Numerical modeling of shear bands present several challenges, primarily due to strain softening, strong nonlinear multiphysics coupling, and steep solution gradients with fine solution features. In general it is not known a priori where a shear band will form or propagate, thus adaptive refinement is sometimes necessary to increase the resolution near the band. In this work we first explore the use of isogeometric analysis for shear band problems by constructing and testing several combinations of NURBS elements for a mixed finite element shear band formulation. Owing to the higher order continuity of the NURBS basis, fine solution features such as shear bands can be resolved accurately and efficiently without adaptive refinement. The results are compared to a mixed element formulation with linear functions for displacement and temperature and Pian–Sumihara shape functions for stress. We find that an element based on high order NURBS functions for displacement, temperature and stress, combined with gauss point sampling of the plastic strain leads to attractive results in terms of rate of convergence, accuracy and cpu time. This element is implemented with a Bbar strain projection method and is shown to be nearly locking free. Second we develop robust parallel preconditioners to GMRES in order to solve the Jacobian systems arising at each time step of the problem efficiently. The main idea is to design Schur complements tailored to the specific block structure of the system and that account for the varying stages of shear bands. We develop multipurpose preconditioners that apply to standard irreducible discretizations as well as our recent work on isogeometric discretizations of shear bands. The proposed preconditioners are tested on benchmark examples and compared to standard state of practice solvers such as GMRES/ILU and LU direct solvers. Nonlinear and linear iterations counts as well as CPU times and computational speedups are reported and it is shown that the proposed preconditioners are robust, efficient and outperform traditional state of the art solvers. Finally, we extend the preconditioners to further take advantage the physics of the problem. That is most of the deformation and plasticity is localized in a narrow band while out of this domain only small deformations and minor plasticity is observed. Hence, a preconditioner that decomposes the domain and concentrate more effort in the shear band domain while reusing information away from the band may lead to a significantly improved computational performance. To this end, we first propose a schur complement strategy which takes advantage of the gauss point history variables conveniently. Then, a general overlapping domain decomposition procedure is performed, partitioning the domain into so called 'shear band subdomain' and a 'healthy subdomain', which is used to precondition the Schur complement system. The shear band subdomain preconditioner is then solved exactly with an LU solver while the healthy subdomain preconditioner is only solved once in the elastic region and reused throughout the simulation. This localization awareness approach is shown to be very efficient and leads to an attractive solver for shear bands.

Significant research efforts have been directed towards exploring electrocatalysts for the selective reduction of CO2 to fuels such as methanol. Bocarsly et al (Princeton University) have recently reported the use of aromatic amines (e.g. pyridine (C5H5N)) as electrocatalysts in aqueous electrolytes for the reduction of CO2 at low overpotentials (50-150 mV). Importantly, the CO2-pyridine reduction process was claimed to selectively produce methanol with Faradaic efficiencies of ~100% on p-GaP electrode and 22-30% on Pt and Pd electrodes. Moreover, the initially proposed mechanism based on a radical intermediate interaction with CO2 as a key step toward the production of methanol was subsequently disproved. In this project, methanol formation by the CO2-pyridine (C5H5N) system was assessed by conducting electrolysis under various conditions at platinum electrodes. High pressure CO2 was used with the aim of increasing the methanol yield. In the course of the present study, the bulk electrolysis confirmed the methanol production at 1 bar and at 55bar of CO2 in the presence of pyridine. However, the methanol yield was found to be persistently limited to sub-ppm level (< 1ppm) under all conditions investigated. The observed methanol yield limitation could not be overcome by the electrode reactivation techniques used. Moreover, the methanol formation seemed unaffected by the current density or the biasing mode. This was an indication of the independence of methanol production from the charge transfer on the electrode. In agreement with these observations, analysis of the voltammetric data supported by simulation revealed that the CO2-pyridine reduction system is mainly pyridinium assisted molecular hydrogen production under all conditions investigated. In particular, protonated pyridine (C5H5N) ‘pyridinium’ was confirmed to behave as a weak acid on platinum. It was found that CO2 is merely a proton source of pyridine reprotonation via the hydration reaction followed by carbonic acid dissociation. The reprotonation reaction coupled to the electrode reaction ultimately leads to the dihydrogen production. No direct contribution of CO2 in the reduction process was observed. The production of methanol seems to occur chemically rather than directly driven by the charge transfer on the electrode. The role of pyridine (C5H5N) appears to be restricted to assisting in the generation of the hydrogen necessary for the alcohol production.

This thesis presents an empirical study comparing the ability of multi-static and bi-static, handheld, ground penetrating radar (GPR) systems, using full wave inversion (FWI), to determine the properties of buried anti-personnel (AP) landmines. A major problem associated with humanitarian demining is the occurrence of many false positives during clearance operations. Therefore, a reduction of the false alarm rate (FAR) and/or increasing the probability of detection (POD) is a key research and technical objective. Sensor fusion has emerged as a technique that promises to significantly enhance landmine detection. This study considers a handheld, combined metal detector (MD) and GPR device, and quantifies the advantages of the use of antenna arrays. During demining operations with such systems, possible targets are detected using the MD and further categorised using the GPR, possibly excluding false positives. A system using FWI imaging techniques to estimate the subsurface parameters is considered in this work. A previous study of multi-static GPR FWI used simplistic, 2D far-field propagation models, despite the targets being 3D and within the near field. This novel study uses full 3D electromagnetic (EM) wave simulation of the antenna arrays and propagation through the air and ground. Full EM simulation allows the sensitivity of radio measurements to landmine characteristics to be determined. The number and configuration of antenna elements are very important and must be optimised, contrary to the 2D sensitivity studies in (Watson, Lionheart 2014, Watson 2016) which conclude that the degree (number of elements) of the multi-static system is not critical. A novel sensitivity analysis for tilted handheld GPR antennas is used to demonstrate the positive impact of tilted antenna orientation on detection performance. A time domain GPR and A-scan data, consistent with a commercial handheld system, the MINEHOUND, is used throughout the simulated experiments which are based on synthetic GPR measurements. Finally, this thesis introduces a novel method of optimising the FWI solution through feature extraction or estimation of the internal air void typically present in pressure activated mines, to distinguish mines from non-mine targets and reduce the incidence of false positives.

Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types. In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types. To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity. In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments.

As radios in future wireless systems become more flexible and reconfigurable whilst available radio spectrum becomes scarce, the possibility of using TV White Space devices (WSD) as secondary users in the TV Broadcast Bands (without causing harmful interference to licensed incumbents) becomes ever more attractive. Cognitive Radio encompasses a number of technologies which enable adaptive self-programming of systems at different levels to provide more effective use of the increasingly congested radio spectrum. Cognitive Radio has the potential to use spectrum allocated to TV services, which is not actually being used by these services, without causing disruptive interference to licensed users by using channel selection aided by use of appropriate propagation modelling in TV White Spaces. The main purpose of this thesis is to explore the potential of the Cognitive Radio concept to provide additional bandwidth and improved efficiency to help accelerate the development and acceptance of Cognitive Radio technology. Specifically, firstly: three main classes of spectrum sensing techniques (Energy Detection, Matched Filtering and Cyclostationary Feature Detection) have compare in terms of time and spectrum resources consumed, required prior knowledge and complexity, ranking the three classes according to accuracy and performance. Secondly, investigate spectrum occupancy of the UHF TV band in the frequency range from 470 to 862 MHz by undertaking spectrum occupancy measurements in different locations around the Hull area in the UK, using two different receiver devices; a low cost Software-Defined Radio device and a laboratory-quality spectrum analyser. Thirdly, investigate the best propagation model among three propagation models (Extended-Hata, Davidson-Hata and Egli) for use in the TV band, whilst also finding the optimum terrain data resolution to use (1000, 100 or 30 m). it compares modelled results with the previously-mentioned practical measurements and then describe how such models can be integrated into a database-driven tool for Cognitive Radio channel selection within the TV White Space environment. Fourthly, create a flexible simulation system for creating a TV White Space database by using different propagation models. Finally, design a flexible system which uses a combination of Geolocation Database and Spectrum Sensing in the TV band, comparing the performance of two spectrum analysers (Agilent E4407B and Agilent EXA N9010A) with that of a low cost Software-Defined Radio in the real radio environment. The results shows that white space devices can be designed using SDRs based on the Realtek RTL2832U chip (RTL-SDR), combined with a geolocation database for identifying the primary user in the specific location in a cost-effective manner. Furthermore it is shown that improving the sensitivity of RTL-SDR will affect the accuracy and performance of the WSD.

abstract: Expansive clay soils, when subjected to substantial moisture change, can be extremely problematic causing various types of damage to lightly-loaded structures. Solving these problems requires an understanding of unsaturated soil mechanics. Soil suction, related to moisture content change, is important in the development of unsaturated soil properties and in the assessment of initial and final stress for heave computation. Direct measurement of soil suction on expansive clays to determine field suction profiles is quite limited due primarily to tradition and cost-driven geotechnical field investigation practices prioritizing water content measurement over soil suction measurement. This study employs a surrogate to estimate soil suction profiles for various sites consisting of clay soils with a Plasticity Index of greater than 15. The soil suction surrogate was used to determine suction profiles from existing geotechnical engineering expansive clay field investigations and a limited amount of directly measured suction profiles were also used. Equilibrium suction magnitudes and the depths to constant suction were obtained from the field suction profiles and results were compared to data found in the existing literature. Thornthwaite Moisture Index (TMI) is a climatic index to describe climatic conditions for a given region. Surface flux boundary conditions (i.e. covered and uncovered and irrigated and non-irrigated) were investigated and comparisons were made to the extent possible. Previous studies have presented correlations between TMI and equilibrium suction and TMI and depth to constant suction. Relationships within this study are presented and comparisons are made to existing relationships. Results and recommendations for further research are discussed. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2018

abstract: Across a wide variety of sports, our visual abilities have been proven to profoundly impact performance. Numerous studies have examined the effects of visual training in athletes and have found supporting evidence that performance can be enhanced through vision training. The present case study aimed to expand on research in the field of stroboscopic visual training. To do so, twelve softball players, half novice and half expert, took part in this study. Six underwent a four-week stroboscopic training program and six underwent a four-week non-stroboscopic training program. The quantitative data collected in this case study showed that training group (stroboscopic vs. non-stroboscopic) and skill level (novice vs expert) of each softball player were significant factors that contributed to how much their fielding performance increased. Qualitative data collected in this study support these findings as well as players’ subjective reports that their visual and perceptual skills had increased. Players trained in the stroboscopic group reported that they felt like they could “focus” on the ball better and “predict” where the ball would be. Future research should examine more participants across a longer training period and determine if more data would yield even greater significance for stroboscopic training. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2018

abstract: Nanolaminate materials are layered composites with layer thickness ≤ 100 nm. They exhibit unique properties due to their small length scale, the presence of a high number of interfaces and the effect of imposed constraint. This thesis focuses on the mechanical behavior of Al/SiC nanolaminates. The high strength of ceramics combined with the ductility of Al makes this combination desirable. Al/SiC nanolaminates were synthesized through magnetron sputtering and have an overall thickness of ~ 20 μm which limits the characterization techniques to microscale testing methods. A large amount of work has already been done towards evaluating their mechanical properties under indentation loading and micropillar compression. The effects of temperature, orientation and layer thickness have been well established. Al/SiC nanolaminates exhibited a flaw dependent deformation, anisotropy with respect to loading direction and strengthening due to imposed constraint. However, the mechanical behavior of nanolaminates under tension and fatigue loading has not yet been studied which is critical for obtaining a complete understanding of their deformation behavior. This thesis fills this gap and presents experiments which were conducted to gain an insight into the behavior of nanolaminates under tensile and cyclic loading. The effect of layer thickness, tension-compression asymmetry and effect of a wavy microstructure on mechanical response have been presented. Further, results on in situ micropillar compression using lab-based X-ray microscope through novel experimental design are also presented. This was the first time when a resolution of 50 nms was achieved during in situ micropillar compression in a lab-based setup. Pores present in the microstructure were characterized in 3D and sites of damage initiation were correlated with the channel of pores present in the microstructure. The understanding of these deformation mechanisms paved way for the development of co-sputtered Al/SiC composites. For these composites, Al and SiC were sputtered together in a layer. The effect of change in the atomic fraction of SiC on the microstructure and mechanical properties were evaluated. Extensive microstructural characterization was performed at the nanoscale level and Al nanocrystalline aggregates were observed dispersed in an amorphous matrix. The modulus and hardness of co- sputtered composites were much higher than their traditional counterparts owing to denser atomic packing and the absence of synthesis induced defects such as pores and columnar boundaries. / Dissertation/Thesis / Doctoral Dissertation Materials Science and Engineering 2018

Observing biophysical phenomena at the nanometer scale with both high spatial and temporal resolution is a challenging feat. Although many techniques, including atomic force microscopy and scanning electron microscopy, have demonstrated subnanometer spatial resolution, most exhibit drawbacks which limit their temporal resolution. On the other hand, light microscopy exhibits poor spatial resolution (typically greater than 100 nm) due to diffraction. The desire to image features below the resolution of light has spawned the term super-resolution microscopy to which many powerful, albeit complicated, techniques may be associated; such techniques include Stimulated Emission-Depletion Microscopy (STED) and Stochastic Optical Reconstruction Microscopy (STORM). Within the field of super-resolution there exists a subset of methods which involve tagging features of interest (e.g., a virus or motor protein) with small, fluorescent molecules and measuring their emitted fluorescence over time. Although the emitted light is diffraction-limited, the precision of localizing the position of the molecule is proportional to the number of photons acquired. Thus, fluorescent particle tracking is a method which augments traditional light microscopy so that features may be localized to spatial resolutions below the diffraction limit while still maintaining useful temporal resolutions. One common approach for tracking fluorescent particles involves passively observing the particle with a stationary detector; this approach, however, is limited by its inability to observe particles in three dimensions over a large field of view. Consequently, specialized techniques have been developed that actively track the particle, but the majority of these methods, unfortunately, utilize non-standard optical paths which complicate their use. Moreover, analysis methods pertaining to both paradigms, which infer both position locations and model-based parameter estimates, are often subjective or employ simplified and potentially inaccurate models. In widefield microscopy, for example, the common approach involves first localizing the particle within each image via a heuristic method, such as calculating the centroid, and then inferring diffusion coefficients by regressing to the mean squared displacement. This approach to localization disregards information involving the optical setup (e.g., the point spread function, aberrations, and noise) as well as information regarding the particle's motion. Although methods exist for optimally calculating diffusion coefficients, they are limited to the case of unconfined diffusion with measurements corrupted by additive, white noise. The work in this thesis provides two specific contributions. The first presents an active approach to tracking a single fluorescent particle in three dimensions that requires no specialized hardware aside from a standard confocal microscope. Inspired by works involving the autonomous exploration of unknown potential fields, the algorithm operates by moving the microscope's focal volume toward the maximum of the field of light emitted by the particle. For a stationary particle and a radial field, an equilibrium trajectory is derived and its local stability is proven. In addition, the algorithm's ability to track both stationary and diffusive particles is numerically characterized. The second contribution presents the application of a numerical, iterative algorithm to the problem of simultaneously inferring both location and model parameters from particle tracking data of potentially nonlinear and non-Gaussian imaging modalities. The method, which is leveraged from the field of system identification, employs Sequential Monte Carlo methods in conjunction with the Expectation Maximization algorithm to provide approximate maximum likelihood estimates of model parameters (e.g., diffusion coefficients) as well as approximate posterior probability densities of the particle's location over time. The effectiveness of the method is demonstrated through numerical simulations of two- and three-dimensional motion (including free, confined, and tethered diffusion) imaged in a widefield context. Lastly, the effectiveness of both methods is demonstrated by tracking a quantum dot in a hydrogel with the proposed tracking method and by analyzing the resulting data using the aforementioned inference method.

abstract: Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019