant molecule in which the point-to-point links between two UASs are able to grow and retract analogously to the bonds between atoms in a molecule. This cross-disciplinary methodology explores the application of potential energy surfaces and normal mode analysis as an extension to the topology control optimization. Each of these methodologies provides a new and unique ability for predicting unstable configurations of DWB networks through an understanding of second-order principle dynamics inherent within the aggregate configuration of the system. This insight is not available through monitoring individual link performance. Together, the techniques used to model the DWB network through molecular dynamics are referred to as predictive analytics and provide reliable results that lead to smarter self-organizing reconfigurable beam-steered DWB networks. Furthermore, a comprehensive control architecture is proposed that complements traditional network science (e.g. Internet protocol) and the unique design aspects of DWB networks. The distinct ability of a beam-steered DWB network to adjust the direction of its antennas (i.e. reconfigure) in response to degraded effects within the atmosphere or due to an increased separation of nodes, is not incorporated in traditional network processes such re-routing mechanism, and therefore, processes for reconfiguration can be abstracted which both optimize the physical interconnections while maintaining interoperability with existing protocols. This control framework is validated using network metrics for latency and throughput and compared to existing architectures which use only standard re-routing mechanisms. Results are shown that validate both the analogous molecular modeling of a reconfigurable beam-steered directional wireless backbone network and a comprehensive control architecture which coalesces the unique capabilities of reconfiguration and mobility of mobile wireless backbone networks with existing protocols for networks such as IPv6.Item Quantitative Prediction of Tip-Sample Repulsive Forces and Sample Deformation in Tapping-Mode Frequency and Force Modulation Atomic Force Microscopy(2008-08-27) Crone, Joshua C; Solares, Santiago D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ability to predict sample deformation and the resultant interaction forces is a vital component to preventing sample damage and acquiring accurate height traces in atomic force microscopy (AFM). By using the recently developed frequency and force modulation (FFM) control scheme, a prediction method is developed by coupling previously developed analytical work with numerical integration of the equation of motion for the AFM tip. By selecting a zero resonance frequency shift, the sample deformation is found to depend only on those parameters defining the tip-sample interaction forces. The results are represented graphically and through a multiple regression model so that the user can predict the tip penetration and maximum repulsive force with knowledge of the maximum attractive force and steepness of the repulsive regime in the tip-sample interaction force curve. The prediction model is shown to be accurate for a wide range of imaging conditions.Item A New Physics-of-Failure Based VLSI Circuits Reliability Simulation and Prediction Methodology(2007-08-27) QIN, JIN; Bernstein, Joseph B; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It has long been a challenge for reliability engineers to provide accurate VLSI circuits reliability simulation and prediction. The decreasing feature sizes, coupled with non-ideal voltage scaling, raises new reliability concerns such as negative bias temperature instability (NBTI) and adversely affects those long-existed failure mechanisms: electromigration (EM), hot carrier degradation (HCD) and time dependent dielectric breakdown (TDDB). The multiple failure mechanisms effect, together with the increasing circuit complexity make the prediction more difficult to tackle with. A new physics-of-failure based VLSI circuit reliability prediction methodology is proposed to handle the simulation and prediction challenges. The new methodology takes an unique top-down, bottom-up approach to reduce the modeling and simulation complexity. Detailed application breakdown reveals the cell's operation profile. Cell-level reliability characterization provides accurate operation-based dynamic stress modeling by utilizing the physics-of-failure models. For each failure mechanism, the best-fit lifetime distribution is selected to provide reliability prediction. The application-specific circuit reliability is further predicted by considering the system structure. A 90nm 64Kb SRAM module is designed and used as an example to demonstrate the prediction methodology. With the given application profile, simulation results showed that TDDB is the most serious reliability concern for the SRAM bit cell, NBTI is in the second place, and HCD has a negligible degradation effect. The memory core's reliability prediction shows the core has a low constant failure rate (2.90E-4 FIT) before 5.8E+4 hours, and an increasing failure rate after that because NBTI wearout starts to kick in.Item A Reliable Travel Time Prediction System With Sparsely Distributed Detectors(2007-05-22) Zou, Nan; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to develop a travel time prediction system that needs only a small number of reliable traffic detectors to perform accurate real-time travel time predictions under recurrent traffic conditions. To ensure its effectiveness, the proposed system consists of three principle modules: travel time estimation module, travel time prediction module, and the missing data estimation module. The travel time estimation module with its specially designed hybrid structure is responsible for estimating travel times for traffic scenarios with or without sufficient field observations, and for supplying the estimated results to support the prediction module. The travel time prediction module is developed to take full advantage of various available information, including historical travel times, geometric features, and daily/weekly traffic patterns. It can effectively deal with various traffic patterns with its multiple embedded models, including the primary module of a multi-topology Neural Network model with a rule-based clustering function and the supplemental module of an enhanced k-Nearest Neighbor model. To contend with the missing data issue, which occurs frequently in any real-world system, this study incorporates a missing data estimation module in the travel time prediction system, which is based on the multiple imputation technique to estimate both the short- and long-term missing traffic data so as to avoid interrupting the operations. The system developed in this study has been implemented with data from 10 roadside detectors on a 25-mile stretch of I-70 eastbound, and its performance has been tested against actual travel time data collected by an independent evaluation team. Results of extensive evaluation have indicated that the developed system is capable of generating reliable prediction of travel times under various types of traffic conditions and outperforms both state-of-the-practice and state-of-the-art models in the literature. Its embedded missing data estimation models also top existing methods and are able to maintain the prediction system under a reliable state when one of its detectors at a key location experience the data missing rate from 20% to 100% during uncongested, congested and transition periods.