Non-Linear Journal Bearing Model for Analysis of Superharmonic Vibrations of Rotor Systems

A rotating machine usually consists of a rotor and bearings that supports it. The nonidealities in these components may excite vibration of the rotating system. The uncontrolled vibrations may lead to excessive wearing of the components of the rotating machine or reduce the process quality. Vibrations may be harmful even when amplitudes are seemingly low, as is usually the case in superharmonic vibration that takes place below the first critical speed of the rotating machine. Superharmonic vibration is excited when the rotational velocity of the machine is a fraction of the natural frequency of the system. In such a situation, a part of the machine’s rotational energy is transformed into vibration energy. The amount of vibration energy should be minimised in the design of rotating machines. The superharmonic vibration phenomena can be studied by analysing the coupled rotor-bearing system employing a multibody simulation approach. This research is focused on the modelling of hydrodynamic journal bearings and rotorbearing systems supported by journal bearings. In particular, the non-idealities affecting the rotor-bearing system and their effect on the superharmonic vibration of the rotating system are analysed. A comparison of computationally efficient journal bearing models is carried out in order to validate one model for further development. The selected bearing model is improved in order to take the waviness of the shaft journal into account. The improved model is implemented and analyzed in a multibody simulation code. A rotor-bearing system that consists of a flexible tube roll, two journal bearings and a supporting structure is analysed employing the multibody simulation technique. The modelled non-idealities are the shell thickness variation in the tube roll and the waviness of the shaft journal in the bearing assembly. Both modelled non-idealities may cause subharmonic resonance in the system. In multibody simulation, the coupled effect of the non-idealities can be captured in the analysis. Additionally one non-ideality is presented that does not excite the vibrations itself but affects the response of the rotorbearing system, namely the waviness of the bearing bushing which is the non-rotating part of the bearing system. The modelled system is verified with measurements performed on a test rig. In the measurements the waviness of bearing bushing was not measured and therefore it’s affect on the response was not verified. In conclusion, the selected modelling approach is an appropriate method when analysing the response of the rotor-bearing system. When comparing the simulated results to the measured ones, the overall agreement between the results is concluded to be good.

Foreign Direct Investment, Clean Development Mechanism, and Environmental Management: A case of Sub-Saharan Africa

This doctoral dissertation explores the contribution of environmental management practices, the so-called clean development mechanism (CDM) projects, and foreign direct investment (FDI) in achieving sustainable development in developing countries, particularly in Sub- Saharan Africa. Because the climate change caused by greenhouse gas emissions is one of the most serious global environmental challenges, the main focus is on the causal links between carbon dioxide (CO2) emissions, energy consumption, and economic development in Sub-Saharan Africa. In addition, the dissertation investigates the factors that have affected the distribution of CDM projects in developing countries and the relationships between FDI and other macroeconomic variables of interest. The main contribution of the dissertation is empirical. One of the publications uses crosssectional data and Tobit and Poisson regressions. Three of the studies use time-series data and vector autoregressive and vector error correction models, while two publications use panel data and panel data estimation methods. One of the publications uses thus both timeseries and panel data. The concept of Granger causality is utilized in four of the publications. The results indicate that there are significant differences in the Granger causality relationships between CO2 emissions, energy consumption, economic growth, and FDI in different countries. It appears also that the causality relationships change over time. Furthermore, the results support the environmental Kuznets curve hypothesis but only for some of the countries. As to CDM activities, past emission levels, institutional quality, and the size of the host country appear to be among the significant determinants of the distribution of CDM projects. FDI and exports are also found to be significant determinants of economic growth.

THE IMPACT OF ARCHITECTURAL DESIGN ON SOFTWARE DEVELOPMENT

Tässä työssä tutkitaan ohjelmistoarkkitehtuurisuunnitteluominaisuuksien vaikutusta erään client-server –arkkitehtuuriin perustuvan mobiilipalvelusovelluksen suunnittelu- ja toteutusaikaan. Kyseinen tutkimus perustuu reaalielämän projektiin, jonka kvalitatiivinen analyysi paljasti arkkitehtuurikompponenttien välisten kytkentöjen merkittävästi vaikuttavan projektin työmäärään. Työn päätavoite oli kvantitatiivisesti tutkia yllä mainitun havainnon oikeellisuus. Tavoitteen saavuttamiseksi suunniteltiin ohjelmistoarkkitehtuurisuunnittelun mittaristo kuvaamaan kyseisen järjestelmän alijärjestelmien arkkitehtuuria ja luotiin kaksi suunniteltua mittaristoa käyttävää, työmäärää (komponentin suunnittelu-, toteutus- ja testausaikojen summa) arvioivaa mallia, joista toinen on lineaarinen ja toinen epälineaarinen. Näiden mallien kertoimet sovitettiin optimoimalla niiden arvot epälineaarista gloobaalioptimointimenetelmää, differentiaalievoluutioalgoritmia, käyttäen, niin että mallien antamat arvot vastasivat parhaiten mitattua työmäärää sekä kaikilla ominaisuuksilla eli attribuuteilla että vain osalla niistä (yksi jätettiin vuorotellen pois). Kun arkkitehtuurikompenttien väliset kytkennät jätettiin malleista pois, mitattujen ja arvoitujen työmäärien välinen ero (ilmaistuna virheenä) kasvoi eräässä tapauksessa 367 % entisestä tarkoittaen sitä, että näin muodostettu malli vastasi toteutusaikoja huonosti annetulla ainestolla. Tämä oli suurin havaitu virhe kaikkien poisjätettyjen ominaisuuksien kesken. Saadun tuloksen perusteella päätettiin, että kyseisen järjestelmän toteutusajat ovat vahvasti riippuvaisia kytkentöjen määrästä, ja näin ollen kytkentöjen määrä oli mitä todennäköisemmin kaikista tärkein työmäärään vaikuttava tekijä tutkitun järjestelmän arkkitehtuurisuunnittelussa.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

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.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Effect of competitive situation and price on diffusion of mobile communications

Työn päätavoitteena oli selvittää hinnan ja kilpailutilanteen vaikutusta matkaviestinnän diffuusioon. Työn empiirinen osuus tarkasteli matkapuhelinliittymien hinnan vaikutusta liittymien diffuusioon sekä sitä, miten alan kilpailu on vaikuttanut matkaviestinnän hintatasoon. Työssä analysoitiin myös matkaviestinnän kilpailutilannetta Suomen markkinoilla. Tutkimuksen empiirinen aineisto kerättiin toissijaisista lähteistä, esimerkiksi EMC-tietokannasta. Tutkimus oli luonteeltaan kvantitatiivinen.Empiirisessä osassa käytetyt mallit oli muodostettu aikaisempien tutkimuksien perusteella. Regressioanalyysiä käytettiin arvioitaessa hinnan vaikutusta diffuusionopeuteen ja mahdollisten omaksujien määrään. Regressioanalyysissä sovellettiin ei-lineaarista mallia.Tutkimustulokset osoittivat, että tasaisesti laskevilla matkapuhelinliittymien sekä matkapuhelimien hinnoilla ei ole merkittävää vaikutusta matkaviestinnän diffuusioon. Myöskään kilpailutilanne ei ole vaikuttanut paljon matkaviestinnän yleiseen hintatasoon. Työn tulosten perusteella voitiin antaa myös muutamia toimenpide-ehdotuksia jatkotutkimuksia varten.

Kylmämuovattujen putkipalkkien X-liitosten äärikestävyyden ja jäykkyyden määrittäminen sekä materiaalimallin kehittäminen

Työssä on tutkittu elementtimenetelmän avulla kylmämuovattujen nelikulmaisten putkipalkkien materiaalimallin kehittämistä ja putkipalkkien X-liitosten jäykkyyden ja äärikestävyyden määrittämistä. Työn tavoitteena on tutkia kylmämuovauksen vaikutuksia putkipalkkiprofiilin materiaaliominaisuuksiin materiaalikokeiden ja elementtianalyysien avulla sekä kehittää putkipalkille anisotrooppista materiaalimallia. Työssä määritettyjä materiaalimalleja on sovellettu X-liitosten elementtimalleihin, joiden käyttäytymistä on verrattu äärikestävyyskokeiden tuloksiin. Tutkimuksen perusteella Eurocode 3:n mitoitusohjeita voidaan turvallisesti soveltaa kylmämuovattujen putkipalkkien X-liitosten laskennassa. Työssä tehtyjen materiaalikokeiden ja elementtianalyysien perusteella materiaalin anisotrooppisuuden vaikutus liitoksen kestävyyteen on vähäistä, ja putkipalkin pituussuuntaista materiaalimallia voidaan soveltaa myös kehäsuuntaisille materiaaliominaisuuksille. Materiaalikokeiden simulointi osoittaa, että elementtimenetelmää voidaan käyttää materiaalimallin määrittämisen apuvälineenä.

Development of phase retrieval algorithm and techniques for optical spectrum analysis

Coherent anti-Stokes Raman scattering is the powerful method of laser spectroscopy in which significant successes are achieved. However, the non-linear nature of CARS complicates the analysis of the received spectra. The objective of this Thesis is to develop a new phase retrieval algorithm for CARS. It utilizes the maximum entropy method and the new wavelet approach for spectroscopic background correction of a phase function. The method was developed to be easily automated and used on a large number of spectra of different substances.. The algorithm was successfully tested on experimental data.

Reaction Kinetics and Viscosity Modelling in the Fusion Syntheses of Ca- and Ca/Mg-resinates

Rosin is a natural product from pine forests and it is used as a raw material in resinate syntheses. Resinates are polyvalent metal salts of rosin acids and especially Ca- and Ca/Mg- resinates find wide application in the printing ink industry. In this thesis, analytical methods were applied to increase general knowledge of resinate chemistry and the reaction kinetics was studied in order to model the non linear solution viscosity increase during resinate syntheses by the fusion method. Solution viscosity in toluene is an important quality factor for resinates to be used in printing inks. The concept of critical resinate concentration, c crit, was introduced to define an abrupt change in viscosity dependence on resinate concentration in the solution. The concept was then used to explain the non-inear solution viscosity increase during resinate syntheses. A semi empirical model with two estimated parameters was derived for the viscosity increase on the basis of apparent reaction kinetics. The model was used to control the viscosity and to predict the total reaction time of the resinate process. The kinetic data from the complex reaction media was obtained by acid value titration and by FTIR spectroscopic analyses using a conventional calibration method to measure the resinate concentration and the concentration of free rosin acids. A multivariate calibration method was successfully applied to make partial least square (PLS) models for monitoring acid value and solution viscosity in both mid-infrared (MIR) and near infrared (NIR) regions during the syntheses. The calibration models can be used for on line resinate process monitoring. In kinetic studies, two main reaction steps were observed during the syntheses. First a fast irreversible resination reaction occurs at 235 °C and then a slow thermal decarboxylation of rosin acids starts to take place at 265 °C. Rosin oil is formed during the decarboxylation reaction step causing significant mass loss as the rosin oil evaporates from the system while the viscosity increases to the target level. The mass balance of the syntheses was determined based on the resinate concentration increase during the decarboxylation reaction step. A mechanistic study of the decarboxylation reaction was based on the observation that resinate molecules are partly solvated by rosin acids during the syntheses. Different decarboxylation mechanisms were proposed for the free and solvating rosin acids. The deduced kinetic model supported the analytical data of the syntheses in a wide resinate concentration region, over a wide range of viscosity values and at different reaction temperatures. In addition, the application of the kinetic model to the modified resinate syntheses gave a good fit. A novel synthesis method with the addition of decarboxylated rosin (i.e. rosin oil) to the reaction mixture was introduced. The conversion of rosin acid to resinate was increased to the level necessary to obtain the target viscosity for the product at 235 °C. Due to a lower reaction temperature than in traditional fusion synthesis at 265 °C, thermal decarboxylation is avoided. As a consequence, the mass yield of the resinate syntheses can be increased from ca. 70% to almost 100% by recycling the added rosin oil.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Harustetun 110 kV kannatuspylvään konseptin kehittäminen

Diplomityössä kehitettiin harustetun 110 kV kannatuspylvään konsepti tuotteeksi. Pylväs on säänkestävästä teräksestä valmistettu putkipalkkirakenteinen I-pylväs. Tavoitteena oli suunnitella rakenteesta kokonaistaloudellisesti edullinen. Rakenteen suunnittelussa otettiin huomioon valmistus-, kuljetus- ja varastointi- sekä rakentamisnäkökohtia. Työssä perehdyttiin pylväsrakenteiden yksityiskohtiin, putkipalkkien liitosmenetelmiin ja pylvään jalan nivelöintiratkaisuihin. Säänkestävä rakennemateriaali otettiin huomioon rakennesuunnittelussa. Rakenteen lujuusteknisen suunnittelun apuna käytettiin epälineaarista elementtimenetelmää. Pylväsrakenteen käyttäytyminen mallinnettiin geometrisesti epälineaariseksi, ja liitosdetaljien analysointia varten kehitettiin epälineaarisia materiaalimalleja. Rakenteen värähtelykäyttäytyminen analysoitiin myös elementtimenetelmällä. Lopputuloksena saatiin aikaan pylväs, joka täyttää sille asetetut vaatimukset. Pylväs on helposti valmistettava, kuljetettava ja pystytettävä.

Design and Analysis of Forward Error Control Coding and Signaling for Guaranteeing QoS in Wireless Broadcast Systems

Broadcasting systems are networks where the transmission is received by several terminals. Generally broadcast receivers are passive devices in the network, meaning that they do not interact with the transmitter. Providing a certain Quality of Service (QoS) for the receivers in heterogeneous reception environment with no feedback is not an easy task. Forward error control coding can be used for protection against transmission errors to enhance the QoS for broadcast services. For good performance in terrestrial wireless networks, diversity should be utilized. The diversity is utilized by application of interleaving together with the forward error correction codes. In this dissertation the design and analysis of forward error control and control signalling for providing QoS in wireless broadcasting systems are studied. Control signaling is used in broadcasting networks to give the receiver necessary information on how to connect to the network itself and how to receive the services that are being transmitted. Usually control signalling is considered to be transmitted through a dedicated path in the systems. Therefore, the relationship of the signaling and service data paths should be considered early in the design phase. Modeling and simulations are used in the case studies of this dissertation to study this relationship. This dissertation begins with a survey on the broadcasting environment and mechanisms for providing QoS therein. Then case studies present analysis and design of such mechanisms in real systems. The mechanisms for providing QoS considering signaling and service data paths and their relationship at the DVB-H link layer are analyzed as the first case study. In particular the performance of different service data decoding mechanisms and optimal signaling transmission parameter selection are presented. The second case study investigates the design of signaling and service data paths for the more modern DVB-T2 physical layer. Furthermore, by comparing the performances of the signaling and service data paths by simulations, configuration guidelines for the DVB-T2 physical layer signaling are given. The presented guidelines can prove useful when configuring DVB-T2 transmission networks. Finally, recommendations for the design of data and signalling paths are given based on findings from the case studies. The requirements for the signaling design should be derived from the requirements for the main services. Generally, these requirements for signaling should be more demanding as the signaling is the enabler for service reception.

On convergence of transforms based on parabolic scaling

This thesis studies properties of transforms based on parabolic scaling, like Curvelet-, Contourlet-, Shearlet- and Hart-Smith-transform. Essentially, two di erent questions are considered: How these transforms can characterize H older regularity and how non-linear approximation of a piecewise smooth function converges. In study of Hölder regularities, several theorems that relate regularity of a function f : R2 → R to decay properties of its transform are presented. Of particular interest is the case where a function has lower regularity along some line segment than elsewhere. Theorems that give estimates for direction and location of this line, and regularity of the function are presented. Numerical demonstrations suggest also that similar theorems would hold for more general shape of segment of low regularity. Theorems related to uniform and pointwise Hölder regularity are presented as well. Although none of the theorems presented give full characterization of regularity, the su cient and necessary conditions are very similar. Another theme of the thesis is the study of convergence of non-linear M ─term approximation of functions that have discontinuous on some curves and otherwise are smooth. With particular smoothness assumptions, it is well known that squared L2 approximation error is O(M-2(logM)3) for curvelet, shearlet or contourlet bases. Here it is shown that assuming higher smoothness properties, the log-factor can be removed, even if the function still is discontinuous.