Control algorithms for large scale adaptive optics

In this dissertation, the problem of creating effective large scale Adaptive Optics (AO) systems control algorithms for the new generation of giant optical telescopes is addressed. The effectiveness of AO control algorithms is evaluated in several respects, such as computational complexity, compensation error rejection and robustness, i.e. reasonable insensitivity to the system imperfections. The results of this research are summarized as follows: 1. Robustness study of Sparse Minimum Variance Pseudo Open Loop Controller (POLC) for multi-conjugate adaptive optics (MCAO). The AO system model that accounts for various system errors has been developed and applied to check the stability and performance of the POLC algorithm, which is one of the most promising approaches for the future AO systems control. It has been shown through numerous simulations that, despite the initial assumption that the exact system knowledge is necessary for the POLC algorithm to work, it is highly robust against various system errors. 2. Predictive Kalman Filter (KF) and Minimum Variance (MV) control algorithms for MCAO. The limiting performance of the non-dynamic Minimum Variance and dynamic KF-based phase estimation algorithms for MCAO has been evaluated by doing Monte-Carlo simulations. The validity of simple near-Markov autoregressive phase dynamics model has been tested and its adequate ability to predict the turbulence phase has been demonstrated both for single- and multiconjugate AO. It has also been shown that there is no performance improvement gained from the use of the more complicated KF approach in comparison to the much simpler MV algorithm in the case of MCAO. 3. Sparse predictive Minimum Variance control algorithm for MCAO. The temporal prediction stage has been added to the non-dynamic MV control algorithm in such a way that no additional computational burden is introduced. It has been confirmed through simulations that the use of phase prediction makes it possible to significantly reduce the system sampling rate and thus overall computational complexity while both maintaining the system stable and effectively compensating for the measurement and control latencies.

LARGE SCALE LANDSLIDE ON THE ONTONAGON RIVER, MICHIGAN

In 2003, a large landslide occurred along the Ontonagon River, located in the Upper Peninsula of Michigan, and adjacent to US-45 in Ontonagon County. The failure took place during the springtime, when the river reached a peak discharge that was the second highest on record. The volume of the slide has been estimated to be approximately 1,400,000 cubic yards. The colluvium blocked the river, forcing a new channel to be carved around the debris. The landslide consisted of a silt layer at its base, overlain by a coarsening upward sand sequence, and finally a varved glacio-lacustrine clay with sparse dropstone inclusions making up the upper section of hillside.

Computer Aided Design Algorithmic Technique for Very Large Scale Integrated Circuit Design and Smart Home Embedded System Design

To tackle the challenges at circuit level and system level VLSI and embedded system design, this dissertation proposes various novel algorithms to explore the efficient solutions. At the circuit level, a new reliability-driven minimum cost Steiner routing and layer assignment scheme is proposed, and the first transceiver insertion algorithmic framework for the optical interconnect is proposed. At the system level, a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems, which optimizes system energy consumption under stochastic fault occurrences, is proposed. The embedded system design is also widely used in the smart home area for improving health, wellbeing and quality of life. The proposed scheduling scheme for multiprocessor embedded systems is hence extended to handle the energy consumption scheduling issues for smart homes. The extended scheme can arrange the household appliances for operation to minimize monetary expense of a customer based on the time-varying pricing model.

Graphite exfoliation by supercritical carbon dioxide extraction

Supercritical carbon dioxide is used to exfoliate graphite, producing a small, several-layer graphitic flake. The supercritical conditions of 2000, 2500, and 3000 psi and temperatures of 40°, 50°, and 60°C, have been used to study the effect of critical density on the sizes and zeta potentials of the treated flakes. Photon Correlation Spectroscopy (PCS), Brunauer-Emmett-Teller (BET) surface area measurement, field emission scanning electron microscopy (FE-SEM), and atomic force microscopy (AFM) are used to observe the features of the flakes. N-methyl-2-pyrrolidinone (NMP), dimethylformamide (DMF), and isopropanol are used as co-solvents to enhance the supercritical carbon dioxide treatment. As a result, the PCS results show that the flakes obtained from high critical density treatment (low temperature and high pressure) are more stable due to more negative charges of zeta potential, but have smaller sizes than those from low critical density (high temperature and low pressure). However, when an additional 1-hour sonication is applied, the size of the flakes from low critical density treatment becomes smaller than those from high critical density treatment. This is probably due to more CO2 molecules stacked between the layers of the graphitic flakes. The zeta potentials of the sonicated samples were slightly more negative than nonsonicated samples. NMP and DMF co-solvents maintain stability and prevented reaggregation of the flakes better than isopropanol. The flakes tend to be larger and more stable as the treatment time increases since larger flat area of graphite is exfoliated. In these experiments, the temperature has more impact on the flakes than pressure. The BET surface area resultsshow that CO2 penetrates the graphite layers more than N2. Moreover, the negative surface area of the treated graphite indicates that the CO2 molecules may be adsorbed between the graphite layers during supercritical treatment. The FE-SEM and AFM images show that the flakes have various shapes and sizes. The effects of surfactants can be observed on the FE-SEM images of the samples in one percent by weight solution of SDBS in water since the sodium dodecylbenzene sulfonate (SDBS) residue covers all of the remaining flakes. The AFM images show that the vertical thickness of the graphitic flakes can ranges from several nanometers (less than ten layers thick), to more than a hundred nanometers. In conclusion, supercritical carbon dioxide treatment is a promising step compared to mechanical and chemical exfoliation techniques in the large scale production of thin graphitic flake, breaking down the graphite flakes into flakes only a fewer graphene layers thick.

THE USE OF LIFE-CYCLE ANALYSIS TO REDUCE THE ENVIRONMENTAL IMPACT OF MATERIALS IN MANUFACTURING

This thesis is composed of three life-cycle analysis (LCA) studies of manufacturing to determine cumulative energy demand (CED) and greenhouse gas emissions (GHG). The methods proposed could reduce the environmental impact by reducing the CED in three manufacturing processes. First, industrial symbiosis is proposed and a LCA is performed on both conventional 1 GW-scaled hydrogenated amorphous silicon (a-Si:H)-based single junction and a-Si:H/microcrystalline-Si:H tandem cell solar PV manufacturing plants and such plants coupled to silane recycling plants. Using a recycling process that results in a silane loss of only 17 versus 85 percent, this results in a CED savings of 81,700 GJ and 290,000 GJ per year for single and tandem junction plants, respectively. This recycling process reduces the cost of raw silane by 68 percent, or approximately $22.6 and $79 million per year for a single and tandem 1 GW PV production facility, respectively. The results show environmental benefits of silane recycling centered around a-Si:H-based PV manufacturing plants. Second, an open-source self-replicating rapid prototype or 3-D printer, the RepRap, has the potential to reduce the environmental impact of manufacturing of polymer-based products, using distributed manufacturing paradigm, which is further minimized by the use of PV and improvements in PV manufacturing. Using 3-D printers for manufacturing provides the ability to ultra-customize products and to change fill composition, which increases material efficiency. An LCA was performed on three polymer-based products to determine the CED and GHG from conventional large-scale production and are compared to experimental measurements on a RepRap producing identical products with ABS and PLA. The results of this LCA study indicate that the CED of manufacturing polymer products can possibly be reduced using distributed manufacturing with existing 3-D printers under 89% fill and reduced even further with a solar photovoltaic system. The results indicate that the ability of RepRaps to vary fill has the potential to diminish environmental impact on many products. Third, one additional way to improve the environmental performance of this distributed manufacturing system is to create the polymer filament feedstock for 3-D printers using post-consumer plastic bottles. An LCA was performed on the recycling of high density polyethylene (HDPE) using the RecycleBot. The results of the LCA showed that distributed recycling has a lower CED than the best-case scenario used for centralized recycling. If this process is applied to the HDPE currently recycled in the U.S., more than 100 million MJ of energy could be conserved per annum along with significant reductions in GHG. This presents a novel path to a future of distributed manufacturing suited for both the developed and developing world with reduced environmental impact. From improving manufacturing in the photovoltaic industry with the use of recycling to recycling and manufacturing plastic products within our own homes, each step reduces the impact on the environment. The three coupled projects presented here show a clear potential to reduce the environmental impact of manufacturing and other processes by implementing complimenting systems, which have environmental benefits of their own in order to achieve a compounding effect of reduced CED and GHG.