Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

Adherence to therapeuthic regimen and metabolic control of type 1 diabetes

Abstract Objectives: To assess the adherence to therapeutic regimen; to determine the Hemoglobin Glycation Index (HbA1c); to analyse the relationship that exists between the adherence to therapeutic regimen and metabolic control. Design: correlational analytical study, carried out according to a cross-sectional perspective. Participants: A non-probabilistic sample of 266 people with type 1 diabetes aged between 18 and 78 years old (mean M = 51.02 ± SD = 18.710), attending follow-up diabetes consultations. Mostly male individuals (51.88%), with low schooling level (50.75% had only inished elementar school). Measuring Instruments: We used the following data collection tools: a questionnaire on clinical and socio-demographic data, blood analysis of venous blood to determine the glycated hemoglobin level (HbA1c).Three self-report scales were used: Accession to Diabetes Treatment (Matos, 1999), Self-perception Scale (Vaz Serra, 1986) and Social Support Scale (Matos & Rodrigues, 2000). Results: In a sample in which the mean disease duration is 12.75 years, 69.17% of the sample run glycemic control tests between once a day and four times a year and 42.86% of them undergo insulin treatment. In the last 3 weeks, 26.32% of these people have experienced an average of 4.22 to 44.36%, hypoglycemic crises and experienced an average of 6.18 hyperglycemic crises. 57% of the individuals have showed a poor metabolic control (mean HbA1c higher than 7.5% (HbA1c mean M ≥ 7.50%). The mean psychosocial proile revealed individuals who show a decent self-esteem (M = 70.81) and acceptable social support (M = 58.89). Conclusions: The results suggest we should develop a kind of investigation that could be used to monitor the strenght of the mediation effect effect of the psychosocial predictive dimension of the adherence, since it has become essential to support a multidisciplinary approach which center lays in the promotion of a co-responsible self-management from the person who suffers from diabetes. This will enable a better quality of life; fewer years of people’s lives lost prematurely and a better health with less economical costs for citizens and healthcare systems.

Modelling and control of a floating oscillating water column

A novel numerical model of a Bent Backwards Duct Buoy (BBDB) Oscillating Water Column (OWC) Wave Energy Converter was created based on existing isolated numerical models of the different energy conversion systems utilised by an OWC. The novel aspect of this numerical model is that it incorporates the interdependencies of the different power conversion systems rather than modelling each system individually. This was achieved by accounting for the dynamic aerodynamic damping caused by the changing turbine rotational velocity by recalculating the turbine damping for each simulation sample and applying it via a feedback loop. The accuracy of the model was validated using experimental data collected during the Components for Ocean Renewable Energy Systems (CORES) EU FP-7 project that was tested in Galway Bay, Ireland. During the verification process, it was discovered that the model could also be applied as a valuable tool when troubleshooting device performance. A new turbine was developed and added to a full scale model after being investigated using Computational Fluid Dynamics. The energy storage capacity of the impulse turbine was investigated by modelling the turbine with both high and low inertia and applying three turbine control theories to the turbine using the full scale model. A single Maximum Power Point Tracking algorithm was applied to the low-inertia turbine, while both a fixed and dynamic control algorithm was applied to the high-inertia turbine. These results suggest that the highinertia turbine could be used as a flywheel energy storage device that could help minimize output power variation despite the low operating speed of the impulse turbine. This research identified the importance of applying dynamic turbine damping to a BBDB OWC numerical model, revealed additional value of the model as a device troubleshooting tool, and found that an impulse turbine could be applied as an energy storage system.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Commande prédictive non linéaire d’un séchoir à lit fluidisé pour granules pharmaceutiques

Avec la disponibilité de capteurs fiables de teneur en eau exploitant la spectroscopie proche infrarouge (NIR pour near-infrared) et les outils chimiométriques, il est maintenant possible d’appliquer des stratégies de commande en ligne sur plusieurs procédés de séchage dans l’industrie pharmaceutique. Dans cet ouvrage, le séchage de granules pharmaceutiques avec un séchoir à lit fluidisé discontinu (FBD pour fluidized bed dryer) de taille pilote est étudié à l’aide d’un capteur d’humidité spectroscopique. Des modifications électriques sont d’abord effectuées sur le séchoir instrumenté afin d’acheminer les signaux mesurés et manipulés à un périphérique d’acquisition. La conception d’une interface homme-machine permet ensuite de contrôler directement le séchoir à l’aide d’un ordinateur portable. Par la suite, un algorithme de commande prédictive (NMPC pour nonlinear model predictive control), basée sur un modèle phénoménologique consolidé du FBD, est exécuté en boucle sur ce même ordinateur. L’objectif est d’atteindre une consigne précise de teneur en eau en fin de séchage tout en contraignant la température des particules ainsi qu’en diminuant le temps de lot. De plus, la consommation énergétique du FBD est explicitement incluse dans la fonction objectif du NMPC. En comparant à une technique d’opération typique en industrie (principalement en boucle ouverte), il est démontré que le temps de séchage et la consommation énergétique peuvent être efficacement gérés sur le procédé pilote tout en limitant plusieurs problèmes d’opération comme le sous-séchage, le surséchage ou le surchauffage des granules.

Numerical simulation of fresh SCC flow in wall and beam elements using flow dynamics models

Abstract : Recently, there is a great interest to study the flow characteristics of suspensions in different environmental and industrial applications, such as snow avalanches, debris flows, hydrotransport systems, and material casting processes. Regarding rheological aspects, the majority of these suspensions, such as fresh concrete, behave mostly as non-Newtonian fluids. Concrete is the most widely used construction material in the world. Due to the limitations that exist in terms of workability and formwork filling abilities of normal concrete, a new class of concrete that is able to flow under its own weight, especially through narrow gaps in the congested areas of the formwork was developed. Accordingly, self-consolidating concrete (SCC) is a novel construction material that is gaining market acceptance in various applications. Higher fluidity characteristics of SCC enable it to be used in a number of special applications, such as densely reinforced sections. However, higher flowability of SCC makes it more sensitive to segregation of coarse particles during flow (i.e., dynamic segregation) and thereafter at rest (i.e., static segregation). Dynamic segregation can increase when SCC flows over a long distance or in the presence of obstacles. Therefore, there is always a need to establish a trade-off between the flowability, passing ability, and stability properties of SCC suspensions. This should be taken into consideration to design the casting process and the mixture proportioning of SCC. This is called “workability design” of SCC. An efficient and non-expensive workability design approach consists of the prediction and optimization of the workability of the concrete mixtures for the selected construction processes, such as transportation, pumping, casting, compaction, and finishing. Indeed, the mixture proportioning of SCC should ensure the construction quality demands, such as demanded levels of flowability, passing ability, filling ability, and stability (dynamic and static). This is necessary to develop some theoretical tools to assess under what conditions the construction quality demands are satisfied. Accordingly, this thesis is dedicated to carry out analytical and numerical simulations to predict flow performance of SCC under different casting processes, such as pumping and tremie applications, or casting using buckets. The L-Box and T-Box set-ups can evaluate flow performance properties of SCC (e.g., flowability, passing ability, filling ability, shear-induced and gravitational dynamic segregation) in casting process of wall and beam elements. The specific objective of the study consists of relating numerical results of flow simulation of SCC in L-Box and T-Box test set-ups, reported in this thesis, to the flow performance properties of SCC during casting. Accordingly, the SCC is modeled as a heterogeneous material. Furthermore, an analytical model is proposed to predict flow performance of SCC in L-Box set-up using the Dam Break Theory. On the other hand, results of the numerical simulation of SCC casting in a reinforced beam are verified by experimental free surface profiles. The results of numerical simulations of SCC casting (modeled as a single homogeneous fluid), are used to determine the critical zones corresponding to the higher risks of segregation and blocking. The effects of rheological parameters, density, particle contents, distribution of reinforcing bars, and particle-bar interactions on flow performance of SCC are evaluated using CFD simulations of SCC flow in L-Box and T-box test set-ups (modeled as a heterogeneous material). Two new approaches are proposed to classify the SCC mixtures based on filling ability and performability properties, as a contribution of flowability, passing ability, and dynamic stability of SCC.

Assessment of catches, landings and fishing effort as useful tools for MPA management

Marine protected areas (MPAs) have been widely recognized as a tool to achieve both fisheries management and conservation goals. Simultaneously achieving these multiple goals is difficult due to conflicts between conservation (often long-term) and economic (often short-term) objectives. MPA implementation often includes additional control measures on fisheries (e.g. vessel size restrictions, gear exclusion, catch controls) that in the short-term may have impacts on local fishers' communities. Thus, monitoring fisheries catches before, during and after MPA implementation is essential to document changes in fisheries activities and to evaluate the impact of MPAs in fishers' communities. Remarkably, in contrast with standard fisheries-independent biological surveys, these data are rarely measured at appropriate spatial scales following MPA implementation. Here, the effects of MPA implementation on local fisheries are assessed in a temperate MPA (Arrabida Marine Park, Portugal), using fisheries monitoring methods combining spatial distribution of fishing effort, on-board observations and official landings statistics at scales appropriate to the Marine Park. Fisheries spatial distribution, fishing effort, on-board data collection and official landings registered for the same vessels over time were analysed between 2004 and 2010. The applicability and reliability of using landings statistics alone was tested (i.e. when no sampling data are available) and we conclude that landings data alone only allow the identification of general patterns. The combination of landings information (which is known to be unreliable in many coastal communities) with other methods, provides an effective tool to evaluate fisheries dynamics in response to MPA implementation. As resources for monitoring socio-ecological responses to MPAs are frequently scarce, the use of landings data calibrated with fisheries information (from vessels, gear distribution and on-board data) is a valuable tool applicable to many worldwide coastal small-scale fisheries. (C) 2015 Elsevier B.V. All rights reserved.

Evidencia sobre la eficacia de cannabinoides en el dolor neuropático oncológico

Contexto: La eficacia de los cannabinoides en el dolor neuropático es desconocida. El control del dolor es determinante en los pacientes ya que genera un impacto negativo en la calidad de vida de los pacientes. Objetivo: El presente trabajo pretende demostrar la evidencia sobre la eficacia de los medicamentos cannabinoides en el control del dolor neuropático oncológico, mediante la evaluación de la literatura disponible. Metodología: Se realizó una revisión sistemática de literatura incluyendo estudios experimentales, observacionales y revisiones sistemáticas en un periodo de 15 años. Se incluyeron todos los estudios desde el años 2000 con evidencia IB según la escala de evidencia de Oxford. Resultados: Cuatro estudios cumplieron criterios para su inclusión, sin embargo la evidencia es baja y no permite recomendar o descartar los cannabinoides como terapia coadyuvante en control del dolor neuropático oncológico. La combinación de THC/CDB (Sativex®) parece ser un medicamento seguro pues no se reportaron muertes asociadas a su uso, sin embargo la presentación de eventos adversos a nivel gastrointestinal y neurológico podría aumentar el riesgo de interacciones medicamentosas y tener un impacto negativo en la calidad de vida de los pacientes oncológicos. Conclusiones: No hay suficiente literatura y la evidencia no es suficiente para recomendar o descartar el uso de los cannabinoides en dolor neuropático oncológico. Futuros estudios deben realizarse para analizar el beneficio de estos medicamentos. Aunque ética y socialmente hay resistencia para el uso de los cannabinoides, actualmente hay una gran discusión política en el mundo y en Colombia para su aceptación como terapia en el control del dolor.

Water Delivery Quality and Automatic Control Modes in Irrigation Canals. A Case Study.

Regarding canal management modernization, water savings and water delivery quality, the study presents two automatic canal control approaches of the PI (Proportional and Integral) type: the distant and the local downstream control modes. The two PI controllers are defined, tuned and tested using an hydraulic unsteady flow simulation model, particularly suitable for canal control studies. The PI control parameters are tuned using optimization tools. The simulations are done for a Portuguese prototype canal and the PI controllers are analyzed and compared considering a demand-oriented-canal operation. The paper presents and analyzes the two control modes answers for five different offtake types – gate controlled weir, gate controlled orifice, weir with or without adjustable height and automatic flow adjustable offtake. The simulation results are compared using water volumes performance indicators (considering the demanded, supplied and the effectives water volumes) and a time indicator, defined taking into account the time during which the demand discharges are effective discharges. Regarding water savings, the simulation results for the five offtake types prove that the local downstream control gives the best results (no water operational losses) and that the distant downstream control presents worse results in connection with the automatic flow adjustable offtakes. Considering the water volumes and time performance indicators, the best results are obtained for the automatic flow adjustable offtakes and the worse for the gate controlled orifices, followed by the weir with adjustable height.

Control and Supervision of Wind Energy Conversion Systems

This paper is about a PhD thesis and includes the study and analysis of the performance of an onshore wind energy conversion system. First, mathematical models of a variable speed wind turbine with pitch control are studied, followed by the study of different controller types such as integer-order controllers, fractional-order controllers, fuzzy logic controllers, adaptive controllers and predictive controllers and the study of a supervisor based on finite state machines is also studied. The controllers are included in the lower level of a hierarchical structure composed by two levels whose objective is to control the electric output power around the rated power. The supervisor included at the higher level is based on finite state machines whose objective is to analyze the operational states according to the wind speed. The studied mathematical models are integrated into computer simulations for the wind energy conversion system and the obtained numerical results allow for the performance assessment of the system connected to the electric grid. The wind energy conversion system is composed by a variable speed wind turbine, a mechanical transmission system described by a two mass drive train, a gearbox, a doubly fed induction generator rotor and by a two level converter.

Problem of Hydroelectric Resources Management: Numerical Approach

In this work we analyze an optimal control problem for a system of two hydroelectric power stations in cascade with reversible turbines. The objective is to optimize the profit of power production while respecting the system’s restrictions. Some of these restrictions translate into state constraints and the cost function is nonconvex. This increases the complexity of the optimal control problem. The problem is solved numerically and two different approaches are adopted. These approaches focus on global optimization techniques (Chen-Burer algorithm) and on a projection estimation refinement method (PERmethod). PERmethod is used as a technique to reduce the dimension of the problem. Results and execution time of the two procedures are compared.

Biological control of Sclerotinia sclerotiorum on beans in field by Trichoderma asperellum and Clonostachys rosea.

2008

Control of Fusarium in Chrysanthemum with sewage sludge, biofertilizer, hydrolyzed fish, chitosan and Trichoderma.

2008