Modeling Front End Electronics for a Gas Electron Multiplier Detector

The Large Hadron Collider (LHC) in The European Organization for Nuclear Research (CERN) will have a Long Shutdown sometime during 2017 or 2018. During this time there will be maintenance and a possibility to install new detectors. After the shutdown the LHC will have a higher luminosity. A promising new type of detector for this high luminosity phase is a Triple-GEM detector. During the shutdown these detectors will be installed at the Compact Muon Solenoid (CMS) experiment. The Triple-GEM detectors are now being developed at CERN and alongside also a readout ASIC chip for the detector. In this thesis a simulation model was developed for the ASICs analog front end. The model will help to carry out more extensive simulations and also simulate the whole chip before the whole design is finished. The proper functioning of the model was tested with simulations, which are also presented in the thesis.

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.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Three-dimensional modeling of biomass fuel flow in a circulating fluidized bed furnace

The reduction of greenhouse gas emissions in the European Union promotes the combustion of biomass rather than fossil fuels in energy production. Circulating fluidized bed (CFB) combustion offers a simple, flexible and efficient way to utilize untreated biomass in a large scale. CFB furnaces are modeled in order to understand their operation better and to help in the design of new furnaces. Therefore, physically accurate models are needed to describe the heavily coupled multiphase flow, reactions and heat transfer inside the furnace. This thesis presents a new model for the fuel flow inside the CFB furnace, which acknowledges the physical properties of the fuel and the multiphase flow phenomena inside the furnace. This model is applied with special interest in the firing of untreated biomass. An experimental method is utilized to characterize gas-fuel drag force relations. This characteristic drag force approach is developed into a gas-fuel drag force model suitable for irregular, non-spherical biomass particles and applied together with the new fuel flow model in the modeling of a large-scale CFB furnace. The model results are physically valid and achieve very good correspondence with the measurement results from large-scale CFB furnace firing biomass. With the methods and models presented in this work, the fuel flow field inside a circulating fluidized bed furnace can be modeled with better accuracy and more efficiently than in previous studies with a three-dimensional holistic model frame.

Taloushallinnon raportoinnin kehittäminen - case Trust Accounts

Taloushallinnon raportoinnilla on merkittävä tehtävä yritysten liiketoiminnan johtamisessa. Se antaa tietoa paitsi yrityksen menneisyydestä ja tämän hetkisestä tilanteesta, sen avulla on myös mahdollista ennustaa tulevaisuutta. Taloushallinnon tiedolla on tärkeä rooli, koska sitä hyödynnetään esimerkiksi päätöksenteon tukena. Raporttien sisältämä tieto on oltava paitsi oikeaa ja yritykselle tärkeää, sen on oltava myös helposti luettavissa ja ymmärrettävissä. Raporttien luettavuutta ja lukijoiden raporttien lukutaitoa parantamalla on mahdollista saada yritykset hyödyntämään raportteja entistä enemmän. Jos yritykset eivät osaa lukea raportteja, eivät he niitä tule myöskään hyödyntämään liiketoimintansa johtamisessa. Tämän pro gradu -tutkielman tavoitteena oli antaa toimeksiantajayritykselle kehitysideoita asiakasyrityksiltä siitä, kuinka he voisivat parantaa taloushallinnon raportointia. Tutkimus on toteutettu laadullisena, eli kvalitatiivisena tutkimuksena. Tutkimusmenetelmänä on käytetty haastattelua, joka sisältää piirteitä sekä strukturoidusta että puolistrukturoidusta haastattelusta. Tutkimuksen tuloksena toimeksiantajayritykselle on koottu taulukkoon (1) asiakasyritysten haastatteluista saadut kehitysideat. Kehitysideoiden avulla toimeksiantajayritys voi kehittää omia raportointivälineitään ja sitä kautta lisätä asiakastyytyväisyyttä heidän tilitoimistoa kohtaan

The nature and role of trust in temporary virtual problem-solving teams

The objective of this study is to increase understanding of the nature and role of trust in temporary virtual problem-solving teams engaged in real-life co-creation activities, while much of previous research has been conducted in student settings. The different forms and bases of trust, possible trust barriers and trust building actions, and perceived role of trust in knowledge sharing and collaboration are analyzed. The study is conducted as a qualitative case study in case company. Data includes interviews from 24 people: 13 from 3 different project teams that were going on during the study, 8 from already finalized project teams, and 3 founders of case company. Additional data consists of communication archives from three current teams. The results indicate that there were both knowledge-based and swift trust present, former being based on work-related personal experiences about leaders or other team members, and latter especially on references, disposition to trust and institution-based factors such as norms and rules, as well as leader and expert action. The findings suggest that possible barriers of trust might be related to lack of adaptation to virtual work, unclear roles and safety issues, and nature of virtual communication. Actions that could be applied to enhance trust are for example active behavior in discussions, work-related introductions communicating competence, managerial actions and face-to-face interaction. Finally, results also suggest that trust has a focal role as an enabler of action and knowledge sharing, and coordinator of effective collaboration and performance in temporary virtual problem-solving teams.