Real-Time State Estimation in Plasma Modeling Applications

Development of truly predictive models for plasma physics phenomena continues to pose a significant challenge to the research community. Recent interest in data-driven modeling and data assimilation have arisen in the plasma physics community to provide an alternative to predictive models or to directly solve for uncertain physics. The focus of this doctoral research is the development of a state estimation technique that uses experimental measurements to improve plasma physics models by either improving the solutions of lower-fidelity, faster running models or by estimating unknown or uncertain physics. This dissertation demonstrates that the simple class of Kalman filtering can provide significant insight to plasma modeling. Test cases begin with the canonical Lorenz chaotic attractor and a driven-damped harmonic oscillator to demonstrate the fidelity of the estimation technique as increasingly sparse measurement data are used. Then, the EKF is applied to global plasma models to demonstrate that physical states including the electron temperature, absorbed electron power, and reaction rate coefficients can be estimated with physical relevance. Additionally, test cases including complex models, measurement signals relating to multiple states, and multiple estimates being sought, simultaneously, are examined. Finally, this dissertation extends the EKF into a single spatial dimension. After two general test cases are used to demonstrate how the filter can be applied in one spatial dimension for representative cases of drift and diffusion processes, the conclusion of the dissertation focuses on the challenges of applying the EKF to a one-dimensional fluid model of a Hall effect thruster to study the anomalous component of electron mobility

The application of chaos theory to forecast urban traffic conditions

PhD ThesisThis thesis explores the application of Chaos Theory to forecast urban traffic conditions. The research takes advantage of a highly resolved temporal and spatial data available from the Split Cycle Optimisation Technique (SCOOT) system, in order to overcome the limitations of previous studies to investigate applying Chaos Theory in traffic management. This thesis reports on the development of a chaos-based algorithm and presents results from its application to a SCOOT controlled region in the city of Leicester, UK. A Phase Space Reconstruction method is used to analyse non-linear data from the SCOOT system, and establishes that a 20 second resolved data is suitable for understanding the dynamics of the traffic system. The research develops the Lyapunov exponent as a chaos-based parameter to forecast link occupancy using a multiple regression model based on the temporal and spatial relationships across the links in the network. The model generates a unique forecast function for each link for every hour of the day. The study demonstrates that Lyapunov exponents can be used to predict the occupancy profile of links in the network to a reasonably high level of accuracy (R-values generally greater than 0.6). Evidence also suggests that the predictions from the Lyapunov exponents (rather than occupancy) make it possible to report on the impending conditions over a wider part of the network so that imminent congested conditions can be foreseen in advance and mitigation measures implemented. Thus, the thesis concludes that incorporating chaos-based algorithms in this way can enable urban traffic control systems to be one-step ahead of traffic congestion, rather than one-step behind. This would improve the management of traffic on a more strategic level rather than purely within smaller network regions thus playing an important role in improving journey times and air quality and making a vital contribution to mitigating climate change

The characterisation of multicomponent (liquid) flows using scattered ultrasound.

The aim of this work is to determine the applicability of ultrasonic techniques to developing a non invasive flow meter capable of characterising multicomponent (liquid) flows. The possibility of detecting flow parameters such as velocity distributions, droplet/particle size distributions, spatial distribution and void fraction of the discontinuous phase has been investigated. An early consideration of the likely applications of this meter, revealed that an ultrasonic technique would be the most versatile and suitable. Consequently, a theoretical study of the interaction of an ultrasonic wave and a disperse system has been carried out, as well as a study of the possible regimes where these principles may be applied. The work begins from first principles, studying both experimentally and theoretically the interaction of an acoustic wave with a single particle. This is then extended to characterising a flowing multicomponent system on a larger scale. The nature of complex flows was then investigated from the point of view of a chaotic dynamical system. Both theoretical and experimental methods show this to be a valid approach to understanding the flow of mixtures.PhD in Engineerin