Sähkökäyttöisen konejärjestelmän vuorovaikutteinen simulointi

Työn tavoitteena oli toteuttaa simulointimalli, jolla pystytään tutkimaan kestomagnetoidun tahtikoneen aiheuttaman vääntömomenttivärähtelyn vaikutuksia sähkömoottoriin liitetyssä mekaniikassa. Tarkoitus oli lisäksi selvittää kuinka kyseinen simulointimalli voidaan toteuttaa nykyaikaisia simulointiohjelmia käyttäen. Saatujen simulointitulosten oikeellisuus varmistettiin tätä työtä varten rakennetulla verifiointilaitteistolla. Tutkittava rakenne koostui akselista, johon kiinnitettiin epäkeskotanko. Epäkeskotankoon kiinnitettiin massa, jonka sijaintia voitiin muunnella. Massan asemaa muuttamalla saatiin rakenteelle erilaisia ominaistaajuuksia. Epäkeskotanko mallinnettiin joustavana elementtimenetelmää apuna käyttäen. Mekaniikka mallinnettiin dynamiikan simulointiin tarkoitetussa ADAMS –ohjelmistossa, johon joustavana mallinnettu epäkeskotanko tuotiin ANSYS –elementtimenetelmäohjelmasta. Mekaniikan malli siirrettiin SIMULINK –ohjelmistoon, jossa mallinnettiin myös sähkökäyttö. SIMULINK –ohjelmassa mallinnettiin sähkökäyttö, joka kuvaa kestomagnetoitua tahtikonetta. Kestomagnetoidun tahtikoneen yhtälöt perustuvat lineaarisiin differentiaaliyhtälöihin, joihin hammasvääntömomentin vaikutus on lisätty häiriösignaalina. Sähkökäytön malli tuottaa vääntömomenttia, joka syötetään ADAMS –ohjelmistolla mallinnettuun mekaniikkaan. Mekaniikan mallista otetaan roottorin kulmakiihtyvyyden arvo takaisinkytkentänä sähkömoottorin malliin. Näin saadaan aikaiseksi yhdistetty simulointi, joka koostuu sähkötoimilaitekäytöstä ja mekaniikasta. Tulosten perusteella voidaan todeta, että sähkökäyttöjen ja mekaniikan yhdistetty simulointi on mahdollista toteuttaa valituilla menetelmillä. Simuloimalla saadut tulokset vastaavat hyvin mitattuja tuloksia.

Economic and social convergence in Colombia

GDP has usually been used as a proxy for human well-being. Nevertheless, other social aspects should also be considered, such as life expectancy, infant mortality, educational enrolment and crime issues. With this paper we investigate not only economic convergence but also social convergence between regions in a developing country, Colombia, in the period 1975-2005. We consider several techniques in our analysis: sigma convergence, stochastic kernel estimations, and also several empirical models to find out the beta convergence parameter (cross section and panel estimates, with and without spatial dependence). The main results confirm that we can talk about convergence in Colombia in key social variables, although not in the classic economic variable, GDP per capita. We have also found that spatial autocorrelation reinforces convergence processes through deepening market and social factors, while isolation condemns regions to nonconvergence.

A Computer program for estimating population size by the removal method

A BASIC computer program (REMOVAL) was developed to compute in a VAXNMS environment all the calculations of the removal method for population size estimation (catch-effort method for closed populations with constant sampling effort). The program follows the maximum likelihood methodology,checks the failure conditions, applies the appropriate formula, and displays the estimates of population size and catchability, with their standard deviations and coefficients of variation, and two goodness-of-fit statistics with their significance levels. Data of removal experiments for the cyprinodontid fish Aphanius iberus in the Alt Emporda wetlands are used to exemplify the use of the program

Polarimetric 3D integral imaging in photon-starved conditions

We develop a method for obtaining 3D polarimetric integral images from elemental images recorded in low light illumination conditions. Since photon-counting images are very sparse, calculation of the Stokes parameters and the degree of polarization should be handled carefully. In our approach, polarimetric 3D integral images are generated using the Maximum Likelihood Estimation and subsequently reconstructed by means of a Total Variation Denoising filter. In this way, polarimetric results are comparable to those obtained in conventional illumination conditions. We also show that polarimetric information retrieved from photon starved images can be used in 3D object recognition problems. To the best of our knowledge, this is the first report on 3D polarimetric photon counting integral imaging.

Modeling and simulation of forced-air cooling of strawberries using variable convective coefficient

In the forced-air cooling process of fruits occurs, besides the convective heat transfer, the mass transfer by evaporation. The energy need in the evaporation is taken from fruit that has its temperature lowered. In this study it has been proposed the use of empirical correlations for calculating the convective heat transfer coefficient as a function of surface temperature of the strawberry during the cooling process. The aim of this variation of the convective coefficient is to compensate the effect of evaporation in the heat transfer process. Linear and exponential correlations are tested, both with two adjustable parameters. The simulations are performed using experimental conditions reported in the literature for the cooling of strawberries. The results confirm the suitability of the proposed methodology.

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

Climate trends in the municipality of Pelotas, state of Rio Grande do Sul, Brazil

ABSTRACTChanges in the frequency of occurrence of extreme weather events have been pointed out as a likely impact of global warming. In this context, this study aimed to detect climate change in series of extreme minimum and maximum air temperature of Pelotas, State of Rio Grande do Sul, (1896 - 2011) and its influence on the probability of occurrence of these variables. We used the general extreme value distribution (GEV) in its stationary and non-stationary forms. In the latter case, GEV parameters are variable over time. On the basis of goodness-of-fit tests and of the maximum likelihood method, the GEV model in which the location parameter increases over time presents the best fit of the daily minimum air temperature series. Such result describes a significant increase in the mean values of this variable, which indicates a potential reduction in the frequency of frosts. The daily maximum air temperature series is also described by a non-stationary model, whose location parameter decreases over time, and the scale parameter related to sample variance rises between the beginning and end of the series. This result indicates a drop in the mean of daily maximum air temperature values and increased dispersion of the sample data.

Algorithm to determine the intersection curves between bezier surfaces by the solution of multivariable polynomial system and the differential marching method

The determination of the intersection curve between Bézier Surfaces may be seen as the composition of two separated problems: determining initial points and tracing the intersection curve from these points. The Bézier Surface is represented by a parametric function (polynomial with two variables) that maps a point in the tridimensional space from the bidimensional parametric space. In this article, it is proposed an algorithm to determine the initial points of the intersection curve of Bézier Surfaces, based on the solution of polynomial systems with the Projected Polyhedral Method, followed by a method for tracing the intersection curves (Marching Method with differential equations). In order to allow the use of the Projected Polyhedral Method, the equations of the system must be represented in terms of the Bernstein basis, and towards this goal it is proposed a robust and reliable algorithm to exactly transform a multivariable polynomial in terms of power basis to a polynomial written in terms of Bernstein basis .

Thermal reactions of the major hydrocarbon components of biomass gasification gas

Gasification of biomass is an efficient method process to produce liquid fuels, heat and electricity. It is interesting especially for the Nordic countries, where raw material for the processes is readily available. The thermal reactions of light hydrocarbons are a major challenge for industrial applications. At elevated temperatures, light hydrocarbons react spontaneously to form higher molecular weight compounds. In this thesis, this phenomenon was studied by literature survey, experimental work and modeling effort. The literature survey revealed that the change in tar composition is likely caused by the kinetic entropy. The role of the surface material is deemed to be an important factor in the reactivity of the system. The experimental results were in accordance with previous publications on the subject. The novelty of the experimental work lies in the used time interval for measurements combined with an industrially relevant temperature interval. The aspects which are covered in the modeling include screening of possible numerical approaches, testing of optimization methods and kinetic modelling. No significant numerical issues were observed, so the used calculation routines are adequate for the task. Evolutionary algorithms gave a better performance combined with better fit than the conventional iterative methods such as Simplex and Levenberg-Marquardt methods. Three models were fitted on experimental data. The LLNL model was used as a reference model to which two other models were compared. A compact model which included all the observed species was developed. The parameter estimation performed on that model gave slightly impaired fit to experimental data than LLNL model, but the difference was barely significant. The third tested model concentrated on the decomposition of hydrocarbons and included a theoretical description of the formation of carbon layer on the reactor walls. The fit to experimental data was extremely good. Based on the simulation results and literature findings, it is likely that the surface coverage of carbonaceous deposits is a major factor in thermal reactions.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Kalman filter for computational market dynamics

Kalman filter is a recursive mathematical power tool that plays an increasingly vital role in innumerable fields of study. The filter has been put to service in a multitude of studies involving both time series modelling and financial time series modelling. Modelling time series data in Computational Market Dynamics (CMD) can be accomplished using the Jablonska-Capasso-Morale (JCM) model. Maximum likelihood approach has always been utilised to estimate the parameters of the JCM model. The purpose of this study is to discover if the Kalman filter can be effectively utilized in CMD. Ensemble Kalman filter (EnKF), with 50 ensemble members, applied to US sugar prices spanning the period of January, 1960 to February, 2012 was employed for this work. The real data and Kalman filter trajectories showed no significant discrepancies, hence indicating satisfactory performance of the technique. Since only US sugar prices were utilized, it would be interesting to discover the nature of results if other data sets are employed.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

A simple model for circadian timing by mammals

Circadian timing is structured in such a way as to receive information from the external and internal environments, and its function is the timing organization of the physiological and behavioral processes in a circadian pattern. In mammals, the circadian timing system consists of a group of structures, which includes the suprachiasmatic nucleus (SCN), the intergeniculate leaflet and the pineal gland. Neuron groups working as a biological pacemaker are found in the SCN, forming a biological master clock. We present here a simple model for the circadian timing system of mammals, which is able to reproduce two fundamental characteristics of biological rhythms: the endogenous generation of pulses and synchronization with the light-dark cycle. In this model, the biological pacemaker of the SCN was modeled as a set of 1000 homogeneously distributed coupled oscillators with long-range coupling forming a spherical lattice. The characteristics of the oscillator set were defined taking into account the Kuramoto's oscillator dynamics, but we used a new method for estimating the equilibrium order parameter. Simultaneous activities of the excitatory and inhibitory synapses on the elements of the circadian timing circuit at each instant were modeled by specific equations for synaptic events. All simulation programs were written in Fortran 77, compiled and run on PC DOS computers. Our model exhibited responses in agreement with physiological patterns. The values of output frequency of the oscillator system (maximal value of 3.9 Hz) were of the order of magnitude of the firing frequencies recorded in suprachiasmatic neurons of rodents in vivo and in vitro (from 1.8 to 5.4 Hz).

Mathematical modeling of microbial growth in milk

A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.