Tropical and extratropical Pacific decadal climate variability substantially impact physical and biological systems in the Pacific Ocean and strongly influence global climate through teleconnection patterns. Current understanding of Pacific decadal climate variability centers around the El Niño-Southern Oscillation (ENSO), the Aleutian Low (AL), and the Pacific Decadal Oscillation (PDO). However, recent literature has highlighted the emerging roles of secondary modes of variability of the tropical and extratropical Pacific atmosphere and ocean in global climate change: the Central Pacific Warming (CPW) phenomenon, the North Pacific Oscillation (NPO), and the North Pacific Gyre Oscillation (NPGO). This work analyzes the statistics and uncertainties behind Pacific interannual and decadal-scale climate variability, and focuses on better understanding the roles of the CPW, NPO, and NPGO in the climate system. The study begins by examining the dynamics of the NPO and its role in Pacific interannual and decadal climate variability. Results illustrate that the individual poles of the NPO have relations at high frequencies, but only the southern node contains a deterministic low-frequency component, which is forced by tropical Pacific sea surface temperature (SST) variability, as shown with a modeling experiment. The NPO-induced variability by the tropical Pacific SST is then integrated by the underlying ocean surface to form the decadal-scale NPGO signal. Thus, a new link between the CPW, the NPO, and the NPGO is formed, expanding the current framework of Pacific decadal variability and its implications for weather and climate. The new framework of North Pacific decadal variability (NPDV) is then evaluated in 24 state-of-the-art coupled climate models. Results indicate that the models in general have difficulty reproducing the leading modes of NPDV in space and time, particularly the NPGO mode and its connection to the NPO. Furthermore, most models lack the proper connections between extratropical and tropical Pacific, for both the ENSO/AL/PDO and CPW/NPO/NPGO connections. Improvements in these teleconnections are thus needed to increase confidence in future climate projections. The last part of the dissertation explores further the importance of the CPW mode by comparing and contrasting two popular paleoclimate SST anomaly reconstruction methods used for tropical Indo-Pacific SSTs. The first method exploits the high correlation between the canonical ENSO mode and tropical precipitation; the second method uses a multi-regression model that exploits the multiple modes of covariability between tropical precipitation and SSTs, including the CPW mode. The multi-regression approach demonstrates higher skill throughout the tropical Indo-Pacific than the first approach, illustrating the importance of including the CPW phenomenon in understanding past climates.

Tyrosyl radicals can facilitate proton-coupled electron transfer (PCET) reactions that are linked to catalysis in many biological systems. One such protein system is ribonucleotide reductase (RNR). This enzyme is responsible for the conversion of ribonucleotides to deoxyribonucleotides. The beta2 subunit of class Ia RNRs contains a diiron cluster and a stable tyrosyl radical (Y122*). Reduction of ribonucleotides is dependent on reversible, long-distance PCET reactions involving Y122* located 35 Å from the active site. Protein conformational dynamics are postulated to precede diiron cluster assembly and PCET reactions in RNR. Using UV resonance Raman spectroscopy, we identified structural changes to histidine, tyrosine, and tryptophan residues with metal cluster assembly in beta2. With a reaction-induced infrared spectroscopic technique, local amide bond structural changes, which are associated with the reduction of Y122*, were observed. Moreover, infrared spectroscopy of tyrosine-containing pentapeptide model compounds supported the hypothesis that local amide bonds are perturbed with tyrosyl radical formation. These findings demonstrate the importance of the amino acid primary sequence and amide bonds on tyrosyl radical redox changes. We also investigated the function of a unique tyrosine-histidine cross-link, which is found in the active site of cytochrome c oxidase (CcO). Spectrophotometric titrations of model compounds that mimic the cross-link were consistent with a proton transfer role in CcO. Infrared spectroscopic data support the formation of tyrosyl radicals in these model compounds. Collectively, the effect of the local structure and the corresponding protein dynamics involved in tyrosyl radical-mediated PCET reactions are illustrated in this work.

This thesis aims at the methodological developments in Nuclear Magnetic Resonance (NMR). The methodological developments in NMR has a long and successful history. The present thesis attempts to contribute some novel methods in this direction. This thesis restricts itself to two methodological developments, namely, (1) effects of cross-correlations between the chemical shift anisotropy (CSA) and dipole-dipole interactions in the relaxation of various nuclei and experiments which utilize spatial encoding. The cross-correlation has been successfully utilized to investigate the anisotropic motions of liquid crystals, and to understand the chemical shift anisotropy of fluorine atoms of Fluorine substituted ring compounds. Spatial encoding schemes have been developed to facilitate single scan measurements of longitudinal spin lattice relaxation times and implementations of parallel search algorithms.

Fiber reinforced composite materials are widely used in aerospace, mechanical, civil and other industries because of their high strength-to-weight and stiffness-to-weight ratios. However, composite structures are highly prone to impact damage. Possible types of defect or damage in composite include matrix cracking, fiber breakage, and delamination between plies. In addition, delamination in a laminated composite is usually invisible. It is very diffcult to detect it while the component is in service and this will eventually lead to catastrophic failure of the structure. Such damages may be caused by dropped tools and ground handling equipments. Damage in a composite structure normally starts as a tiny speckle and gradually grows with the increase in load to some degree. However, when such damage reaches a threshold level, serious accident can occur. Hence, it is important to have up-to-date information on the integrity of the structure to ensure the safety and reliability of composite components, which require frequent inspections to identify and quantify damage that might have occurred even during manufacturing, transportation or storage. How to identify a damage using the obtained information from a damaged composite structure is one of the most pivotal research objectives. Various forms of structural damage cause variations in structural mechanical characteristics, and this property is extensively employed for damage detection. Existing traditional non-destructive inspection techniques utilize a variety of methods such as acoustic emission, C-scan, thermography, shearography and Moir interferometry etc. Each of these techniques is limited in accuracy and applicability. Most of these methods require access to the structure.They also require a significant amount of equipment and expertise to perform inspection. The inspections are typically based on a schedule rather than based on the condition of the structure. Furthermore, the cost associated with these traditional non-destructive techniques can be rather prohibitive. Therefore, there is a need to develop a cost-effective, in-service, diagnostic system for monitoring structural integrity in composite structures. Structural health monitoring techniques based on dynamic response is being used for several years. Changes in lower natural frequencies and mode shapes with their special derivatives or stiffness/ exibility calculation from the measured displacement mode shapes are the most common parameters used in identification of damage. But the sensitivity of these parameters for incipient damage is not satisfactory. On the other hand, for in service structural health monitoring, direct use of structural response histories are more suitable. However, they are very few works reported in the literature on these aspects, especially for composite structures, where higher order modes are the ones that get normally excited due to the presence of flaws. Due to the absence of suitable direct procedure, damage identification from response histories needs inverse mapping; like artificial neural network. But, the main diffculty in such mapping using whole response histories is its high dimensionality. Different general purpose dimension reduction procedures; like principle component analysis or indepen- dent component analysis are available in the literature. As these dimensionally reduced spaces may loose the output uniqueness, which is an essential requirement for neural network mapping, suitable algorithms for extraction of damage signature from these re- sponse histories are not available. Alternatively, fusion of trained networks for different partitioning of the damage space or different number of dimension reduction technique, can overcome this issue efficiently. In addition, coordination of different networks trained with different partitioning for training and testing samples, training algorithms, initial conditions, learning and momentum rates, architectures and sequence of training etc., are some of the factors that improves the mapping efficiency of the networks. The applications of smart materials have drawn much attention in aerospace, civil, mechanical and even bioengineering. The emerging field of smart composite structures offers the promise of truly integrated health and usage monitoring, where a structure can sense and adapt to their environment, loading conditions and operational requirements, and materials can self-repair when damaged. The concept of structural health monitoring using smart materials relies on a network of sensors and actuators integrated with the structure. This area shows great promise as it will be possible to monitor the structural condition of a structure, throughout its service lifetime. Integrating intelligence into the structures using such networks is an interesting field of research in recent years. Some materials that are being used for this purpose include piezoelectric, magnetostrictive and fiber-optic sensors. Structural health monitoring using, piezoelectric or fiber-optic sensors are available in the literature. However, very few works have been reported in the literature on the use of magnetostrictive materials, especially for composite structures. Non contact sensing and actuation with high coupling factor, along with other prop- erties such as large bandwidth and less voltage requirement, make magnetostrictive materials increasingly popular as potential candidates for sensors and actuators in structural health monitoring. Constitutive relationships of magnetostrictive material are represented through two equations, one for actuation and other for sensing, both of which are coupled through magneto-mechanical coefficient. In existing finite element formulation, both the equations are decoupled assuming magnetic field as proportional to the applied current. This assumption neglects the stiffness contribution coming from the coupling between mechanical and magnetic domains, which can cause the response to deviate from the time response. In addition, due to different fabrication and curing difficulties, the actual properties of this material such as magneto-mechanical coupling coefficient or elastic modulus, may differ from results measured at laboratory conditions. Hence, identification of the material properties of these embedded sensor and actuator are essential at their in-situ condition. Although, finite element method still remains most versatile, accurate and generally applicable technique for numerical analysis, the method is computationally expensive for wave propagation analysis of large structures. This is because for accurate prediction, the finite element size should be of the order of the wavelength, which is very small due to high frequency loading. Even in health monitoring studies, when the flaw sizes are very small (of the order of few hundred microns), only higher order modes will get affected. This essentially leads to wave propagation problem. The requirement of cost-effective computation of wave propagation brings us to the necessity of spectral finite element method, which is suitable for the study of wave propagation problems. By virtue of its domain transfer formulation, it bypasses the large system size of finite element method. Further, inverse problem such as force identification problem can be performed most conveniently and efficiently, compared to any other existing methods. In addition, spectral element approach helps us to perform force identification directly from the response histories measured in the sensor. The spectral finite element is used widely for both elementary and higher order one or two dimensional waveguides. Higher order waveguides, normally gives a behavior, where a damping mode (evanescent) will start propagating beyond a certain frequency called the cut-off frequency. Hence, when the loading frequencies are much beyond their corresponding cut-off frequencies, higher order mo des start propagating along the structure and should be considered in the analysis of wave propagations. Based on these considerations, three main goals are identified to be pursued in this thesis. The first is to develop the constitutive relationship for magnetostrictive sensor and actuator suitable for structural analysis. The second is the development of different numerical tools for the modelling the damages. The third is the application of these developed elements towards solving inverse problems such as, material property identification, impact force identification, detection and identification of delamination in composite structure. The thesis consists of four parts spread over six chapters. In the first part, linear, nonlinear, coupled and uncoupled constitutive relationships of magnetostrictive materials are studied and the elastic modulus and magnetostrictive constant are evaluated from the experimental results reported in the literature. In uncoupled model, magnetic field for actuator is considered as coil constant times coil current. The coupled model is studied without assuming any explicit direct relationship with magnetic field. In linear coupled model, the elastic modulus, the permeability and magnetostrictive coupling are assumed as constant. In nonlinear-coupled model, the nonlinearity is decoupled and solved separately for the magnetic domain and mechanical domain using two nonlinear curves,’ namely the stress vs. strain curve and magnetic flux density vs. magnetic field curve. This is done by two different methods. In the first, the magnetic flux density is computed iteratively, while in the second, artificial neural network is used, where a trained network gives the necessary strain and magnetic flux density for a given magnetic field and stress level. In the second part, different finite element formulations for composite structures with embedded magnetostrictive patches, which can act both as sensors and actuators, is studied. Both mechanical and magnetic degrees of freedoms are considered in the formulation. One, two and three-dimensional finite element formulations for both coupled and uncoupled analysis is developed. These developed elements are then used to identify the errors in the overall response of the structure due to uncoupled assumption of the magnetostrictive patches and shown that this error is comparable with the sensitivity of the response due to different damage scenarios. These studies clearly bring out the requirement of coupled analysis for structural health monitoring when magnetostrictive sensor and actuator are used. For the specific cases of beam elements, super convergent finite element formulation for composite beam with embedded magnetostrictive patches is introduced for their specific advantages in having superior convergence and in addition, these elements are free from shear locking. A refined 2-node beam element is derived based on classical and first order shear deformation theory for axial-flexural-shear coupled deformation in asymmetrically stacked laminated composite beams with magnetostrictive patches. The element has an exact shape function matrix, which is derived by exactly solving the static part of the governing equations of motion, where a general ply stacking is considered. This makes the element super convergent for static analysis. The formulated consistent mass matrix, however, is approximate. Since the stiffness is exactly represented, the formulated element predicts natural frequency to greater level of accuracy with smaller discretization compared to other conventional finite elements. Finally, these elements are used for material property identification in conjunction with artificial neural network. In the third part, frequency domain analysis is performed using spectrally formulated beam elements. The formulated elements consider deformation due to both shear and lateral contraction, and numerical experiments are performed to highlight the higher order effects, especially at high frequencies. Spectral element is developed for modelling wave propagation in composite laminate in the presence of magnetostrictive patches. The element, by virtue of its frequency domain formulation, can analyze very large domain with nominal cost of computation and is suitable for studying wave propagation through composite materials. Further more, identification of impact force is performed form the magnetostrictive sensor response histories using these spectral elements. In the last part, different numerical examples for structural health monitoring are directed towards studying the responses due to the presence of the delamination in the structure; and the identification of the delamination from these responses using artificial neural network. Neural network is applied to get structural damage status from the finite element response using its mapping feature, which requires output uniqueness. To overcome the loss of output uniqueness due to the dimension reduction, damage space is divided into different overlapped zones and then different networks are trained for these zones. Committee machine is used to co ordinate among these networks. Next, a five-stage hierarchy of networks is used to consider partitioning of damage space, where different dimension reduction algorithms and different partitioning between training and testing samples are used for better mapping fro the identification procedure. The results of delamination detection for composite laminate show that the method developed in this thesis can be applied to structural damage detection and health monitoring for various industrial structures. This thesis collectively addresses all aspects pertaining to the solution of inverse problem and specially the health monitoring of composite structures using magnetostric tive sensor and actuator. In addition, the thesis discusses the necessity of higher order theory in the high frequency analysis of wavw propagation. The thesis ends with brief summary of the tasks accomplished, significant contribution made to the literature and the future applications where the proposed methods addressed in this thesis can be applied.

As computational grids have become popular and ubiquitous, users have access to large number and different types of geographically distributed grid resources. Many computational grid frameworks are composed of multiple distributed sites with each site consisting of one or more dedicated or non-dedicated clusters. Jobs submitted to a grid are handled by a matascheduler which interacts with the local schedulers of the clusters for scheduling jobs to the individual clusters. Computational grids have been found to be powerful research-beds for execution of various kinds of parallel applications. When a parallel application is submitted to a grid, the metascheduler has to choose a set of resources from a cluster for application execution. To select the best set of resources for application execution, it is important to determine the performance of the application. Accurate performance estimates of an application is essential in assisting a grid meta scheduler to efficiently schedule user jobs. Thus models that predict execution times of parallel applications on a set of resources and a search procedure (scheduling strategy) which selects the best set of machines within a cluster for application execution are of importance for enabling the parallel applications on grids. For efficient execution of large scientific parallel applications consisting of multiple phases, performance models of the individual phases should be obtained. Efficient rescheduling strategies that can use the per-phase models to adapt the parallel applications to application and resource dynamics are necessary for maintaining high performance of the applications on grids. A practical and robust grid computing infrastructure that integrates components related to application and resource monitoring, performance modeling, scheduling and rescheduling techniques, is highly essential for large-scale deployment and high performance of scientific applications on grid systems and hence for fostering high performance computing. This thesis focuses on developing performance models for predicting execution times of parallel problems/subproblems on dedicated and non-dedicated grid resources. The thesis also constructs robust scheduling and rescheduling strategies in a grid metascheduler that can use the performance models for efficient execution of large scientific parallel applications on dynamic grids. Finally, the thesis builds a practical and robust grid middleware infrastructure which integrates components related to performance modeling, scheduling and rescheduling, monitoring and migration frameworks for large-scale deployment and use of high performance applications on grids. The thesis consists of four main components. In the first part of the thesis, we have developed a comprehensive set of performance modeling strategies to predict the execution times of tightly-coupled parallel applications on a set of resources in a dedicated or non-dedicated cluster. The main purpose of our prediction strategies is to aid grid metaschedulers in making scheduling decisions. Our performance modeling strategies, based on linear regression, can deal with non-dedicated systems where the loads can change during application executions. Our models do not require detailed knowledge and instrumentation of the applications and can be constructed without the involvement of application developers. The strategies are intended for rapid and large scale deployment of parallel applications on non-dedicated grid systems. We have evaluated our strategies on 8, 16, 24 and 32-node clusters with random loads and load traces from a grid system. Our performance modeling strategies gave less than 30% average percentage prediction errors in all cases, which is reasonable for non-dedicated systems. We also found that scheduling based on the predictions by our strategies will result in perfect scheduling in many cases. For modeling large-scale scientific applications, we use execution profiles and automatic program analysis, and manual analysis of significant portions of the application’s code to identify the different phases of applications. We then adopt our performance modeling strategies to predict execution times for the different phases of the tightly-coupled parallel applications on a set of resources in a dedicated or non-dedicated cluster. Our experiments show that using combinations of performance models of the phases give 18% – 70% more accurate predictions than using single performance models for the applications. In the second part of the thesis, we have devised, evaluated and compared algorithms for scheduling tightly-coupled parallel applications on multi-cluster grids. Our algorithms use performance models that predict the execution times of parallel applications, for evaluations of candidate schedules. In this work, we propose a novel algorithm called Box Elimination (BE) that searches a space of performance model parameters to determine efficient schedules. By eliminating large search space regions containing poorer solutions at each step and searching high quality solutions, our algorithm is able to generate efficient schedules within few seconds for even clusters of 512 processors. By means of large number of real and simulation experiment, we compared our algorithm with popular optimization techniques. We show that our algorithm generates up to 80% more efficient schedules than other algorithms and the resulting execution times are more robust against performance modeling errors. The third part of the thesis deals with policies for rescheduling long-running multi-phase parallel applications in response to application and resource dynamics. In this work, we use our performance modeling and scheduling strategies to derive rescheduling plans for executing multi-phase parallel applications on grids. A rescheduling plan consists of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. We have developed three algorithms, namely an incremental algorithm, a divide-and-conquer algorithm and a genetic algorithm, for deriving a rescheduling plan for a parallel application execution. We have also developed an algorithm that uses rescheduling plans derived on different clusters to form a single coherent rescheduling plan for application execution on a grid consisting of multiple clusters. The rescheduling plans generated by our algorithms are highly efficient leading to application execution times that are higher than the execution times corresponding to brute force method by less than 10%. We also find that rescheduling in response to changing application and resource dynamics, using the rescheduling plans for multi-cluster grids generated by our algorithms, give much lesser execution times when compared to executions of the applications on a single schedule throughout application execution. In the final part of the thesis, we have developed a practical grid middleware framework called MerITA (Middleware for Performance Improvement of Tightly Coupled Parallel Applications on Grids), a system for effective execution of tightly-coupled parallel applications on multi-cluster grids consisting of dedicated or non-dedicated, interactive or batch systems. The framework brings together performance modeling for automatically determining the characteristics of parallel applications, scheduling strategies that use the performance models for efficient mapping of applications to resources, rescheduling policies for determining the points in application execution when executing applications can be rescheduled to different sets of resources to obtain performance improvement and a check-pointing library for enabling rescheduling.

The magnetization processes of antiferromagnetically (AF) coupled Co/Pt multilayers on extended substrates and of Co/Pd multilayers deposited on arrays of 58 nm spheres are investigated via magnetic force microscopy at room temperature by imaging the domain configuration in magnetic fields. Adding AF exchange to such perpendicular anisotropy systems changes the typical energy balance that controls magnetic band domain formation, thus resulting in two competing reversal modes for the system. In the ferromagnetic (FM) dominated regime the magnetization forms FM band domains, vertically correlated. By applying a magnetic field, a transition from band to bubble domains is observed. In the AF-exchange dominated regime, by applying a field or varying the temperature it is possible to alter the magnetic correlation from horizontal (AF state) to vertical (FM state) via the formation of specific multidomain states, called metamagnetic domains. A theoretical model, developed for complex multilayers is applied to the experimentally studied multilayer architecture, showing a good agreement. Magnetic nanoparticles have attracted considerable interest in recent years due to possible applications in high density data storage technology. Requirements are a well defined and localized magnetic switching behavior and a large thermal stability in zero fields. The thermal stability of [Co/Pt]N multilayers with different numbers of repeats (N), deposited on nanospheres is studied by magnetic viscosity measurements. The magnetic activation volume, representing the effect of thermal activation on the switching process, is estimated. It is found that the activation volume is much smaller than the volume of the nanosphere and almost independent of the number of bilayers supporting an inhomogeneous magnetization reversal process.

<p>In this thesis, fractured rock masses are treated asequivalent continua for large-scale analyses of rockengineering projects. Systematic developments are made for thedetermination of equivalent mechanical and hydraulic propertiesof fractured rock masses using a hybrid discrete fracturenetwork - distinct element method (DFN-DEM) approach. Thedetermined equivalent properties are then used for a far-fieldfinite element analysis of the thermo-mechanical impacts on thestress, deformation and permeability of fractured rockssurrounding a hypothetical geological repository of nuclearwaste. The geological data were extracted from the results ofan extensive site investigation programme at Sellafield, UK,conducted by Nirex UK Ltd.</p><p>The scale dependencies of the hydraulic and mechanicalproperties were investigated by using multiple realizations ofthe fracture system geometry with increasing model sizes untilproperly defined hydraulic and mechanical representativeelementary volumes (REVs) were reached. The validity of thesecond order permeability tensor and the fourth-ordermechanical compliance tensor were tested for continuum analysesat larger scales. The REV was determined to be around 5 m formechanical and hydraulic data in this study.</p><p>Analysis of the stress-dependent mechanical and hydraulicproperties shows that the effect of rock stresses is crucial.The elastic moduli increase significantly with the increase ofstress and an empirical equation of stress-dependent elasticmodulus is suggested based on results of numerical experiments.Calculations of the Poisson's ratios suggest greater valuesthan are normally assumed in practice. Depending on the stateof stress, permeability decreases or increases with increasingcompressive stress. Stress-induced flow channeling effect iscaptured by numerical modeling for the first time and detailedmechanisms of shear dilation of fractures are provided. Basedon the numerical experiments, a set of empirical equations wassuggested for the stress-dependent permeability, consideringboth normal deformation and shear dilation of fractures.</p><p>Thermo-mechanical impact on the performance of ahypothetical repository at a far-field scale (5 km by 1 km) wasinvestigated with the stress-dependent equivalent propertiesdetermined at the REV scale. This analysis shows thatmechanical responses vary significantly depending on how themechanical properties were determined. The change ofpermeability due to the thermal loading is, however, notsignificant in this particular case.</p><p>The thesis provides a framework for systematic analysis oflarge-scale engineering applications in fractured rock masses,such as geological repositories of nuclear wastes.</p><p><b>Keyword:</b>Fractured rock masses, Equivalent Continuum,Discrete Fracture Network (DFN), Distinct Element Method (DEM),Finite Element Method (FEM), Nuclear Waste Disposal, CoupledThermo-Hydro-Mechanical Processes</p>

The human sense of hearing is characterized by its exquisite sensitivity, sharp frequency selectivity, and wide dynamic range. These features depend on an active process that in the inner ear boosts vibrations evoked by auditory stimuli. Spontaneous otoacoustic emissions constitute a demonstrative manifestation of this physiologically vulnerable mechanism. In the cochlea, sensory hair bundles transduce sound-induced vibrations into neural signals. Hair bundles can power mechanical movements of their tip, oscillate spontaneously, and operate as tuned nonlinear amplifiers of weak periodic stimuli. Active hair-bundle motility constitutes a promising candidate with respect to the biophysical implementation of the active process underlying human hearing. The responsiveness of isolated hair bundles, however, is seriously hampered by intrinsic fluctuations. In this thesis, we present theoretical and experimental results concerning the noise-imposed limitations of nonlinear amplification by active sensory hair bundles. We analyze the effect of noise within the framework of a stochastic description of hair-bundle dynamics and relate our findings to generic aspects of the stochastic dynamics of oscillatory systems. Hair bundles in vivo are often elastically coupled by overlying gelatinous membranes. In addition to theoretical results concerning the dynamics of elastically coupled hair bundles, we report on an experimental study. We have interfaced dynamic force clamp performed on a hair bundle from the sacculus of the bullfrog with real-time stochastic simulations of hair-bundle dynamics. By means of this setup, we could couple a hair bundle to two virtual neighbors, called cyber clones. Our theoretical and experimental work shows that elastic coupling leads to an effective noise reduction. Coupled hair bundles exhibit an increased coherence of spontaneous oscillations and an enhanced amplification gain. We therefore argue that elastic coupling by overlying membranes constitutes a morphological specialization for reducing the detrimental effect of intrinsic fluctuations.

The growth of flexible electronics industry has given rise to light-weight, flexible devices which have a wide range of applications such as wearable electronics, flexible sensors, conformal antennas, bio-medical applications, solar cells etc. Though several techniques exist to fabricate flexible devices, the limiting factors have been durability, cost and complexity of the approach. In this research, the focus has been on developing stretchable (flexible) conductors using a multi-layer structure of metal and conductive rubber. The stretchable conductors developed using this approach do not lose electrical connection when subjected to large strains up to 25%. Also, the conductivity of the conductive rubber has been improved by ~20 times using the multi-layer approach. Furthermore, the multi-layer approach was used to fabricate devices for RF and antenna applications. A flexible micro-stripline was fabricated using the multi-layer approach to study the performance at microwave frequencies up to 5 GHz. It was observed that using an optimal metal and conductive rubber layer structure can help to reduce the loss of the device by 58% and also the device does not get damaged due to bending. In addition to this, an aperture-coupled patch antenna at 3.1 GHz was fabricated using the multi-layer approach to demonstrate reconfigurability. Ideally, the multi-layer patch antennas can be stretched up to 25% which helps to tune the resonance frequency from 3.1 GHz to 2.5 GHz. The multi-layer patch antennas were tested up to ~10% strains to study their radiation properties. It was demonstrated that using an ideal multi-layer structure of metal and conductive rubber layer can help to improve the antenna's peak gain by 3.3 dBi compared to a conductive rubber based antenna.

Coupled systems are ubiquitous in modern engineering and science. Such systems can encompass fluid dynamics, structural mechanics, chemical species transport and electrostatic effects among other components, all of which can be coupled in many different ways. In addition, such models are usually multiscale, making their numerical simulation challenging, and necessitating the use of adaptive modeling techniques. The multiscale, multiphysics models of electrosomotic flow (EOF) constitute a particularly challenging coupled flow system. A special feature of such models is that the coupling between the electric physics and hydrodynamics is via the boundary. Numerical simulations of coupled systems are typically targeted towards specific Quantities of Interest (QoIs). Adjoint-based approaches offer the possibility of QoI targeted adaptive mesh refinement and efficient parameter sensitivity analysis. The formulation of appropriate adjoint problems for EOF models is particularly challenging, due to the coupling of physics via the boundary as opposed to the interior of the domain. The well-posedness of the adjoint problem for such models is also non-trivial. One contribution of this dissertation is the derivation of an appropriate adjoint problem for slip EOF models, and the development of penalty-based, adjoint-consistent variational formulations of these models. We demonstrate the use of these formulations in the simulation of EOF flows in straight and T-shaped microchannels, in conjunction with goal-oriented mesh refinement and adjoint sensitivity analysis. Complex computational models may exhibit uncertain behavior due to various reasons, ranging from uncertainty in experimentally measured model parameters to imperfections in device geometry. The last decade has seen a growing interest in the field of Uncertainty Quantification (UQ), which seeks to determine the effect of input uncertainties on the system QoIs. Monte Carlo methods remain a popular computational approach for UQ due to their ease of use and "embarassingly parallel" nature. However, a major drawback of such methods is their slow convergence rate. The second contribution of this work is the introduction of a new Monte Carlo method which utilizes local sensitivity information to build accurate surrogate models. This new method, called the Local Sensitivity Derivative Enhanced Monte Carlo (LSDEMC) method can converge at a faster rate than plain Monte Carlo, especially for problems with a low to moderate number of uncertain parameters. Adjoint-based sensitivity analysis methods enable the computation of sensitivity derivatives at virtually no extra cost after the forward solve. Thus, the LSDEMC method, in conjuction with adjoint sensitivity derivative techniques can offer a robust and efficient alternative for UQ of complex systems. The efficiency of Monte Carlo methods can be further enhanced by using stratified sampling schemes such as Latin Hypercube Sampling (LHS). However, the non-incremental nature of LHS has been identified as one of the main obstacles in its application to certain classes of complex physical systems. Current incremental LHS strategies restrict the user to at least doubling the size of an existing LHS set to retain the convergence properties of LHS. The third contribution of this research is the development of a new Hierachical LHS algorithm, that creates designs which can be used to perform LHS studies in a more flexibly incremental setting, taking a step towards adaptive LHS methods. / text