Analysis and validation of space averaged drag model for numerical simulations of gas-solid flows in fluidized beds

This thesis presents an approach for formulating and validating a space averaged drag model for coarse mesh simulations of gas-solid flows in fluidized beds using the two-fluid model. Proper modeling for fluid dynamics is central in understanding any industrial multiphase flow. The gas-solid flows in fluidized beds are heterogeneous and usually simulated with the Eulerian description of phases. Such a description requires the usage of fine meshes and small time steps for the proper prediction of its hydrodynamics. Such constraint on the mesh and time step size results in a large number of control volumes and long computational times which are unaffordable for simulations of large scale fluidized beds. If proper closure models are not included, coarse mesh simulations for fluidized beds do not give reasonable results. The coarse mesh simulation fails to resolve the mesoscale structures and results in uniform solids concentration profiles. For a circulating fluidized bed riser, such predicted profiles result in a higher drag force between the gas and solid phase and also overestimated solids mass flux at the outlet. Thus, there is a need to formulate the closure correlations which can accurately predict the hydrodynamics using coarse meshes. This thesis uses the space averaging modeling approach in the formulation of closure models for coarse mesh simulations of the gas-solid flow in fluidized beds using Geldart group B particles. In the analysis of formulating the closure correlation for space averaged drag model, the main parameters for the modeling were found to be the averaging size, solid volume fraction, and distance from the wall. The closure model for the gas-solid drag force was formulated and validated for coarse mesh simulations of the riser, which showed the verification of this modeling approach. Coarse mesh simulations using the corrected drag model resulted in lowered values of solids mass flux. Such an approach is a promising tool in the formulation of appropriate closure models which can be used in coarse mesh simulations of large scale fluidized beds.

The energy efficiency of chilled beam systems in an office building at different climates

Besides the sustaining of healthy and comfortable indoor climate, the air conditioning system should also achieve for energy efficiency. The target indoor climate can be ob-tained with different systems; this study focuses on comparing the energy efficiency of different air conditioning room unit systems in different climates. The calculations are made with dynamic energy simulation software IDA ICE by comparing the indoor cli-mate and energy consumption of an office building with different systems in different climates. The aim of the study is to compare the energy efficiency of chilled beam systems to other common systems: variable air volume, fan coil and radiant ceiling systems. Besides the annual energy consumption also the sustainability of target indoor climate is compared between the simulations. Another aim is to provide conclusions to be used in the product development of the chilled beam systems’ energy efficiency. The adaptable chilled beam system and the radiant ceiling system prove to be energy efficient independent of the climate. The challenge of reliable comparison is that other systems are not able to reach the target indoor climate as well as the others. The complex calculation environment of the simulation software, made assumptions and excluding of the financial aspects complicate comparing the big picture. The results show that the development of the chilled beam systems should concentrate on energy efficient night heating, flexible demand based ventilation and capacity control and possibilities on integrating the best practices with other systems. 

Energy Efficiency of a Diesel-Electric MobileWorking Machine

The power demand of many mobile working machines such as mine loaders, straddle carriers and harvesters varies significantly during operation, and typically, the average power demand of a working machine is considerably lower than the demand for maximum power. Consequently, for most of the time, the diesel engine of a working machine operates at a poor efficiency far from its optimum efficiency range. However, the energy efficiency of dieseldriven working machines can be improved by electric hybridization. This way, the diesel engine can be dimensioned to operate within its optimum efficiency range, and the electric drive with its energy storages responds to changes in machine loading. A hybrid working machine can be implemented in many ways either as a parallel hybrid, a series hybrid or a combination of these two. The energy efficiency of hybrid working machines can be further enhanced by energy recovery and reuse. This doctoral thesis introduces the component models required in the simulation model of a working machine. Component efficiency maps are applied to the modelling; the efficiency maps for electrical machines are determined analytically in the whole torque–rotational speed plane based on the electricalmachine parameters. Furthermore, the thesis provides simulation models for parallel, series and parallel-series hybrid working machines. With these simulation models, the energy consumption of the working machine can be analysed. In addition, the hybridization process is introduced and described. The thesis provides a case example of the hybridization and dimensioning process of a working machine, starting from the work cycle of the machine. The selection and dimensioning of the hybrid system have a significant impact on the energy consumption of a hybrid working machine. The thesis compares the energy consumption of a working machine implemented by three different hybrid systems (parallel, series and parallel-series) and with different component dimensions. The payback time of a hybrid working machine and the energy storage lifetime are also estimated in the study.

Energy Efficiency Consideration in Electric Vehicle Transmission

This study is a survey of benefits and drawbacks of embedding a variable gearbox instead of a single reduction gear in electric vehicle powertrain from efficiency point of view. Losses due to a pair of spur gears meshing with involute teeth are modeled on the base of Coulomb’s law and fluid mechanics. The model for a variable gearbox is fulfilled and further employed in a complete vehicle simulation. Simulation model run for a single reduction gear then the results are taken as benchmark for other types of commonly used transmissions. Comparing power consumption, which is obtained from simulation model, shows that the extra load imposed by variable transmission components will shade the benefits of efficient operation of electric motor. The other accomplishment of this study is a combination of modified formulas that led to a new methodology for power loss prediction in gear meshing which is compatible with modern design and manufacturing technology.

The Anchoring Bias of Investors and the 52-Week High Momentum Strategy in the OMX Helsinki Stock Exchange: Psychological Approach to Market Efficiency

The behavioural finance literature expects systematic and significant deviations from efficiency to persist in securities markets due to behavioural and cognitive biases of investors. These behavioural models attempt to explain the coexistence of intermediate-term momentum and long-term reversals in stock returns based on the systematic violations of rational behaviour of investors. The study investigates the anchoring bias of investors and the profitability of the 52-week momentum strategy (GH henceforward). The relatively highly volatile OMX Helsinki stock exchange is a suitable market for examining the momentum effect, since international investors tend to realise their positions first from the furthest security markets by the time of market turbulence. Empirical data is collected from Thomson Reuters Datastream and the OMX Nordic website. The objective of the study is to provide a throughout research by formulating a self-financing GH momentum portfolio. First, the seasonality of the strategy is examined by taking the January effect into account and researching abnormal returns in long-term. The results indicate that the GH strategy is subject to significantly negative revenues in January, but the strategy is not prone to reversals in long-term. Then the predictive proxies of momentum returns are investigated in terms of acquisition prices and 52-week high statistics as anchors. The results show that the acquisition prices do not have explanatory power over the GH strategy’s abnormal returns. Finally, the efficacy of the GH strategy is examined after taking transaction costs into account, finding that the robust abnormal returns remain statistically significant despite the transaction costs. As a conclusion, the relative distance between a stock’s current price and its 52-week high statistic explains the profits of momentum investing to a high degree. The results indicate that intermediateterm momentum and long-term reversals are separate phenomena. This presents a challenge to current behavioural theories, which model these aspects of stock returns as subsequent components of how securities markets respond to relevant information.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Applying Nordic Energy Efficiency and Renewable Energy Solutions in Kaliningrad Oblast, Russia

RENSOL (Regional Energy Solutions) project deals with the use of energy efficiency and renewable energy solutions in Kaliningrad Oblast to tackle climate change. Overall objective of the RENSOL work package 1 is to build awareness and knowledge on solutions for energy efficient buildings and street lightning applications. The project report describes available solutions to improve housing energy efficiency.

Characterization Of Process Efficiency Improvement In Laser Additive Manufacturing

Laser additive manufacturing (LAM), known also as 3D printing, has gained a lot of interest in past recent years within various industries, such as medical and aerospace industries. LAM enables fabrication of complex 3D geometries by melting metal powder layer by layer with laser beam. Research in laser additive manufacturing has been focused in development of new materials and new applications in past 10 years. Since this technology is on cutting edge, efficiency of manufacturing process is in center role of research of this industry. Aim of this thesis is to characterize methods for process efficiency improvements in laser additive manufacturing. The aim is also to clarify the effect of process parameters to the stability of the process and in microstructure of manufactured pieces. Experimental tests of this thesis were made with various process parameters and their effect on build pieces has been studied, when additive manufacturing was performed with a modified research machine representing EOSINT M-series and with EOS EOSINT M280. Material used was stainless steel 17-4 PH. Also, some of the methods for process efficiency improvements were tested. Literature review of this thesis presents basics of laser additive manufacturing, methods for improve the process efficiency and laser beam – material- interaction. It was observed that there are only few public studies about process efficiency of laser additive manufacturing of stainless steel. According to literature, it is possible to improve process efficiency with higher power lasers and thicker layer thicknesses. The process efficiency improvement is possible if the effect of process parameter changes in manufactured pieces is known. According to experiments carried out in this thesis, it was concluded that process parameters have major role in single track formation in laser additive manufacturing. Rough estimation equations were created to describe the effect of input parameters to output parameters. The experimental results showed that the WDA (width-depth-area of cross-sections of single track) is correlating exponentially with energy density input. The energy density input is combination of the input parameters of laser power, laser beam spot diameter and scan speed. The use of skin-core technique enables improvement of process efficiency as the core of the part is manufactured with higher laser power and thicker layer thickness and the skin with lower laser power and thinner layer thickness in order to maintain high resolution. In this technique the interface between skin and core must have overlapping in order to achieve full dense parts. It was also noticed in this thesis that keyhole can be formed in LAM process. It was noticed that the threshold intensity value of 106 W/cm2 was exceeded during the tests. This means that in these tests the keyhole formation was possible.

PDM system functions and utilizations analysis to improve efficiency of sheet metal product design and manufacturing

This study will concentrate on Product Data Management (PDM) systems, and sheet metal design features and classification. In this thesis, PDM is seen as an individual system which handles all product-related data and information. The meaning of relevant data is to take the manufacturing process further with fewer errors. The features of sheet metals are giving more information and value to the designed models. The possibility of implementing PDM and sheet metal features recognition are the core of this study. Their integration should make the design process faster and manufacturing-friendly products easier to design. The triangulation method is the basis for this research. The sections of this triangle are: scientific literature review, interview using the Delphi method and the author’s experience and observations. The main key findings of this study are: (1) the area of focus in triangle (the triangle of three different point of views: business, information exchange and technical) depends on the person’s background and their role in the company, (2) the classification in the PDM system (and also in the CAD system) should be done using the materials, tools and machines that are in use in the company and (3) the design process has to be more effective because of the increase of industrial production, sheet metal blank production and the designer’s time spent on actual design and (4) because Design For Manufacture (DFM) integration can be done with CAD-programs, DFM integration with the PDM system should also be possible.

Modal testing and numerical modeling of the dynamic properties of layered sheet-steel structure

Recently, due to the increasing total construction and transportation cost and difficulties associated with handling massive structural components or assemblies, there has been increasing financial pressure to reduce structural weight. Furthermore, advances in material technology coupled with continuing advances in design tools and techniques have encouraged engineers to vary and combine materials, offering new opportunities to reduce the weight of mechanical structures. These new lower mass systems, however, are more susceptible to inherent imbalances, a weakness that can result in higher shock and harmonic resonances which leads to poor structural dynamic performances. The objective of this thesis is the modeling of layered sheet steel elements, to accurately predict dynamic performance. During the development of the layered sheet steel model, the numerical modeling approach, the Finite Element Analysis and the Experimental Modal Analysis are applied in building a modal model of the layered sheet steel elements. Furthermore, in view of getting a better understanding of the dynamic behavior of layered sheet steel, several binding methods have been studied to understand and demonstrate how a binding method affects the dynamic behavior of layered sheet steel elements when compared to single homogeneous steel plate. Based on the developed layered sheet steel model, the dynamic behavior of a lightweight wheel structure to be used as the structure for the stator of an outer rotor Direct-Drive Permanent Magnet Synchronous Generator designed for high-power wind turbines is studied.

Höyryturbiinin kanavistojen numeerinen virtauslaskenta

Diplomityössä tutkittiin Fortumin Loviisan ydinvoimalaitoksen ulosvirtauskanaviston ja suurnopeuskosteudenerottimen toimintaa, sekä selvitettiin taustalla olevaa teoriaa ja aiemmin tehtyjä tutkimuksia. Tavoitteena oli ymmärtää ja esittää laitteiden toimintaa, sekä tutkia voiko ulosvirtauskanaviston suorituskykyä parantaa geometrian muutoksilla. Työssä luotiin tutkittaville kohteille geometriat ja laskentahilat, joiden avulla simuloitiin niiden toimintaa eri käyttötilanteissa numeerisen virtauslaskennan avulla. Laskennan reunaehdot saatiin olemassa olevasta prosessimallista ja aiemmista turbiiniselvityksistä. Ulosvirtauskanaviston suorituskyky laskettiin kolmella eri lauhdutinpaineella neljällä eri geometrialla. Geometrian muutokset vaikuttivat selkeästi ulosvirtauskanaviston suorituskykyyn ja sitä saatiin parannettua. Kaksi kolmesta muutoksesta, lisäkanavat ja oikaistu vesilippa, pa-ransivat suorituskykyä. Lokinsiipien poistaminen heikensi ulosvirtauskanaviston toi-mintaa. Suurnopeuskosteudenerottimen mallintaminen jäi lähtötietojen ja ajan puutteen takia hieman tavoitteesta. Sekä ulosvirtauskanaviston että suurnopeuskosteudenerotti-men jatkotutkimusta ja mahdollisia toimenpiteitä varten saatiin arvokasta uutta tietoa.

Belt conveyor energy efficiency analysis and energy model development

An energy model of a belt conveyor was designed. Operation of a belt conveyor was researched. Operational indexes were calculated. Energy optimization process and recommendations were presented.

Concentration of hydrogen peroxide and improving its energy efficiency

Distillation is a unit operation of process industry, which is used to separate a liquid mixture into two or more products and to concentrate liquid mixtures. A drawback of the distillation is its high energy consumption. An increase in energy and raw material prices has led to seeking ways to improve the energy efficiency of distillation. In this Master's Thesis, these ways are studied in connection with the concentration of hydrogen peroxide at the Solvay Voikkaa Plant. The aim of this thesis is to improve the energy efficiency of the concentration of the Voikkaa Plant. The work includes a review of hydrogen peroxide and its manufacturing. In addition, the fundamentals of distillation and its energy efficiency are reviewed. An energy analysis of the concentration unit of Solvay Voikkaa Plant is presented in the process development study part. It consists of the current and past information of energy and utility consumptions, balances, and costs. After that, the potential ways to improve the energy efficiency of the distillation unit at the factory are considered and their feasibility is evaluated technically and economically. Finally, proposals to improve the energy efficiency are suggested. Advanced process control, heat integration and energy efficient equipment are the most potential ways to carry out the energy efficient improvements of the concentration at the Solvay Voikkaa factory. Optimization of the reflux flow and the temperatures of the overhead condensers can offer immediate savings in the energy and utility costs without investments. Replacing the steam ejector system with a vacuum pump would result in savings of tens of thousands of euros per year. The heat pump solutions, such as utilizing a mechanical vapor recompression or thermal vapor recompression, are not feasible due to the high investment costs and long pay back times.

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.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.