College of Engineering

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs’ upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models’ performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

Resilience is a relatively new concept in computer security that is continuing to evolve. The research community has not settled on an exact definition for resilience, but most agree that this security property should include resistence to attack, damage recovery, and the ability for a system to learn and better resist such an attack in the future. Much of the existing research has focused on resilience solely in terms of availability, or in defining metrics to describe and compare the resilience of systems. The goal of this dissertation is to not only explore the possibility of a more general framework for resilience, but to also analyze the effectiveness of methods and technologies that can be used to measure and provide resilience.

The dissertation begins by covering common elements of computer security, providing exam- ples, addressing vulnerabilities and exploits, and suggesting potential solutions. In later sections, we examine the feasibility of the proposed solutions. Alternative solutions are compared in the context of a network’s priorities, abilities, and dependencies. Our work is inspired by the need for better security metrics in order to quantitatively evaluate and compare different systems and networks. A robust set of metrics that describe the security and recovery features of systems can provide a foundation for at least two key concepts: a network resilience communication protocol and a resilience testing framework. The communication protocol could help network administrators maintain and improve the resilience of their networks. It would facilitate communication between systems on the network so that potential threats can be quickly identified and so that changes can be made autonomously to reduce the impact of a threat without the need for human intervention. The testing framework can be used to test a system’s resilience to specific attacks, packaged as portable modules. Network administrators can use data and visualization results of this framework to make informed decisions about how to improve their resilience. The communication protocol may be able to analyze results from the testing framework to improve a network’s resilience. The goal of these two projects would be to develop solutions that can improve the resilience of networks in general, taking into account their size, security requirements, and critical functions.

Tanglegrams are a tool to infer joint evolution of species. Tanglegrams are widely used in ecology to study joint evolution history of parasitic or symbiotically linked species.  Visually, a tanglegram is a pair of evolutionary trees drawn with the leaves facing at each other. One species at the leaf of one trees is related ecologically to a species at a leaf of another tree. Related species from the two trees are connected by an edge. The number of crossings between the edges joining the leaves indicate the relatedness of the trees.        Earlier work on tanglegrams considered the same number of leaves on both the trees and one edge between the leaves of the two trees. In this paper we consider multiple edges from a leaf in the trees. These edges correspond to ecological events like duplication, host switching etc. We generalize the definition of tanglegrams to admit multiple edges between the leaves. We show integer programs for optimizing the number of crossings. The integer program has an XOR formulation very similar to the formulation for the tanglegrams. We also show how the ideas for distance minimization on tanglegrams can be extended for the generalized tanglegrams.  We show that the tanglegram drawings used in ecology can be improved to have fewer crossings using our integer programs.

We propose a system for dynamic triangle mesh processing entirely on the GPU. Our system offers an efficient data structure that allows fast updates of the underlying mesh connectivity and attributes. Our data structure partitions the mesh into small patches which allows processing all dynamic updates for each patch within the GPU's fast shared memory. This allows us to rely on speculative processing for conflict handling, which has low rollback cost while maximizing parallelism and reducing the cost of locking. Our system also introduces a new programming model for dynamic mesh processing. The programming model offers concise semantics for dynamic updates, relieving the user from having to worry about conflicting updates in the context of parallel execution. Our programming model relies on the cavity operator, which is a general mesh update operator that formulates any dynamic operation as an element reinsertion by removing a set of mesh elements and inserting others in the created void. We used our system to implement Delaunay edge flips and isotropic remeshing applications on the GPU. Our system achieves a 3—18x speedup on large models compared to multithreaded CPU solutions. Despite our additional dynamic features, our data structure also outperforms state-of-the-art GPU static data structures in terms of speed and memory requirements.

The heterogeneous and homogeneous combustion-based homogeneous charge compression ignition of fuel-lean methane-air mixtures over alumina-supported platinum catalysts was investigated experimentally and numerically in free-piston micro-engines without ignition sources. Single-shot experiments were carried out in the purely homogeneous and coupled heterogeneous and homogeneous combustion modes, involved temperature measurements, capturing the visible combustion image sequences, exhaust gas analysis, and the physicochemical characterization of catalysts. Simulations were performed with a two-dimensional transient model that includes detailed heterogeneous and homogeneous chemistry and transport, leakage, and free-piston motion to gain physical insight and to explore the heterogeneous and homogeneous combustion characteristics. The micro-engine performance concerning combustion efficiency, mass loss, energy density, and free-piston dynamics was investigated. The results reveal that heterogeneous reactions cause earlier ignition, which is very favourable for the micro-device. Both purely homogeneous and coupled heterogeneous and homogeneous combustion of methane-air mixtures in a narrow cylinder with a diameter of 3 mm and a height of approximately 0.3 mm are possible. Heat losses result in higher mass losses. The coupled heterogeneous and homogeneous mode can not only significantly improve the combustion efficiency, in-cylinder temperature and pressure, output power and energy density, but also reduce the mass loss because of its lower compression ratio and less time spent around the top dead centre and during the expansion stroke, indicating that this coupled mode is a promising combustion scheme for micro-engines.

In spite of significant efforts to investigate the ability of hydrogenated and fluorinated graphene to conduct heat, little research has focused particularly upon their other thermal properties, such as thermal contraction and heat capacity, which have implications for the development of thermal nanotechnology. In an attempt to determine these thermal properties, a few experiments have been carried out, with rather conflicting results. In the present study, calculations were performed using molecular dynamics to investigate the thermal properties of graphane and fluorographene and especially the phenomena involved. The thermal expansion coefficients and heat capacities of the two-dimensional materials were determined at different temperatures. The results indicated that graphane is thermally contracted more significantly than graphene. The calculated molar heat capacity at constant volume is about 25.00 J/(mol·K) for graphene and about 29.26 J/(mol·K) for graphane. The specific heat capacity of fluorographene is always lower than that of graphane. A negative relationship does exist between the binding energy and the temperature.

Flow of a fluid past a body is a very complicated phenomenon. Computational fluid dynamics is used for studying the characteristics of flow past an array of hemispherical protrusions that is disposed on the wall surfaces of a millisecond microchannel reactor. Protrusions can be used to improve the transport processes involved, but the causes of the phenomena are still incompletely understood. Parametric analyses are performed under different sets of circumstances to delineate the role of geometric features and operation conditions in reactor performance. Dimensionless quantities are used to simplify the characterization of the reactor system with multiple interacting transport phenomena. The mechanisms involved in the intensified processes are analysed, and performance improvement recommendations are presented. The results indicate that the protruded reactor behaves effectively and good yields can be obtained with only milliseconds residence of the mixtures within the channels. The reactor offers the unique advantage for hydrogen production from methanol in that process intensification is realized while preserving the energy balance between the exothermic and endothermic processes. However, the flow rates must be adjusted as needed to maximize production of hydrogen and minimize pressure drops. The momentum diffusivity is more dominant around the protrusion regions than in the other regions. The thermal diffusivity is more dominant in the protruded channels than in the flat channels. The results have implications for hydrogen production and beyond for the study of transport phenomena in microchemical systems.