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.
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Item Mathematical Model and Framework for Multi-Phase Project Optimization(2016) Shafahi, Ali; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research aims to assist investors of “real” tangible assets such as construction projects in making an optimal portfolio of phased and regular projects which will yield the best financial outcome calculated in terms of discounted cash flow of future anticipated revenues and costs. We use optimization techniques to find the optimal timing and phasing of a single project that has the potential of being decomposed into smaller sequential phases. Existence of uncertainties is inevitable especially in cases in which we are planning for long durations. In the presence of these uncertainties, full upfront commitment to large projects may jeopardize the rationality of investments and cause substantial economic risks. Breaking a big project into smaller stages (phases) and implementing a staged development is a potential mechanism to hedge the risk. Under this approach, by adding managerial flexibilities, we may choose to abandon a project at any time once the uncertain outcomes are not favorable. In addition to the benefits resulting from hedging unfavorable risks, phasing a project can transform a financially infeasible project into a feasible one due to less load on capital budgets during each time. Once some phases of a project are delayed and planned to be implemented sequentially, it is important to prepare the infrastructure required for their future development. Initially, we present a Mixed Integer Programming (MIP) model for the deterministic case with no uncertainties that considers interrelationships between phases of projects such as scheduling and costs (economy of scales) in addition to the initial infrastructural investment required for implementation of future phases. Pairing possible phases of a project and doing them in parallel is beneficial due to positive synergies between phases but on the downside requires larger capital investments. Unavailability of enough budgets to fully develop a profitable project will cause the investment to be carried out in different phases e.g. during times when the required capital for developing the next phase (or group of phases) is available. After, presenting the model for the deterministic case, we present a scenario-based multi-stage MIP model for the stochastic case. The source of uncertainty considered is future demand that is modeled using a trinomial lattice. We then present two methods for solving the stochastic problem and finding the value of the here and now decision variable (the size of the infrastructure/foundation). Finding the value of the here and now decision variable for all scenarios using a novel technique that does not require solving all the scenarios is the first method. The second method combines simulation and optimization to find good solutions for the here and now decision variable. Lastly, we present a MIP for the deterministic multi-project case. In this setting, projects could have multiple phases. The MIP will help the managers in making the project selection and scheduling decision simultaneously. It will also assist the managers in making appropriate decisions for the size of the infrastructure and the implementation schedule of the phases of each project. To solve this complex model, we present a pre-processing step that helps reduce the size of the problem and a heuristic that finds good solutions very fast.Item Adaptive Magnetorheological Seat Suspension for Shock Mitigation(2014) Singh, Harinder Jit; Wereley, Norman M; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research focuses on theoretical and experimental analysis of an adaptive seat suspension employing magnetorheological energy absorber with the objective of minimizing injury potential to seated occupant of different weights subjected to broader crash intensities. The research was segmented into three tasks: (1) development of magnetorheological energy absorber, (2) biodynamic modeling of a seated occupant, and (3) control schemes for shock mitigation. A linear stroking semi-active magnetorheological energy absorber (MREA) was designed, fabricated and tested for intense impact conditions with piston velocities up to 8 m/s. MREA design was optimized on the basis of Bingham-plastic model (BPM model) in order to maximize the energy absorption capabilities at high impact velocities. Computational fluid dynamics and magnetic FE analysis were conducted to validate MREA performance. Subsequently, low-speed cyclic testing (0-2 Hz subjected to 0-5.5 A) and high-speed drop testing (0-4.5 m/s at 0 A) were conducted for quantitative comparison with the numerical simulations. Later, a nonlinear four degrees-of-freedom biodynamic model representing a seated 50th percentile male occupant was developed on the basis of experiments conducted on Hybrid II 50th percentile male anthropomorphic test device. The response of proposed biodynamic model was compared quantitatively against two different biodynamic models from the literature that are heavily implemented for obtaining biodynamic response under impact conditions. The proposed biodynamic model accurately predicts peak magnitude, overall shape and the duration of the biodynamic transient response, with minimal phase shift. The biodynamic model was further validated against 16 impact tests conducted on horizontal accelerator facility at NAVAIR for two different shock intensities. Compliance effects of human body were also investigated on the performance of adaptive seat suspension by comparing the proposed biodynamic model response with that of a rigid body response. Finally, three different control schemes were analyzed for maximizing shock attenuation using semi-active magnetorheological energy absorber. High-speed drop experiments were conducted by dropping two rigid payloads of 240 and 340 lb mass from heights of 35 and 60 inch to simulate different impact intensities. First control scheme called constant stroking load control offered inflexible stroking load irrespective of varying impact severity or occupant weight. The other two control schemes: terminal trajectory control and optimal control adapted stroking load as per the shock intensity. The control schemes were compared on the basis of their adaptability and ease of implementation. These tools can serve as the basis for future research and development of state-of-the-art crashworthy seat suspension designs that further enhance occupant protection compared to limited performance of existing passive crashworthy concepts.Item A Proposed Mechanical-Metabolic Model of the Human Red Blood Cell(2014) Oursler, Stephen Mark; Solares, Santiago D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The theoretical modeling and computational simulation of human red blood cells is of interest to researchers for both academic and practical reasons. The red blood cell is one of the simplest in the body, yet its complex behaviors are not fully understood. The ability to perform accurate simulations of the cell will assist efforts to treat disorders of the cell. In this thesis, a computational model of a human red blood cell that combines preexisting mechanical and metabolic models is proposed. The mechanical model is a coarse-grained molecular dynamics model, while the metabolic model considers the set of chemical reactions as a system of first-order ordinary differential equations. The models are coupled via the connectivity of the cytoskeleton with a novel method. A simulation environment is developed in MATLAB® to evaluate the combined model. The combined model and the simulation environment are described in detail and illustrated in this thesis.