Liquid cooling solutions for rotating permanent magnet synchronous machines

In the design of electrical machines, efficiency improvements have become very important. However, there are at least two significant cases in which the compactness of electrical machines is critical and the tolerance of extremely high losses is valued: vehicle traction, where very high torque density is desired at least temporarily; and direct-drive wind turbine generators, whose mass should be acceptably low. As ever higher torque density and ever more compact electrical machines are developed for these purposes, thermal issues, i.e. avoidance of over-temperatures and damage in conditions of high heat losses, are becoming of utmost importance. The excessive temperatures of critical machine components, such as insulation and permanent magnets, easily cause failures of the whole electrical equipment. In electrical machines with excitation systems based on permanent magnets, special attention must be paid to the rotor temperature because of the temperature-sensitive properties of permanent magnets. The allowable temperature of NdFeB magnets is usually significantly less than 150 ˚C. The practical problem is that the part of the machine where the permanent magnets are located should stay cooler than the copper windings, which can easily tolerate temperatures of 155 ˚C or 180 ˚C. Therefore, new cooling solutions should be developed in order to cool permanent magnet electrical machines with high torque density and because of it with high concentrated losses in stators. In this doctoral dissertation, direct and indirect liquid cooling techniques for permanent magnet synchronous electrical machines (PMSM) with high torque density are presented and discussed. The aim of this research is to analyse thermal behaviours of the machines using the most applicable and accurate thermal analysis methods and to propose new, practical machine designs based on these analyses. The Computational Fluid Dynamics (CFD) thermal simulations of the heat transfer inside the machines and lumped parameter thermal network (LPTN) simulations both presented herein are used for the analyses. Detailed descriptions of the simulated thermal models are also presented. Most of the theoretical considerations and simulations have been verified via experimental measurements on a copper tooth-coil (motorette) and on various prototypes of electrical machines. The indirect liquid cooling systems of a 100 kW axial flux (AF) PMSM and a 110 kW radial flux (RF) PMSM are analysed here by means of simplified 3D CFD conjugate thermal models of the parts of both machines. In terms of results, a significant temperature drop of 40 ̊C in the stator winding and 28 ̊C in the rotor of the AF PMSM was achieved with the addition of highly thermally conductive materials into the machine: copper bars inserted in the teeth, and potting material around the end windings. In the RF PMSM, the potting material resulted in a temperature decrease of 6 ̊C in the stator winding, and in a decrease of 10 ̊C in the rotor embedded-permanentmagnets. Two types of unique direct liquid cooling systems for low power machines are analysed herein to demonstrate the effectiveness of the cooling systems in conditions of highly concentrated heat losses. LPTN analysis and CFD thermal analysis (the latter being particularly useful for unique design) were applied to simulate the temperature distribution within the machine models. Oil-immersion cooling provided good cooling capability for a 26.6 kW PMSM of a hybrid vehicle. A direct liquid cooling system for the copper winding with inner stainless steel tubes was designed for an 8 MW directdrive PM synchronous generator. The design principles of this cooling solution are described in detail in this thesis. The thermal analyses demonstrate that the stator winding and the rotor magnet temperatures are kept significantly below their critical temperatures with demineralized water flow. A comparison study of the coolant agents indicates that propylene glycol is more effective than ethylene glycol in arctic conditions.

Transient stability indices from a hybrid approach

This paper deals with hybrid method for transient stability analysis combining time domain simulation and a direct method. Nowadays, the step-by-step simulation is the best available tool for allowing the uses of detailed models and for providing reliable results. The main limitation of this approach involves the large time of computational simulations and the absence of stability margin. On the other hand, direct methods, that demand less CPU time, did not show ample reliability and applicability yet. The best way seems to be using hybrid solutions, in which a direct method is incorporated in a time domain simulation tool. This work has studied a direct method using the transient potential and kinetic energy of the critical machine only. In this paper the critical machine is identified by a fast and efficient method, and the proposal is new for using to get stability margins from hybrid approaches. Results from systems, like 16-machine, show stability indices to dynamic security assessment. © 2001 IEEE.

Utilização de índices de estabilidade transitória para redespacho de geração

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

InUse – Intelligente Unterlegscheibe zur Messung der Kräfte in Schraubverbindungen

Die lösbare Verbindung von mechanischen Komponenten innerhalb von Maschinen und Anlagen wird zumeist über Schraubverbindungen realisiert. Um eine ausreichende Verbindung zu erzielen werden derzeit die Klemmkräfte durch ein definiertes Anzugsmoment aufgebracht. Bei der Berechnung des Momentes entsprechend der benötigten Kraft finden Reibbeiwerte innerhalb der Schraubverbindung Berücksichtigung, welche nur bedingt ermittelt werden können. In kritischen Maschinenkomponenten wird daher eine Überdimensionierung der Schrauben zur Erhöhung der Sicherheit akzeptiert. Die direkte Kraftmessung mittels einer intelligenten Unterlegscheibe und eine transpondergestützte Datenübertragung ermöglicht es, die Kräfte genau einzustellen und somit die Sicherheit zu erhöhen.

Critical feature analysis of a radiotherapy machine

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An `assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Modelling capability and affordance as properties of human/machine resource systems

Understanding how and why the capability of one set of business resources, its structural arrangements and mechanisms compared to another works can provide competitive advantage in terms of new business processes and product and service development. However, most business models of capability are descriptive and lack formal modelling language to qualitatively and quantifiably compare capabilities, Gibson’s theory of affordance, the potential for action, provides a formal basis for a more robust and quantitative model, but most formal affordance models are complex and abstract and lack support for real-world applications. We aim to understand the ‘how’ and ‘why’ of business capability, by developing a quantitative and qualitative model that underpins earlier work on Capability-Affordance Modelling – CAM. This paper integrates an affordance based capability model and the formalism of Coloured Petri Nets to develop a simulation model. Using the model, we show how capability depends on the space time path of interacting resources, the mechanism of transition and specific critical affordance factors relating to the values of the variables for resources, people and physical objects. We show how the model can identify the capabilities of resources to enable the capability to inject a drug and anaesthetise a patient.

Soil compaction by machine traffic and least limiting water range related to soybean yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Safety illusion and error trap in a collectively-operated machine accident

Workplace accidents involving machines are relevant for their magnitude and their impacts on worker health. Despite consolidated critical statements, explanation centered on errors of operators remains predominant with industry professionals, hampering preventive measures and the improvement of production-system reliability. Several initiatives were adopted by enforcement agencies in partnership with universities to stimulate production and diffusion of analysis methodologies with a systemic approach. Starting from one accident case that occurred with a worker who operated a brake-clutch type mechanical press, the article explores cognitive aspects and the existence of traps in the operation of this machine. It deals with a large-sized press that, despite being endowed with a light curtain in areas of access to the pressing zone, did not meet legal requirements. The safety devices gave rise to an illusion of safety, permitting activation of the machine when a worker was still found within the operational zone. Preventive interventions must stimulate the tailoring of systems to the characteristics of workers, minimizing the creation of traps and encouraging safety policies and practices that replace judgments of behaviors that participate in accidents by analyses of reasons that lead workers to act in that manner.