Esikiristetyn pulttiliitoksen vaikutus hitsausmuodonmuutoksiin

Tässä työssä tutkitaan propulsioyksikön kiinnitysrenkaan pulttiliitosten vaikutusta asen-nushitsauksesta aiheutuviin hitsausmuodonmuutoksiin. Hitsausmuodonmuutoksissa tutki-taan tärkeimpinä kohtina asennuslohkossa laakerin rajapintaa sekä kääntömoottorin kiinni-tyspintaa. Tutkimuksessa asennuslohkon hitsaaminen ja muodonmuutosten arvioiminen toteutettiin käyttämällä epälineaarista elementtimenetelmää. Ensisijaisena tavoitteena työssä on tutkia esikiristettyjen pulttiliitoksien vaikutusta raken-teen muodonmuutoksiin ja pohtia aiheutuvien siirtymien perusteella pulttien tarpeellisuutta rakenteessa. Tämän lisäksi vertaillaan pultillisen ja pultittoman kiinnitystavan eroavaisuuk-sia tuloksia analysoimalla. Saatujen tuloksien perusteella radiaaliset ja aksiaaliset siirtymät eivät olleet riittävän suuria aiheuttamaan haittoja rakenteen toimivuudelle kummassakaan mallissa. Lisäanalyysejä tar-kemmalla lämmöntuonnilla voidaan pitää tarvittavana pulttiliitoksien tarpeellisuuden tar-kemman testaamisen vuoksi.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

Cost Reduction of Permanent Magnet Synchronous Machines

Electrical machine drives are the most electrical energy-consuming systems worldwide. The largest proportion of drives is found in industrial applications. There are, however many other applications that are also based on the use of electrical machines, because they have a relatively high efficiency, a low noise level, and do not produce local pollution. Electrical machines can be classified into several categories. One of the most commonly used electrical machine types (especially in the industry) is induction motors, also known as asynchronous machines. They have a mature production process and a robust rotor construction. However, in the world pursuing higher energy efficiency with reasonable investments not every application receives the advantage of using this type of motor drives. The main drawback of induction motors is the fact that they need slipcaused and thus loss-generating current in the rotor, and additional stator current for magnetic field production along with the torque-producing current. This can reduce the electric motor drive efficiency, especially in low-speed, low-power applications. Often, when high torque density is required together with low losses, it is desirable to apply permanent magnet technology, because in this case there is no need to use current to produce the basic excitation of the machine. This promotes the effectiveness of copper use in the stator, and further, there is no rotor current in these machines. Again, if permanent magnets with a high remanent flux density are used, the air gap flux density can be higher than in conventional induction motors. These advantages have raised the popularity of PMSMs in some challenging applications, such as hybrid electric vehicles (HEV), wind turbines, and home appliances. Usually, a correctly designed PMSM has a higher efficiency and consequently lower losses than its induction machine counterparts. Therefore, the use of these electrical machines reduces the energy consumption of the whole system to some extent, which can provide good motivation to apply permanent magnet technology to electrical machines. However, the cost of high performance rare earth permanent magnets in these machines may not be affordable in many industrial applications, because the tight competition between the manufacturers dictates the rules of low-cost and highly robust solutions, where asynchronous machines seem to be more feasible at the moment. Two main electromagnetic components of an electrical machine are the stator and the rotor. In the case of a conventional radial flux PMSM, the stator contains magnetic circuit lamination and stator winding, and the rotor consists of rotor steel (laminated or solid) and permanent magnets. The lamination itself does not significantly influence the total cost of the machine, even though it can considerably increase the construction complexity, as it requires a special assembly arrangement. However, thin metal sheet processing methods are very effective and economically feasible. Therefore, the cost of the machine is mainly affected by the stator winding and the permanent magnets. The work proposed in this doctoral dissertation comprises a description and analysis of two approaches of PMSM cost reduction: one on the rotor side and the other on the stator side. The first approach on the rotor side includes the use of low-cost and abundant ferrite magnets together with a tooth-coil winding topology and an outer rotor construction. The second approach on the stator side exploits the use of a modular stator structure instead of a monolithic one. PMSMs with the proposed structures were thoroughly analysed by finite element method based tools (FEM). It was found out that by implementing the described principles, some favourable characteristics of the machine (mainly concerning the machine size) will inevitable be compromised. However, the main target of the proposed approaches is not to compete with conventional rare earth PMSMs, but to reduce the price at which they can be implemented in industrial applications, keeping their dimensions at the same level or lower than those of a typical electrical machine used in the industry at the moment. The measurement results of the prototypes show that the main performance characteristics of these machines are at an acceptable level. It is shown that with certain specific actions it is possible to achieve a desirable efficiency level of the machine with the proposed cost reduction methods.

Meesauunin vaipan konepajavalmistus ja laaduntarkastus

Meesauuni on osa sulfaattisellutehdasta ja sen kemikaalikiertoa. Se on pyörivä kaltevaan tasoon asetettu rumpu-uuni, joka voi olla jopa 160 metriä pitkä ja halkaisijaltaan 5,5 metriä. Kalkki on kiertävä apukemikaali, jota käytetään soodakattilalta tulevan viherlipeän muuttamiseen valkolipeäksi. Meesauunin tehtävänä on kierrättää kalkki (CaO) uudelleen käytettäväksi kaustisoinnissa syntyneestä meesasta (CaCO3). Meesauunin vaipan konepajavalmistus on prosessina hyvin yksinkertainen, mutta toleranssivaatimukset ovat hyvin tiukat suhteutettuna meesauunin kokoon. Vaippalohkojen valmistus on siirtynyt halpatyövoiman maihin lähelle loppukäyttäjiä, joten vaatimukset piirustusten laadulle, valmistukselle, ohjeille ja tarkastamiselle ovat lisääntyneet. Uunin vaippa toimitetaan asennuspaikalle useassa lohkossa ja jokainen vaippalohko on tarkastettava ennen toimitusta. Virheellisten vaippalohkojen siirtyminen asennuspaikalle on estettävä. Työn tavoitteena oli parantaa meesauunin vaippalohkojen konepajavalmistuksen laaduntarkastusta. Tässä työssä tutkitaan mittausmenetelmiä vaippalohkojen geometrian mittaamiseen. Tärkeimmät uunin toiminnallisiin ominaisuuksiin vaikuttavat muototoleranssit vaippalohkoille ovat ympyrämäisyys ja keskiviivan suoruus. Virheet näissä toleransseissa aiheuttavat vaurioita uunin muurauksille ja liian suuria kuormituksia tuennoille. Vaippalohkot on mitattava pyöritysrullaston päällä ja konepajan olosuhteissa, mikä aiheuttaa omat haasteensa. Vaippalohkojen suuret massat ja dimensiot aiheuttavat vaippalohkoihin muodonmuutoksia. Muodonmuutokset täytyy olla hallinnassa, mikäli halutaan käyttää CMS-laitteistoja (Coordinate Measuring System). Meesauunin vaippalohkot ovat mitattavissa radiaalimittauksina tai käyttäen CMS-laitteistoja.

Spectral Retinal Images Reconstruction

While red-green-blue (RGB) image of retina has quite limited information, retinal multispectral images provide both spatial and spectral information which could enhance the capability of exploring the eye-related problems in their early stages. In this thesis, two learning-based algorithms for reconstructing of spectral retinal images from the RGB images are developed by a two-step manner. First, related previous techniques are reviewed and studied. Then, the most suitable methods are enhanced and combined to have new algorithms for the reconstruction of spectral retinal images. The proposed approaches are based on radial basis function network to learn a mapping from tristimulus colour space to multi-spectral space. The resemblance level of reproduced spectral images and original images is estimated using spectral distance metrics spectral angle mapper, spectral correlation mapper, and spectral information divergence, which show a promising result from the suggested algorithms.