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

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Machine learning for quantum and complex systems

Machine learning now plays a pivotal role in our society, providing solutions to problems that were previously thought intractable. The meteoric rise of this technology can no doubt be attributed to the information age that we now live in. As data is continually amassed, more efficient and scalable methods are required to yield functional models and accurate inferences. Simultaneously we have also seen quantum technology come to the forefront of research and next generation systems. These technologies promise secure information transfer, efficient computation and high precision sensing, at levels unattainable by their classical counterparts. Although these technologies are powerful, they are necessarily more complicated and difficult to control. The combination of these two advances yields an opportunity for study, namely leveraging the power of machine learning to control and optimise quantum (and more generally complex) systems. The work presented in thesis explores these avenues of investigation and demonstrates the potential success of machine learning methods in the domain of quantum and complex systems. One of the most crucial potential quantum technologies is the quantum memory. If we are to one day harness the true power of quantum key distribution for secure transimission of information, and more general quantum computating tasks, it will almost certainly involve the use of quantum memorys. We start by presenting the operation of the cold atom workhorse: the magneto-optical trap (MOT). To use a cold atomic ensemble as a quantum memory we are required to prepare the atoms using a specialised cooling sequence. During this we observe a stable, coherent optical emission exiting each end of the elongated ensemble. We characterise this behaviour and compare it to similar observations in previous work. Following this, we use the ensemble to implement a backward Raman memory. Using this scheme we are able to demonstrate an increased efficiency over that of previous forward recall implementations. While we are limited by the optical depth of the system, we observe an efficiency more than double that of previous implementations. The MOT provides an easily accessible test bed for the optimisation via some machine learning technique. As we require an efficient search method, we implement a new type of algorithm based on deep learning. We design this technique such that the artificial neural networks are placed in control of the online optimisation, rather than simply being used as surrogate models. We experimentally optimise the optical depth of the MOT using this method, by parametrising the time varying compression sequence. We identify a new and unintuitive method for cooling the atomic ensemble which surpasses current methods. Following this initial implementation we make substantial improvements to the deep learning approach. This extends the approach to be applicable to a far wider range of complex problems, which may contain extensive local minima and structure. We benchmark this algorithm against many of the conventional optimisation techniques and demonstrate superior capability to optimise problems with high dimensionality. Finally we apply this technique to a series of preliminary problems, namely the tuning of a single electron transistor and second-order correlations from a quantum dot source

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Inverse Dynamics Problems

The inverse dynamics problem was developed in order to provide researchers with the state of the art in inverse problems for dynamic and vibrational systems. Contrasted with a forward problem, which solves for the system output in a straightforward manner, an inverse problem searches for the system input through a procedure contaminated with errors and uncertainties. An inverse problem, with a focus on structural dynamics, determines the changes made to the system and estimates the inputs, including forces and moments, to the system, utilizing measurements of structural vibration responses only. With its complex mathematical structure and need for more reliable input estimations, the inverse problem is still a fundamental subject of research among mathematicians and engineering scientists. This book contains 11 articles that touch upon various aspects of inverse dynamic problems

Experimental Methods in Cryogenic Spectroscopy: Stark Effect Measurements in Substituted Myoglobin

Dawning from well-defined tertiary structure, the active regions of enzymatic proteins exist as specifically tailored electrostatic microenvironments capable of facilitating chemical interaction. The specific influence these charge distributions have on ligand binding dynamics, and their impact on specificity, reactivity, and biological functionality, have yet to be fully understood. A quantitative determination of these intrinsic fields would offer insight towards the mechanistic aspects of protein functionality. This work seeks to investigate the internal molecular electric fields that are present at the oxygen binding site of myoglobin. Experiments are performed at 1 K on samples located within a glassy matrix, using the high-resolution technique spectral hole-burning. The internal electric field distributions can be explored by implementing a unique mathematical treatment for analyzing the effect that externally applied electric fields have on the spectral hole profiles. Precise control of the light field, the temperature, and the externally applied electric field at the site of the sample is crucial. Experimentally, the functionality of custom cryogenic temperature confocal scanning microscope was extended to allow for collection of imaging and spectral data with the ability to modulate the polarization of the light at the sample. Operation of the instrumentation was integrated into a platform allowing for seamless execution of input commands with high temporal inter-instrument resolution for collection of data streams. For the regulated control and cycling of the sample temperature. the thermal characteristics of the research Dewar were theoretically modeled to systematically predict heat flows throughout the system. A high voltage feedthrough for delivering voltages of up to 5000 V to the sample as positioned within the Dewar was developed. The burning of spectral holes with this particular experimental setup is highly repeatable. The quantum mechanical treatment that is employed during analysis of the experimental data requires the state energies and the transition dipole moments of the porphyrin probe. The configuration interaction, as well as the coupled-cluster approaches, have been investigated for their ability to produce realistic valuations for these calculated quantities as gauged by their ability to accurately reproduce valuations for spectroscopically observable transition energies. A capacitive cell, for the determination of a material’s dielectric permittivity, necessary for defining the magnitude of the externally applied electric field at the sample, was developed and shown to successfully yield permittivity valuations for various media in accordance with those reported the literature, while offering the ability to provide measures for permittivities over the temperature range of 1-300 K

Multi-level-objective design optimization of permanent magnet synchronous wind generator and solar photovoltaic system for an urban environment application

This Ph.D. thesis illustrates a novel study on the analytical and numerical design optimization of radial-flux permanent magnet synchronous wind generators (PMSGs) for small power generation in an urban area, in which an outer rotor topology with a closed-slot stator is employed. The electromagnetic advantages of a double-layer fractional concentration non-overlapping winding configuration are discussed. The analytical behavior of a PMSG is studied in detail; especially for magnetic flux density distribution, time and space harmonics, flux linkages, back-EMF, cogging torque, torque, output power, efficiency, and iron losses computation. The electromagnetic behavior of PMSGs are evaluated when a number of various Halbach array magnetization topologies are presented to maximize the generator’s performance. In addition, the thermal behavior of the PMSG is improved using an innovative natural air-cooling system for rated speed and higher to decrease the machine’s heat mainly at the stator teeth. The analytical investigation is verified via 2-D and 3-D finite element analysis along with a good experimental agreement. Design optimization of electrical machines plays the deterministic role in performance improvements such as the magnetization pattern, output power, and efficiency maximization, as well as losses and material cost minimization. This dissertation proposes a novel multi-objective design optimization technique using a dual-level response surface methodology (D-RSM) and Booth’s algorithm (coupled to a memetic algorithm known as simulated annealing) to maximize the output power and minimize material cost through sizing optimization. Additionally, the efficiency maximization by D-RSM is investigated while the PMSG and drive system are on duty as the whole. It is shown that a better fit is available when utilizing modern design functions such as mixed-resolution central composite (MR-CCD) and mixed-resolution robust (MR-RD), due to controllable and uncontrollable design treatments, and also a Window-Zoom-in approach. The proposed design optimization was verified by an experimental investigation. Additionally, there are several novel studies on vibro-acoustic design optimization of the PMSGs with considering variable speed analysis and natural frequencies using two techniques to minimize the magnetic noise and vibrations. Photovoltaic system design optimization considered of 3-D modeling of an innovative application-oriented urban environment structure, a smart tree for small power generation. The horizon shading is modeled as a broken line superimposed onto the sun path diagram, which can hold any number of height/azimuth points in this original study. The horizon profile is designed for a specific location on the Barcelona coast in Spain and the meteorological data regarding the location of the project was also considered. Furthermore, the input weather data is observed and stored for the whole year (in 2016). These data include, ambient temperature, module’s temperature (open and closed circuits tests), and shading average rate. A novel Pareto-based 3-D analysis was used to identify complete and partial shading of the photovoltaic system. A significant parameter for a photovoltaic (PV) module operation is the nominal operating cell temperature (NOCT). In this research, a glass/glass module has been referenced to the environment based on IEC61215 via a closed-circuit and a resistive load to ensure the module operates at the maximum power point. The proposed technique in this comparative study attempts to minimize the losses in a certain area with improved output energy without compromising the overall efficiency of the system. A Maximum Power Point Track (MPPT) controller is enhanced by utilizing an advanced perturb & observe (P&O) algorithm to maintain the PV operating point at its maximum output under different temperatures and insolation. The most cost-effective design of the PV module is achieved via optimizing installation parameters such as tilt angle, pitch, and shading to improve the energy yield. The variation of un-replicated factorials using a Window-Zoom-in approach is examined to determine the parameter settings and to check the suitability of the design. An experimental investigation was carried out to verify the 3-D shading analysis and NOCT technique for an open-circuit and grid-connected PV module.Esta tesis muestra un novedoso estudio referente al diseño optimizado de forma analítica y numérica de un generador síncrono de imanes permanentes (PMSGs) para una aplicación de microgeneración eólica en un entorno urbano, donde se ha escogido una topología de rotor exterior con un estator de ranuras cerradas. Las ventajas electromagnéticas de los arrollamientos fraccionarios de doble capa, con bobinas concentradas se discuten ampliamente en la parte inicial del diseño del mismo, así como las características de distribución de la inducción, los armónicos espaciales y temporales, la fem generada, el par de cogging así como las características de salida (par, potencia generada, la eficiencia y la distribución y cálculo de las pérdidas en el hierro que son analizadas detalladamente) Posteriormente se evalúan diferentes configuraciones de estructuras de imanes con magnetización Halbach con el fin de maximizar las prestaciones del generador. Adicionalmente se analiza la distribución de temperaturas y su mejora mediante el uso de un novedoso diseño mediante el uso de ventilación natural para velocidades próximas a la nominal y superiores con el fin de disminuir la temperatura de la máquina, principalmente en el diente estatórico. El cálculo analítico se completa mediante simulaciones 2D y 3D utilizando el método de los elementos finitos así como mediante diversas experiencias que validan los modelos y aproximaciones realizadas. Posteriormente se desarrollan algoritmos de optimización aplicados a variables tales como el tipo de magnetización, la potencia de salida, la eficiencia así como la minimización de las pérdidas y el coste de los materiales empleados. En la tesis se proponen un nuevo diseño optimizado basado en una metodología multinivel usando la metodología de superficie de respuesta (D-RSM) y un algoritmo de Booth (maximizando la potencia de salida y minimizando el coste de material empleado) Adicionalmente se investiga la maximización de la eficiencia del generador trabajando conjuntamente con el circuito de salida acoplado. El algoritmo utilizado queda validado mediante la experimentación desarrollada conjuntamente con el mismo. Adicionalmente, se han realizado diversos estudios vibroacústicos trabajando a velocidad variable usando dos técnicas diferentes para reducir el ruido generado y las vibraciones producidas. Posteriormente se considera un sistema fotovoltaico orientado a aplicaciones urbanas que hemos llamado “Smart tree for small power generation” y que consiste en un poste con un generador eólico en la parte superior juntamente con uno o más paneles fotovoltaicos. Este sistema se ha modelado usando metodologías en 3D. Se ha considerado el efecto de las sombras proyectadas por los diversos elementos usando datos meteorológicos y de irradiación solar de la propia ciudad de Barcelona. Usando una metodología basada en un análisis 3D y Pareto se consigue identificar completamente el sistema fotovoltaico; para este sistema se considera la temperatura de la célula fotovoltaica y la carga conectada con el fin de generar un algoritmo de control que permita obtener el punto de trabajo de máxima potencia (MPPT) comprobándose posteriormente el funcionamiento del algoritmo para diversas situaciones de funcionamiento del sistemaLa tesis desenvolupa un nou estudi per al disseny optimitzat, analític i numèric, d’un generador síncron d’imants permanents (PMSGs) per a una aplicació de microgeneració eòlica en aplicacions urbanes, on s’ha escollit una configuració amb rotor exterior i estator amb ranures tancades. Es discuteixen de forma extensa els avantatges electromagnètics dels bobinats fraccionaris de doble capa així com les característiques resultats vers la distribució de les induccions, els harmònics espacials i temporals, la fem generada, el parell de cogging i les característiques de sortida (parell, potencia, eficiència i pèrdues) Tanmateix s’afegeix l’estudi de diferents estructures Halbach per als imants permanents a fi i efecte de maximitzar les característiques del generador. Tot seguit s’analitza la distribució de temperatures i la seva reducció mitjançant la utilització d’una nova metodologia basada en la ventilació natural. Els càlculs analítics es complementen mitjançant anàlisi en 2 i 3 dimensions utilitzant elements finits i diverses experiències que validen els models i aproximacions emprades. Una vegada fixada la geometria inicial es desenvolupen algoritmes d’optimització per a diverses variables (tipus de magnetització dels imants, potencia de sortida, eficiència, minimització de pèrdues i cost dels materials) La tesi planteja una optimització multinivell emprant la metodologia de superfície de resposta i un algoritme de Booth; a més, es realitza la optimització considerant el circuit de sortida. L’algoritme resta validat per la experimentació realitzada. Finalment, s’han considerat diversos estudis vibroacústic treballant a velocitat variable, emprant dues tècniques diferents per a reduir el soroll i les vibracions desenvolupades. Per a finalitzar l’estudi es considera un sistema format per una turbina eòlica instal·lada sobre un pal de llum autònom, els panells fotovoltaics corresponents i el sistema de càrrega. Per a modelitzar l’efecte de l’ombrejat s’ha emprat un model en 3D i les dades del temps i d’irradiació solar de la ciutat de Barcelona. El model s’ha identificat completament i s’ha generat un algoritme de control que considera, a més, l’efecte de la temperatura de la cèl·lula fotovoltaica y la càrrega connectada al sistema per tal d’aconseguir el seguiment del punt de màxima potenciaPostprint (published version

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand