Having a net dipole moment, water molecules tend to align with an external electric field. The re-orientation of water molecules to align with the field direction can result in structural and dynamic changes in liquid water. Studying these changes can help us to understand the role of an E-field in many biological systems, chemical reactions, and many technological advancements. In short, the application of static electric fields causes molecules to stay aligned with the field, so, fewer hydrogen bonds break, and molecules have slower dynamics. This type of field can be used when the mobility of water molecules needs to be reduced, like in electroporation. Alternating electric fields, on the other hand, cause continuous re-orientation of dipole moments, which results in more H bond breaking, water is less structured, and molecules have faster motion. Water under static and alternating electric fields have several applications in science and technology. Although many of the interesting usages of the application of electric fields to water happen at surfaces, the response of hydrogen bonding of water molecules to an E-field is still not fully understood even in bulk. For instance, the rate of hydrogen bond breaking, the re-orientation of water molecules, and the random walk of water molecules under the restrictions of the static electric field have not been thoroughly assessed. The static electric field limits the re-orientation of water molecules, but the translation reduces at the same time, this is clear evidence of roto-translational coupling, and the static electric field is a great groundwork for studying this coupling which is generated by the hydrogen bonds. For studying the effects of an E-field on H-bonding dynamics in depth, we need a model of hydrogen bonding. There are a few models for dynamics of H-bonding and reorientation of water molecules, including Luzar and Chandler model, published in 1996, and the Laage and Hynes jump model, published in 2006, which are described in the introduction chapter. The two models are related but have different perspectives, so it would be very interesting to look for a more general framework of hydrogen bonding by combining these two models, with the help of the influence of external electric fields. We also explain the relation of the random walk diffusion of water molecules and the hydrogen bonding. Since the external electric field can change the dipole moment of water molecules, for a more realistic picture, we need do the simulations with sophisticated polarizable water models to obtain a better estimate of the behavior of experimental water in an electric field. In this thesis, we introduce our generalized hydrogen bond framework; then we assess this framework, as well as other static and dynamic properties of water under static and alternating electric fields.

Two reaction classes have been studied computationally including the pyrolysis of various components of airplane fuels, such as decane, dodecane, butylbenzene isomers, and JP-10 (exo-tetrahydrodicyclopentadiene), and oxidation of a group of molecules belonging to the class of Polycyclic Aromatic Hydrocarbons (PAHs). Investigation of both reaction classes have been performed using ab initio quantum chemistry methods with the Gaussian 09 and MOLPRO programs at various levels of theory. Initially, Potential Energy Surfaces (PES) were generated at the G3(MP2,CC)/B3LYP/6-311G** level of theory for various radicals involved in the reactions as reactants, intermediates, transition states, and products. The next step was to perform RiceRamsperger-Kassel-Marcus (RRKM) / Master Equation calculations in order to calculate rate constants and branching ratios of different products at various temperatures and pressures characteristic for combustion flames. All calculations were then compared with previous works on similar systems available in the literature. The results of these simulations along with previous data were then used to formulate guidelines for the pyrolysis and oxidation patterns of larger and more complex systems, in order to achieve a better understanding of the pathways to the end products in airplane jet engines.

Executive function (EF) is a central aspect of cognition that undergoes significant changes in early childhood. Changes in EF in early childhood are robustly predictive of academic achievement and general quality of life measures later in adulthood. I develop a dynamic neural field (DNF) model which provides a process-based account of behavior and developmental change in a key task used to probe the early development of executive function--the Dimensional Change Card Sort (DCCS) task. In the DCCS, children must flexibly switch from sorting cards either by shape or color to sorting by the other dimension. Typically, 3-year-olds, but not 5-year-olds, lack the flexibility to do so and perseverate on the first set of rules when instructed to switch. In Study 1, I use the DNF model to integrate behavioral and neural processes by simulating hemodynamics associated with the early emergence of flexible rule-use. I then test predictions of the model using near-infrared spectroscopy. In Study 2, I develop a DCCS that can be used with adults that sheds light on key aspects of the task as they have been revealed with children. Using fMRI, a pattern of behavioral and neural effects shed light on the central processes involved in flexible rule-use. These two studies demonstrate that performance emerges as a property of system-wide interactions and that common neurocognitive effects .can be found between childhood and adulthood.

The efficiency of the mechanical design process can be improved by the use of evolutionary algorithms. Evolutionary algorithms provide a convenient and robust method to search for appropriate design solutions. Difficult non-linear problems are often encountered during the mechanical engineering design process. Solutions to these problems often involve computationally-intensive simulations. Evolutionary algorithms tuned to work with a small number of solution iterations can be used to automate the search for optimal solutions to these problems. An evolutionary algorithm was designed to give reliable results after a few thousand iterations; additionally the scalability and the ease of application to varied problems were considered. Convergence velocity of the algorithm was improved considerably by altering the mutation-based parameters in the algorithm. Much of this performance gain can be attributed to making the magnitude of the mutation and the minimum mutation rates self-adaptive. Three motorsport based design problems were simulated and the evolutionary algorithm was applied to search for appropriate solutions. The first two, a racing-line generator and a suspension kinematics simulation, were investigated to highlight properties of the evolutionary algorithm: reliability; solution representation; determining variable/performance relationships; and multiple objectives were discussed. The last of these problems was the lap-time simulation of a Formula SAE vehicle. This problem was solved with 32 variables, including a number of major conceptual differences. The solution to this optimisation was found to be significantly better than the 2004 UWA Motorsport vehicle, which finished 2nd in the 2005 US competition. A simulated comparison showed the optimised vehicle would score 62 more points (out of 675) in the dynamic events of the Formula SAE competition. Notably the optimised vehicle had a different conceptual design to the actual UWA vehicle. These results can be used to improve the design of future Formula SAE vehicles. The evolutionary algorithm developed here can be used as an automated search procedure for problems where performance solutions are computationally intensive.

This thesis focuses on nonlinear robust process control synthesis and analysis. The theoretical fundamental is the L2 finite gain theory. The aim of this research is to gain better understanding of this theory and develop new process control synthesis and analysis methods for nonlinear processes with model uncertainties and unmeasured disturbances.The current nonlinear process control methods are examined in this thesis. The research scopes of this study are described as follows: 1/. To characterize the most common process control problems such as zero-offset requirement, presentation of model uncertainties and unknown disturbance in the L2 finite gain theory framework and solve the basic theoretical issues concerned in controller design. 2/. To solve numerical computation problems arising in the nonlinear controller. 3/. To investigate the relationship between robustness requirement and performance requirement for nonlinear systems in the L2 finite gain theory framework. 4/. To consider the common phenomenon such as time-delay in the new developed methods. 5/. To investigate the advantages of the controller based on the L2 finite gain theory over the current nonlinear control methods. A series of new systematic robust process control synthesis approaches are the main contributions of this study. Simulations show the potential of these newly developed methods. / Doctor of Philosophy (PhD)

Any computation is facilitated by some physical process, and the observable quantities of any physical process can be viewed as a computation. These close ties suggest that the study of what universal computers are capable of may lead to additional insight about the physical universe, and vice versa. In his thesis, we explore three lines of research that are linked to this central theme. The first partition shows how notions of non-computability and undecidability eventually led to evidence of emergence, the concept that even if a ‘theory of everything’ governing all microscopic interactions were discovered, the understanding of macroscopic order is likely to require additional insights. The second partition proposes a physically motivated model of computation that relates quantum complexity, quantum optimal control, and Riemannian geometry. Thus insights in any one of these disciplines could also lead to insights in the others. The remainder of this partition explores a simple application of these relations. The final partition proposes a model of quantum computation that generalizes measurement based computation to continuous variables. We outline its optical implementation, whereby any computation can be performed by single mode measurements on a resource state that can be prepared by passing a collection of squeezed states through a beamsplitter network.

DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions, making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior.

First principles computation can be used to investigate an design materials in ways that can not be achieved with experimental means. We show how computations can be used to rapidly capture the essential physics that determines the useful properties in different applications. Some applications for predicting crystal structure, thermodynamic and kinetic properties, and phase stability are discussed. This first principles tool set will be demonstrated with applications from rechargeable batteries and hydrogen storage materials. / Singapore-MIT Alliance (SMA)

Over the past decade it has become increasingly clear that to understand the brain, we must study not only its biochemical and biophysical mechanisms and its outward perceptual and physical behavior. We also must study the brain at a theoretical level that investigated the computations that are necessary to perform its functions. The control of movements such as reaching, grasping and manipulating objects requires complex mechanisms that elaborate information form many sensors and control the forces generated by a large number of muscles. The act of seeing, which intuitively seems so simple and effortless, requires information processing whose complexity we are just beginning to grasp. A computational approach to the study of vision and motor tasks. This paper discusses a particular view of the computational approach and its relevance to experimental neuroscience.

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.