Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Lightweight, liquid-cooled, direct-drive generator for highpower wind turbines: motivation, concept, and performance

Thesis: A liquid-cooled, direct-drive, permanent-magnet, synchronous generator with helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit offers an excellent combination of attributes to reliably provide economic wind power for the coming generation of wind turbines with power ratings between 5 and 20MW. A generator based on the liquid-cooled architecture proposed here will be reliable and cost effective. Its smaller size and mass will reduce build, transport, and installation costs. Summary: Converting wind energy into electricity and transmitting it to an electrical power grid to supply consumers is a relatively new and rapidly developing method of electricity generation. In the most recent decade, the increase in wind energy’s share of overall energy production has been remarkable. Thousands of land-based and offshore wind turbines have been commissioned around the globe, and thousands more are being planned. The technologies have evolved rapidly and are continuing to evolve, and wind turbine sizes and power ratings are continually increasing. Many of the newer wind turbine designs feature drivetrains based on Direct-Drive, Permanent-Magnet, Synchronous Generators (DD-PMSGs). Being low-speed high-torque machines, the diameters of air-cooled DD-PMSGs become very large to generate higher levels of power. The largest direct-drive wind turbine generator in operation today, rated just below 8MW, is 12m in diameter and approximately 220 tonne. To generate higher powers, traditional DD-PMSGs would need to become extraordinarily large. A 15MW air-cooled direct-drive generator would be of colossal size and tremendous mass and no longer economically viable. One alternative to increasing diameter is instead to increase torque density. In a permanent magnet machine, this is best done by increasing the linear current density of the stator windings. However, greater linear current density results in more Joule heating, and the additional heat cannot be removed practically using a traditional air-cooling approach. Direct liquid cooling is more effective, and when applied directly to the stator windings, higher linear current densities can be sustained leading to substantial increases in torque density. The higher torque density, in turn, makes possible significant reductions in DD-PMSG size. Over the past five years, a multidisciplinary team of researchers has applied a holistic approach to explore the application of liquid cooling to permanent-magnet wind turbine generator design. The approach has considered wind energy markets and the economics of wind power, system reliability, electromagnetic behaviors and design, thermal design and performance, mechanical architecture and behaviors, and the performance modeling of installed wind turbines. This dissertation is based on seven publications that chronicle the work. The primary outcomes are the proposal of a novel generator architecture, a multidisciplinary set of analyses to predict the behaviors, and experimentation to demonstrate some of the key principles and validate the analyses. The proposed generator concept is a direct-drive, surface-magnet, synchronous generator with fractional-slot, duplex-helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit to accommodate liquid coolant flow. The novel liquid-cooling architecture is referred to as LC DD-PMSG. The first of the seven publications summarized in this dissertation discusses the technological and economic benefits and limitations of DD-PMSGs as applied to wind energy. The second publication addresses the long-term reliability of the proposed LC DD-PMSG design. Publication 3 examines the machine’s electromagnetic design, and Publication 4 introduces an optimization tool developed to quickly define basic machine parameters. The static and harmonic behaviors of the stator and rotor wheel structures are the subject of Publication 5. And finally, Publications 6 and 7 examine steady-state and transient thermal behaviors. There have been a number of ancillary concrete outcomes associated with the work including the following. X Intellectual Property (IP) for direct liquid cooling of stator windings via an embedded coaxial coolant conduit, IP for a lightweight wheel structure for lowspeed, high-torque electrical machinery, and IP for numerous other details of the LC DD-PMSG design X Analytical demonstrations of the equivalent reliability of the LC DD-PMSG; validated electromagnetic, thermal, structural, and dynamic prediction models; and an analytical demonstration of the superior partial load efficiency and annual energy output of an LC DD-PMSG design X A set of LC DD-PMSG design guidelines and an analytical tool to establish optimal geometries quickly and early on X Proposed 8 MW LC DD-PMSG concepts for both inner and outer rotor configurations Furthermore, three technologies introduced could be relevant across a broader spectrum of applications. 1) The cost optimization methodology developed as part of this work could be further improved to produce a simple tool to establish base geometries for various electromagnetic machine types. 2) The layered sheet-steel element construction technology used for the LC DD-PMSG stator and rotor wheel structures has potential for a wide range of applications. And finally, 3) the direct liquid-cooling technology could be beneficial in higher speed electromotive applications such as vehicular electric drives.

Large-Size AlGaN/GaN HEMT Large-Signal Electrothermal Characterization and Modeling for Wireless Digital Communications

The rapid growth in high data rate communication systems has introduced new high spectral efficient modulation techniques and standards such as LTE-A (long term evolution-advanced) for 4G (4th generation) systems. These techniques have provided a broader bandwidth but introduced high peak-to-average power ratio (PAR) problem at the high power amplifier (HPA) level of the communication system base transceiver station (BTS). To avoid spectral spreading due to high PAR, stringent requirement on linearity is needed which brings the HPA to operate at large back-off power at the expense of power efficiency. Consequently, high power devices are fundamental in HPAs for high linearity and efficiency. Recent development in wide bandgap power devices, in particular AlGaN/GaN HEMT, has offered higher power level with superior linearity-efficiency trade-off in microwaves communication. For cost-effective HPA design to production cycle, rigorous computer aided design (CAD) AlGaN/GaN HEMT models are essential to reflect real response with increasing power level and channel temperature. Therefore, large-size AlGaN/GaN HEMT large-signal electrothermal modeling procedure is proposed. The HEMT structure analysis, characterization, data processing, model extraction and model implementation phases have been covered in this thesis including trapping and self-heating dispersion accounting for nonlinear drain current collapse. The small-signal model is extracted using the 22-element modeling procedure developed in our department. The intrinsic large-signal model is deeply investigated in conjunction with linearity prediction. The accuracy of the nonlinear drain current has been enhanced through several issues such as trapping and self-heating characterization. Also, the HEMT structure thermal profile has been investigated and corresponding thermal resistance has been extracted through thermal simulation and chuck-controlled temperature pulsed I(V) and static DC measurements. Higher-order equivalent thermal model is extracted and implemented in the HEMT large-signal model to accurately estimate instantaneous channel temperature. Moreover, trapping and self-heating transients has been characterized through transient measurements. The obtained time constants are represented by equivalent sub-circuits and integrated in the nonlinear drain current implementation to account for complex communication signals dynamic prediction. The obtained verification of this table-based large-size large-signal electrothermal model implementation has illustrated high accuracy in terms of output power, gain, efficiency and nonlinearity prediction with respect to standard large-signal test signals.

The Chemical Percolation Devolatilization Model Applied to the Devolatilization of Coal in High Intensity Acoustic Fields

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Methods for modeling and analyzing concurrent software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.

Methods for Modeling and Analyzing Concurrent Software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.

Enriched behavioral prediction equation and its impact on structured learning and the dynamic calculus

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model; R. B. Cattell, 1996b), such as encountered within psycho therapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal.

Stress correlation function evolution in lattice solid elasto-dynamic models of shear and fracture zones and earthquake prediction

It has been argued that power-law time-to-failure fits for cumulative Benioff strain and an evolution in size-frequency statistics in the lead-up to large earthquakes are evidence that the crust behaves as a Critical Point (CP) system. If so, intermediate-term earthquake prediction is possible. However, this hypothesis has not been proven. If the crust does behave as a CP system, stress correlation lengths should grow in the lead-up to large events through the action of small to moderate ruptures and drop sharply once a large event occurs. However this evolution in stress correlation lengths cannot be observed directly. Here we show, using the lattice solid model to describe discontinuous elasto-dynamic systems subjected to shear and compression, that it is for possible correlation lengths to exhibit CP-type evolution. In the case of a granular system subjected to shear, this evolution occurs in the lead-up to the largest event and is accompanied by an increasing rate of moderate-sized events and power-law acceleration of Benioff strain release. In the case of an intact sample system subjected to compression, the evolution occurs only after a mature fracture system has developed. The results support the existence of a physical mechanism for intermediate-term earthquake forecasting and suggest this mechanism is fault-system dependent. This offers an explanation of why accelerating Benioff strain release is not observed prior to all large earthquakes. The results prove the existence of an underlying evolution in discontinuous elasto-dynamic, systems which is capable of providing a basis for forecasting catastrophic failure and earthquakes.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Developing methodologies to allow the robust prediction dynamic context dominated by perturbations

El principal objectiu del projecte era desenvolupar millores conceptuals i metodològiques que permetessin una millor predicció dels canvis en la distribució de les espècies (a una escala de paisatge) derivats de canvis ambientals en un context dominat per pertorbacions. En un primer estudi, vàrem comparar l'eficàcia de diferents models dinàmics per a predir la distribució de l'hortolà (Emberiza hortulana). Els nostres resultats indiquen que un model híbrid que combini canvis en la qualitat de l'hàbitat, derivats de canvis en el paisatge, amb un model poblacional espacialment explícit és una aproximació adequada per abordar canvis en la distribució d'espècies en contextos de dinàmica ambiental elevada i una capacitat de dispersió limitada de l'espècie objectiu. En un segon estudi abordarem la calibració mitjançant dades de seguiment de models de distribució dinàmics per a 12 espècies amb preferència per hàbitats oberts. Entre les conclusions extretes destaquem: (1) la necessitat de que les dades de seguiment abarquin aquelles àrees on es produeixen els canvis de qualitat; (2) el biaix que es produeix en la estimació dels paràmetres del model d'ocupació quan la hipòtesi de canvi de paisatge o el model de qualitat d'hàbitat són incorrectes. En el darrer treball estudiarem el possible impacte en 67 espècies d’ocells de diferents règims d’incendis, definits a partir de combinacions de nivells de canvi climàtic (portant a un augment esperat de la mida i freqüència d’incendis forestals), i eficiència d’extinció per part dels bombers. Segons els resultats dels nostres models, la combinació de factors antropogènics del regim d’incendis, tals com l’abandonament rural i l’extinció, poden ser més determinants per als canvis de distribució que els efectes derivats del canvi climàtic. Els productes generats inclouen tres publicacions científiques, una pàgina web amb resultats del projecte i una llibreria per a l'entorn estadístic R.

Prediction of Soil Properties from the Concepts of Energy Transfer in Dynamic Penetration Tests

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prediction and spatial variability of soil dynamic properties in sugar cane fields of São Paulo State Brazil

The objective of this work was to model and diagnose the spatial variability of soil load support capacity (SLSC) in sugar cane crop fields, as well as to evaluate the management impact on São Paulo State soil structure. The investigated variables were: pressure preconsolidation (sigma(p)), apparent cohesion () and internal friction angle (). The conclusions from the results were that the models and spatial dependence maps constitute important tools in the prediction and location of the mechanical internal strength of soils cultivated with sugar cane. They will help future soil management decisions so that soil structure sustainability will not be compromised.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.