LVDC power distribution system: computational modelling

In the doctoral dissertation, low-voltage direct current (LVDC) distribution system stability, supply security and power quality are evaluated by computational modelling and measurements on an LVDC research platform. Computational models for the LVDC network analysis are developed. Time-domain simulation models are implemented in the time-domain simulation environment PSCAD/EMTDC. The PSCAD/EMTDC models of the LVDC network are applied to the transient behaviour and power quality studies. The LVDC network power loss model is developed in a MATLAB environment and is capable of fast estimation of the network and component power losses. The model integrates analytical equations that describe the power loss mechanism of the network components with power flow calculations. For an LVDC network research platform, a monitoring and control software solution is developed. The solution is used to deliver measurement data for verification of the developed models and analysis of the modelling results. In the work, the power loss mechanism of the LVDC network components and its main dependencies are described. Energy loss distribution of the LVDC network components is presented. Power quality measurements and current spectra are provided and harmonic pollution on the DC network is analysed. The transient behaviour of the network is verified through time-domain simulations. DC capacitor guidelines for an LVDC power distribution network are introduced. The power loss analysis results show that one of the main optimisation targets for an LVDC power distribution network should be reduction of the no-load losses and efficiency improvement of converters at partial loads. Low-frequency spectra of the network voltages and currents are shown, and harmonic propagation is analysed. Power quality in the LVDC network point of common coupling (PCC) is discussed. Power quality standard requirements are shown to be met by the LVDC network. The network behaviour during transients is analysed by time-domain simulations. The network is shown to be transient stable during large-scale disturbances. Measurement results on the LVDC research platform proving this are presented in the work.

Diclofenac plasma protein binding: PK-PD modelling in cardiac patients submitted to cardiopulmonary bypass

Twenty-four surgical patients of both sexes without cardiac, hepatic, renal or endocrine dysfunctions were divided into two groups: 10 cardiac surgical patients submitted to myocardial revascularization and cardiopulmonary bypass (CPB), 3 females and 7 males aged 65 ± 11 years, 74 ± 16 kg body weight, 166 ± 9 cm height and 1.80 ± 0.21 m2 body surface area (BSA), and control, 14 surgical patients not submitted to CPB, 11 female and 3 males aged 41 ± 14 years, 66 ± 14 kg body weight, 159 ± 9 cm height and 1.65 ± 0.16 m2 BSA (mean ± SD). Sodium diclofenac (1 mg/kg, im Voltaren 75® twice a day) was administered to patients in the Recovery Unit 48 h after surgery. Venous blood samples were collected during a period of 0-12 h and analgesia was measured by the visual analogue scale (VAS) during the same period. Plasma diclofenac levels were measured by high performance liquid chromatography. A two-compartment open model was applied to obtain the plasma decay curve and to estimate kinetic parameters. Plasma diclofenac protein binding decreased whereas free plasma diclofenac levels were increased five-fold in CPB patients. Data obtained for analgesia reported as the maximum effect (EMAX) were: 25% VAS (CPB) vs 10% VAS (control), P<0.05, median measured by the visual analogue scale where 100% is equivalent to the highest level of pain. To correlate the effect versus plasma diclofenac levels, the EMAX sigmoid model was applied. A prolongation of the mean residence time for maximum effect (MRTEMAX) was observed without any change in lag-time in CPB in spite of the reduced analgesia reported for these patients, during the time-dose interval. In conclusion, the extent of plasma diclofenac protein binding was influenced by CPB with clinically relevant kinetic-dynamic consequences

CFD modeling of dispersion water feed in wastewater cleaning application

Fluid particle breakup and coalescence are important phenomena in a number of industrial flow systems. This study deals with a gas-liquid bubbly flow in one wastewater cleaning application. Three-dimensional geometric model of a dispersion water system was created in ANSYS CFD meshing software. Then, numerical study of the system was carried out by means of unsteady simulations performed in ANSYS FLUENT CFD software. Single-phase water flow case was setup to calculate the entire flow field using the RNG k-epsilon turbulence model based on the Reynolds-averaged Navier-Stokes (RANS) equations. Bubbly flow case was based on a computational fluid dynamics - population balance model (CFD-PBM) coupled approach. Bubble breakup and coalescence were considered to determine the evolution of the bubble size distribution. Obtained results are considered as steps toward optimization of the cleaning process and will be analyzed in order to make the process more efficient.

Analysis and modelling of chemical looping combustion process with and without oxygen uncoupling

Effective control and limiting of carbon dioxide (CO₂) emissions in energy production are major challenges of science today. Current research activities include the development of new low-cost carbon capture technologies, and among the proposed concepts, chemical combustion (CLC) and chemical looping with oxygen uncoupling (CLOU) have attracted significant attention allowing intrinsic separation of pure CO₂ from a hydrocarbon fuel combustion process with a comparatively small energy penalty. Both CLC and CLOU utilize the well-established fluidized bed technology, but several technical challenges need to be overcome in order to commercialize the processes. Therefore, development of proper modelling and simulation tools is essential for the design, optimization, and scale-up of chemical looping-based combustion systems. The main objective of this work was to analyze the technological feasibility of CLC and CLOU processes at different scales using a computational modelling approach. A onedimensional fluidized bed model frame was constructed and applied for simulations of CLC and CLOU systems consisting of interconnected fluidized bed reactors. The model is based on the conservation of mass and energy, and semi-empirical correlations are used to describe the hydrodynamics, chemical reactions, and transfer of heat in the reactors. Another objective was to evaluate the viability of chemical looping-based energy production, and a flow sheet model representing a CLC-integrated steam power plant was developed. The 1D model frame was succesfully validated based on the operation of a 150 kWth laboratory-sized CLC unit fed by methane. By following certain scale-up criteria, a conceptual design for a CLC reactor system at a pre-commercial scale of 100 MWth was created, after which the validated model was used to predict the performance of the system. As a result, further understanding of the parameters affecting the operation of a large-scale CLC process was acquired, which will be useful for the practical design work in the future. The integration of the reactor system and steam turbine cycle for power production was studied resulting in a suggested plant layout including a CLC boiler system, a simple heat recovery setup, and an integrated steam cycle with a three pressure level steam turbine. Possible operational regions of a CLOU reactor system fed by bituminous coal were determined via mass, energy, and exergy balance analysis. Finally, the 1D fluidized bed model was modified suitable for CLOU, and the performance of a hypothetical 500 MWth CLOU fuel reactor was evaluated by extensive case simulations.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Korkeapainevesisumusuuttimen virtaustekninen laadunhallinta

Tämä työ vastaa tarpeeseen hallita korkeapainevesisumusuuttimen laatua virtausmekaniikan työkalujen avulla. Työssä tutkitaan suutinten testidatan lisäksi virtauksen käyttäytymistä suuttimen sisällä CFD-laskennan avulla. Virtausmallinnus tehdään Navier-Stokes –pohjaisella laskentamenetelmällä. Työn teoriaosassa käsitellään virtaustekniikkaa ja sen kehitystä yleisesti. Lisäksi esitetään suuttimen laskennassa käytettävää perusteoriaa sekä teknisiä ratkaisuja. Teoriaosassa käydään myös läpi laskennalliseen virtausmekaniikkaan (CFD-laskenta) liittyvää perusteoriaa. Tutkimusosiossa esitetään käsitellyt suutintestitulokset sekä mallinnetaan suutinvirtausta ajasta riippumattomaan virtauslaskentaan perustuvalla laskentamenetelmällä. Virtauslaskennassa käytetään OpenFOAM-laskentaohjelmiston SIMPLE-virtausratkaisijaa sekä k-omega SST –turbulenssimallia. Tehtiin virtausmallinnus kaikilla paineilla, joita suuttimen testauksessa myös todellisuudessa käytetään. Lisäksi selvitettiin mahdolliset kavitaatiokohdat suuttimessa ja suunniteltiin kavitaatiota ehkäisevä suutingeometria. Todettiin myös lämpötilan ja epäpuhtauksien vaikuttavan kavitaatioon sekä mallinnettiin lämpötilan vaikutusta. Luotiin malli, jolla suuttimen suunnitteluun liittyviin haasteisiin voidaan vastata numeerisella laskennalla.

Modelling the super-equilibria in thermal biomass conversion : applications and limitations of the constrained free energy method

Bioenergi ses som en viktig del av det nu- och framtida sortimentet av inhemsk energi. Svartlut, bark och skogsavfall täcker mer än en femtedel av den inhemska energianvändningen. Produktionsanläggningar kan fungera ofullständigt och en mängd gas-, partikelutsläpp och tjära produceras samtidigt och kan leda till beläggningsbildning och korrosion. Orsaken till dessa problem är ofta obalans i processen: vissa föreningar anrikas i processen och superjämviktstillstånd är bildas. I denna doktorsavhandling presenteras en ny beräkningsmetod, med vilken man kan beskriva superjämviktstillståndet, de viktigaste kemiska reaktionerna, processens värmeproduktion och tillståndsstorheter samtidigt. Beräkningsmetoden grundar sig på en unik frienergimetod med bivillkor som har utvecklats vid VTT. Den här så kallade CFE-metoden har tidigare utnyttjats i pappers-, metall- och kemiindustrin. Applikationer för bioenergi, vilka är demonstrerade i doktorsavhandlingen, är ett nytt användingsområde för metoden. Studien visade att beräkningsmetoden är väl lämpad för högtemperaturenergiprocesser. Superjämviktstillstånden kan uppstå i dessa processer och det kemiska systemet kan definieras med några bivillkor. Typiska tillämpningar är förbränning av biomassa och svartlut, förgasning av biomassa och uppkomsten av kväveoxider. Också olika sätt att definiera superjämviktstillstånd presenterades i doktorsavhandlingen: empiriska konstanter, empiriska hastighetsuttryck eller reaktionsmekanismer kan användas. Resultaten av doktorsavhandlingen kan utnyttjas i framtiden i processplaneringen och i undersökning av nya tekniska lösningar för förgasning, förbränningsteknik och biobränslen. Den presenterade metoden är ett bra alternativ till de traditionella mekanistiska och fenomenmodeller och kombinerar de bästa delarna av både. --------------------------------------------------------------- Bioenergia on tärkeä osa nykyistä ja tulevaa kotimaista energiapalettia. Mustalipeä, kuori ja metsätähteet kattavat yli viidenneksen kotimaisesta energian kulutuksesta. Tuotantolaitokset eivät kuitenkaan aina toimi täydellisesti ja niiden prosesseissa syntyy erilaisia kaasu- ja hiukkaspäästöjä, tervoja sekä prosessilaitteita kuluttavia saostumia ja ruostumista. Usein syy näihin ongelmiin on prosessissa esiintyvä epätasapainotila: tietyt yhdisteet rikastuvat prosessissa ja muodostavat supertasapainotiloja. Väitöstyössä kehitettiin uusi laskentamenetelmä, jolla voidaan kuvata nämä supertasapainotilat, tärkeimmät niihin liittyvät kemialliset reaktiot, prosessin lämmöntuotanto ja tilansuureet yhtä aikaa. Laskentamenetelmä perustuu VTT:llä kehitettyyn ainutlaatuiseen rajoitettuun vapaaenergiamenetelmään. Tätä niin kutsuttua CFE-menetelmää on aiemmin sovelluttu onnistuneesti muun muassa paperi-, metalli- ja kemianteollisuudessa. Väitöstyössä esitetyt bioenergiasovellukset ovat uusi sovellusalue menetelmälle. Työ osoitti laskentatavan soveltuvan hyvin korkealämpöisiin energiatekniikan prosesseihin, joissa kemiallista systeemiä rajoittavia tekijöitä oli rajallinen määrä ja siten super-tasapainotila saattoi muodostua prosessin aikana. Tyypillisiä sovelluskohteita ovat biomassan ja mustalipeän poltto, biomassan kaasutus ja typpioksidipäästöt. Työn aikana arvioitiin myös erilaisia tapoja määritellä super-tasapainojen muodostumista rajoittavat tekijät. Rajoitukset voitiin tehdä teollisiin mittauksiin pohjautuen, kokeellisia malleja hyödyntäen tai mekanistiseen reaktiokinetiikkaan perustuen. Tulevaisuudessa väitöstyön tuloksia voidaan hyödyntää prosessisuunnittelussa ja tutkittaessa uusia teknisiä ratkaisuja kaasutus- ja polttotekniikoissa sekä biopolttoaineiden tutkimuksessa. Kehitetty menetelmä tarjoaa hyvän vaihtoehdon perinteisille mekanistisille ja ilmiömalleille yhdistäen näiden parhaita puolia.

Remote sensing for three-dimensional modelling of hydromorphology

Successful management of rivers requires an understanding of the fluvial processes that govern them. This, in turn cannot be achieved without a means of quantifying their geomorphology and hydrology and the spatio-temporal interactions between them, that is, their hydromorphology. For a long time, it has been laborious and time-consuming to measure river topography, especially in the submerged part of the channel. The measurement of the flow field has been challenging as well, and hence, such measurements have long been sparse in natural environments. Technological advancements in the field of remote sensing in the recent years have opened up new possibilities for capturing synoptic information on river environments. This thesis presents new developments in fluvial remote sensing of both topography and water flow. A set of close-range remote sensing methods is employed to eventually construct a high-resolution unified empirical hydromorphological model, that is, river channel and floodplain topography and three-dimensional areal flow field. Empirical as well as hydraulic theory-based optical remote sensing methods are tested and evaluated using normal colour aerial photographs and sonar calibration and reference measurements on a rocky-bed sub-Arctic river. The empirical optical bathymetry model is developed further by the introduction of a deep-water radiance parameter estimation algorithm that extends the field of application of the model to shallow streams. The effect of this parameter on the model is also assessed in a study of a sandy-bed sub-Arctic river using close-range high-resolution aerial photography, presenting one of the first examples of fluvial bathymetry modelling from unmanned aerial vehicles (UAV). Further close-range remote sensing methods are added to complete the topography integrating the river bed with the floodplain to create a seamless high-resolution topography. Boat- cart- and backpack-based mobile laser scanning (MLS) are used to measure the topography of the dry part of the channel at a high resolution and accuracy. Multitemporal MLS is evaluated along with UAV-based photogrammetry against terrestrial laser scanning reference data and merged with UAV-based bathymetry to create a two-year series of seamless digital terrain models. These allow the evaluation of the methodology for conducting high-resolution change analysis of the entire channel. The remote sensing based model of hydromorphology is completed by a new methodology for mapping the flow field in 3D. An acoustic Doppler current profiler (ADCP) is deployed on a remote-controlled boat with a survey-grade global navigation satellite system (GNSS) receiver, allowing the positioning of the areally sampled 3D flow vectors in 3D space as a point cloud and its interpolation into a 3D matrix allows a quantitative volumetric flow analysis. Multitemporal areal 3D flow field data show the evolution of the flow field during a snow-melt flood event. The combination of the underwater and dry topography with the flow field yields a compete model of river hydromorphology at the reach scale.

Integrated energy storage tank for large scale power-to-gas applications

The global interest towards renewable energy production such as wind and solar energy is increasing, which in turn calls for new energy storage concepts due to the larger share of intermittent energy production. Power-to-gas solutions can be utilized to convert surplus electricity to chemical energy which can be stored for extended periods of time. The energy storage concept explored in this thesis is an integrated energy storage tank connected to an oxy-fuel combustion plant. Using this approach, flue gases from the plant could be fed directly into the storage tank and later converted into synthetic natural gas by utilizing electrolysis-methanation route. This work utilizes computational fluid dynamics to model the desublimation of carbon dioxide inside a storage tank containing cryogenic liquid, such as liquefied natural gas. Numerical modelling enables the evaluation of the transient flow patterns caused by the desublimation, as well as general fluid behaviour inside the tank. Based on simulations the stability of the cryogenic storage and the magnitude of the key parameters can be evaluated.

Modelling Cutting States in Rough Turning of 34CrNiMo6 Steel

Rough turning is an important form of manufacturing cylinder-symmetric parts. Thus far, increasing the level of automation in rough turning has included process monitoring methods or adaptive turning control methods that aim to keep the process conditions constant. However, in order to improve process safety, quality and efficiency, an adaptive turning control should be transformed into an intelligent machining system optimizing cutting values to match process conditions or to actively seek to improve process conditions. In this study, primary and secondary chatter and chip formation are studied to understand how to measure the effect of these phenomena to the process conditions and how to avoid undesired cutting conditions. The concept of cutting state is used to address the combination of these phenomena and the current use of the power capacity of the lathe. The measures to the phenomena are not developed based on physical measures, but instead, the severity of the measures is modelled against expert opinion. Based on the concept of cutting state, an expert system style fuzzy control system capable of optimizing the cutting process was created. Important aspects of the system include the capability to adapt to several cutting phenomena appearing at once, even if the said phenomena would potentially require conflicting control action.

Mathematical modelling of the osmotic dehydration of cherry tomato (Lycopersicon esculentum var. cerasiforme)

Osmotic dehydration of cherry tomato as influenced by osmotic agent (sodium chloride and a mixed sodium chloride and sucrose solutions) and solution concentration (10 and 25% w/w) at room temperature (25°C) was studied. Kinetics of water loss and solids uptake were determined by a two parameter model, based on Fick's second law and applied to spherical geometry. The water apparent diffusivity coefficients obtained ranged from 2.17x10-10 to 11.69x10-10 m²/s.

Modelling Energy Consumption of a Switch using Fuzzy-Rule Classifier

The energy consumption of IT equipments is becoming an issue of increasing importance. In particular, network equipments such as routers and switches are major contributors to the energy consumption of internet. Therefore it is important to understand how the relationship between input parameters such as bandwidth, number of active ports, traffic-load, hibernation-mode and their impact on energy consumption of a switch. In this paper, the energy consumption of a switch is analyzed in extensive experiments. A fuzzy rule-based model of energy consumption of a switch is proposed based on the result of experiments. The model can be used to predict the energy saving when deploying new switches by controlling the parameters to achieve desired energy consumption and subsequent performance. Furthermore, the model can also be used for further researches on energy saving techniques such as energy-efficient routing protocol, dynamic link shutdown, etc.

Theoretical modelling of Staphylococcus aureus growth in a cooked meat product kept at ambient temperature using temperature profiles of selected Mexican cities

A theoretical model is used to predict the growth of Staphylococcus aureus in a pasteurized meat product kept at ambient temperatures for several hours. For this purpose, the temperature profiles of some cities of Mexico were combined with literature data on the kinetics of S. aureus growth. As shown by theoretical predictions, if the food is kept at ambient temperature, the average daily temperature may not give accurate predictions.

Using Computational Fluid-Dynamics (CFD) for the evaluation of beer pasteurization: effect of orientation of cans

In-package pasteurization is the most used method for beer microbiological stabilization. The search for safer and better quality food has created a need to better understand the processes involved in producing it. However, little is known about the temperature and velocity profiles during the thermal processes of liquid foods in commercial packaging, which results in over-dimensioned processes to guarantee safety, decreasing the sensorial and nutritional characteristics of the product and increasing process costs. Simulations using Computational Fluid-Dynamics (CFD) have been used by various authors to evaluate those processes. The objective of the present paper was to evaluate the effect of packaging orientation in the pasteurization of beer in a commercial aluminum can using CFD. A heating process was simulated at 60 ºC up to 15 PUs (a conventional beer process, in which 1 Pasteurization Unit (PU) is equivalent to 1minute at 60 ºC). The temperature profile and convection current velocity along the process and the variation of the PUs were evaluated in relation to time considering the cans in the conventional, inverted, and horizontal positions. The temperature and velocity profiles were similar to those presented in the literature. The package position did not result in process improvement.

Utilizing analytic modelling process in industrial retail context

Nykypäivän monimutkaisessa ja epävakaassa liiketoimintaympäristössä yritykset, jotka kykenevät muuttamaan tuottamansa operatiivisen datan tietovarastoiksi, voivat saavuttaa merkittävää kilpailuetua. Ennustavan analytiikan hyödyntäminen tulevien trendien ennakointiin mahdollistaa yritysten tunnistavan avaintekijöitä, joiden avulla he pystyvät erottumaan kilpailijoistaan. Ennustavan analytiikan hyödyntäminen osana päätöksentekoprosessia mahdollistaa ketterämmän, reaaliaikaisen päätöksenteon. Tämän diplomityön tarkoituksena on koota teoreettinen viitekehys analytiikan mallintamisesta liike-elämän loppukäyttäjän näkökulmasta ja hyödyntää tätä mallinnusprosessia diplomityön tapaustutkimuksen yritykseen. Teoreettista mallia hyödynnettiin asiakkuuksien mallintamisessa sekä tunnistamalla ennakoivia tekijöitä myynnin ennustamiseen. Työ suoritettiin suomalaiseen teollisten suodattimien tukkukauppaan, jolla on liiketoimintaa Suomessa, Venäjällä ja Balteissa. Tämä tutkimus on määrällinen tapaustutkimus, jossa tärkeimpänä tiedonkeruumenetelmänä käytettiin tapausyrityksen transaktiodataa. Data työhön saatiin yrityksen toiminnanohjausjärjestelmästä.