Working fluid selection and design of small-scale waste heat recovery systems based on organic Rankine cycles

Demand for the use of energy systems, entailing high efficiency as well as availability to harness renewable energy sources, is a key issue in order to tackling the threat of global warming and saving natural resources. Organic Rankine cycle (ORC) technology has been identified as one of the most promising technologies in recovering low-grade heat sources and in harnessing renewable energy sources that cannot be efficiently utilized by means of more conventional power systems. The ORC is based on the working principle of Rankine process, but an organic working fluid is adopted in the cycle instead of steam. This thesis presents numerical and experimental results of the study on the design of small-scale ORCs. Two main applications were selected for the thesis: waste heat re- covery from small-scale diesel engines concentrating on the utilization of the exhaust gas heat and waste heat recovery in large industrial-scale engine power plants considering the utilization of both the high and low temperature heat sources. The main objective of this work was to identify suitable working fluid candidates and to study the process and turbine design methods that can be applied when power plants based on the use of non-conventional working fluids are considered. The computational work included the use of thermodynamic analysis methods and turbine design methods that were based on the use of highly accurate fluid properties. In addition, the design and loss mechanisms in supersonic ORC turbines were studied by means of computational fluid dynamics. The results indicated that the design of ORC is highly influenced by the selection of the working fluid and cycle operational conditions. The results for the turbine designs in- dicated that the working fluid selection should not be based only on the thermodynamic analysis, but requires also considerations on the turbine design. The turbines tend to be fast rotating, entailing small blade heights at the turbine rotor inlet and highly supersonic flow in the turbine flow passages, especially when power systems with low power outputs are designed. The results indicated that the ORC is a potential solution in utilizing waste heat streams both at high and low temperatures and both in micro and larger scale appli- cations.

Verification of a dynamic laboratory scale former

Laboratoriomittakaavainen formeri on välttämätön, jotta paperinvalmistusprosessin jäljitteleminen olisi mahdollista. Vaikka erilaisia formereita löytyykin paperiteollisuudesta, tilaa on kuitenkin laboratoriomittakaavaiselle paperinvalmistusmenetelmälle, joka sijoittuisipilottikoneen ja perinteisen laboratorioarkkimuotin välille. Formeri, jolla saadaan aikaiseksi oikean paperinvalmistuksen kaltaiset olosuhteet ja ilmiöt on kehitetty, ja sen toiminta on testattu Nalcon Papermaking Centreof Excellence:ssä Espoossa. Formeri on yhdistetty Nalcon lähestymisjärjetelmäsimulaattoriin ja simulaattorilla aikaansaadut hydro-kemialliset ilmiöt voidaan testata nyt myös arkeista. Laitteessa on perälaatikko ja viiraosa. Perälaatikosta massa virtaa viiralle, joka liikkuu eteenpäin hihnakuljettimen hihnojen päällä. Suihku-viira -suhdetta voidaan muuttaa joko muuttamalla virtausnopeutta tai viiran nopeutta tai säätämällä perälaatikon huuliaukkoa. Formerintoiminnan testaus osoitti, että se toimii teknisesti hyvin ja tulokset ovat toistettavia ja loogisia. Arkeissa kuidut ovat orientoituneet, formaatio ja vetolujuussuhde KS/PS riippuvat voimakkaasti suihku-viira -suhteesta, kuten oikeillakinpaperikoneilla.

Modeling of Liquid-Solid Flow in Industrial Scale

In the present work, liquid-solid flow in industrial scale is modeled using the commercial software of Computational Fluid Dynamics (CFD) ANSYS Fluent 14.5. In literature, there are few studies on liquid-solid flow in industrial scale, but any information about the particular case with modified geometry cannot be found. The aim of this thesis is to describe the strengths and weaknesses of the multiphase models, when a large-scale application is studied within liquid-solid flow, including the boundary-layer characteristics. The results indicate that the selection of the most appropriate multiphase model depends on the flow regime. Thus, careful estimations of the flow regime are recommended to be done before modeling. The computational tool is developed for this purpose during this thesis. The homogeneous multiphase model is valid only for homogeneous suspension, the discrete phase model (DPM) is recommended for homogeneous and heterogeneous suspension where pipe Froude number is greater than 1.0, while the mixture and Eulerian models are able to predict also flow regimes, where pipe Froude number is smaller than 1.0 and particles tend to settle. With increasing material density ratio and decreasing pipe Froude number, the Eulerian model gives the most accurate results, because it does not include simplifications in Navier-Stokes equations like the other models. In addition, the results indicate that the potential location of erosion in the pipe depends on material density ratio. Possible sedimentation of particles can cause erosion and increase pressure drop as well. In the pipe bend, especially secondary flows, perpendicular to the main flow, affect the location of erosion.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Clinical Learning Environment and Supervision. Development and Validation of the CLES Evaluation Scale

The purpose of the study is: (1) to describe how nursing students' experienced their clinical learning environment and the supervision given by staff nurses working in hospital settings; and (2) to develop and test an evaluation scale of Clinical Learning Environment and Supervision (CLES). The study has been carried out in different phases. The pilot study (n=163) explored the association between the characteristics of a ward and its evaluation as a learning environment by students. The second version of research instrument (which was developed by the results of this pilot study) were tested by an expert panel (n=9 nurse teachers) and test-retest group formed by student nurses (n=38). After this evaluative phase, the CLES was formed as the basic research instrument for this study and it was tested with the Finnish main sample (n=416). In this phase, a concurrent validity instrument (Dunn & Burnett 1995) was used to confirm the validation process of CLES. The international comparative study was made by comparing the Finnish main sample with a British sample (n=142). The international comparative study was necessary for two reasons. In the instrument developing process, there is a need to test the new instrument in some other nursing culture. Other reason for comparative international study is the reflecting the impact of open employment markets in the European Union (EU) on the need to evaluate and to integrate EU health care educational systems. The results showed that the individualised supervision system is the most used supervision model and the supervisory relationship with personal mentor is the most meaningful single element of supervision evaluated by nursing students. The ward atmosphere and the management style of ward manager are the most important environmental factors of the clinical ward. The study integrates two theoretical elements - learning environment and supervision - in developing a preliminary theoretical model. The comparative international study showed that, Finnish students were more satisfied and evaluated their clinical placements and supervision with higher scores than students in the United Kingdom (UK). The difference between groups was statistical highly significant (p= 0.000). In the UK, clinical placements were longer but students met their nurse teachers less frequently than students in Finland. Arrangements for supervision were similar. This research process has produced the evaluation scale (CLES), which can be used in research and quality assessments of clinical learning environment and supervision in Finland and in the UK. CLES consists of 27 items and it is sub-divided into five sub-dimensions. Cronbach's alpha coefficient varied from high 0.94 to marginal 0.73. CLES is a compact evaluation scale and user-friendliness makes it suitable for continuing evaluation.

Keskoslasten motorinen kehitys : Alberta Infant Motor Scale (AIMS) -testistöllä arvioituna

Opinnäytetyön tarkoituksena oli kuvata alle 1 500 gramman painoisena syntyneiden keskoslasten motorista kehitystä kolmen, kuuden ja kahdentoista kuukauden korjatussa iässä, sekä tuoda esille mahdollisia motorisen kehityksen yhteisiä piirteitä Alberta Infant Motor Scale (AIMS) -testistöllä arvioituna. Työ toteutettiin yhteistyössä Lasten ja nuorten sairauksien toimialan fysioterapian yksikön kanssa, jossa keskoslasten motorisen kehityksen arviointi AIMS-testistöllä oli toteutettu vuosina 2005 - 2006. Idea opinnäytetyöhön syntyi yhteisten keskusteluiden pohjalta fysioterapeuttien kanssa. Opinnäytetyön tavoitteena oli analysoida ja koota yhteenveto Lasten ja nuorten sairauksien toimialalle heidän tutkimastaan aineistosta. Työ oli luonteeltaan kuvaileva kvantitatiivinen tutkimus valmiiksi saadun aineiston pohjalta. Aineisto koostui yhteensä 109 keskoslapsen AIMS-testistön arviointilomakkeista. Keskoslapsista 54 oli kolmen kuukauden, 42 kuuden kuukauden ja 13 kahdentoista kuukauden korjatussa iässä. Tulokset analysoitiin käyttämällä SPSS 13.0 Windows Release-tilasto-ohjelmaa ja tulokset esitettiin taulukoiden ja kuvioiden avulla. Tiedonkeruumenetelminä käytimme kirjallisuuden lisäksi uusimpia tutkimusartikkeleita sekä asiantuntijahaastattelua. Kolmen kuukauden ikäisistä keskoslapsista 51 sijoittui AIMS-testistön motorista kehitystä kuvaaville käyrille. Kolme lasta jäi käyrien alapuolelle. Kuuden kuukauden ikäisten keskoslasten kokonaispistemäärissä oli enemmän hajontaa. 15 lasta jäi AIMS-testistön motorista kehitystä kuvaavien käyrien alapuolelle. Kahdentoista kuukauden ikäisistä lapsista yhdeksän sijoittui motorista kehitystä kuvaaville käyrille ja neljä lasta jäi käyrien alapuolelle. Yhteisenä piirteenä kaikilta kolmen kuukauden ikäisiltä ja 14 kuuden kuukauden ikäiseltä lapselta puuttui taito tukeutua yläraajoihin istuma-asennossa (Sitting With Propped Arms). Tutkimustulosten perusteella kolmen kuukauden ikäisten keskoslasten motorinen kehitys oli valtaosalla (51/54) ikätasoista. Kuuden ja kahdentoista kuukauden ikäisten keskoslasten motorisessa kehityksessä yksilölliset erot olivat suurempia. Tutkimusjoukkomme keskoslapsista motorinen kehitys oli ikätasoa heikompaa 22 keskoslapsella. Lasten ja nuorten sairauksien toimiala saa käyttöönsä työmme tulokset, joita voidaan hyödyntää keskoslasten motorisen kehityksen seurannassa sekä fysioterapian kehittämisessä. Työmme lisää AIMS-testistön tunnettavuutta ja siitä on myös laajemmin hyötyä lasten parissa työskenteleville fysioterapeuteille.

Finnish embryo transfer breeding program "ASMO" : description of the goals and a summary of the results of initial selection

Selostus: Alkionsiirtojalostusohjelma "ASMO", sen tavoitteet ja yhteenveto alkuvalinnan tuloksista

Selection of Salix myrsinifolia clones for biomass forestry in Finland

Summary