Statistical Analysis and Numerics of Heat Exchanger Models

The identifiability of the parameters of a heat exchanger model without phase change was studied in this Master’s thesis using synthetically made data. A fast, two-step Markov chain Monte Carlo method (MCMC) was tested with a couple of case studies and a heat exchanger model. The two-step MCMC-method worked well and decreased the computation time compared to the traditional MCMC-method. The effect of measurement accuracy of certain control variables to the identifiability of parameters was also studied. The accuracy used did not seem to have a remarkable effect to the identifiability of parameters. The use of the posterior distribution of parameters in different heat exchanger geometries was studied. It would be computationally most efficient to use the same posterior distribution among different geometries in the optimisation of heat exchanger networks. According to the results, this was possible in the case when the frontal surface areas were the same among different geometries. In the other cases the same posterior distribution can be used for optimisation too, but that will give a wider predictive distribution as a result. For condensing surface heat exchangers the numerical stability of the simulation model was studied. As a result, a stable algorithm was developed.

HTR-PROTEUS-reaktorin mallintaminen

Maailmassa on tarve entistä turvallisemmille ja taloudellisemmille ydinreaktoreille. Neljännen sukupolven reaktorikonseptit ovat aiempia turvallisempia ja luotettavampia, niissä on tehokkaampi polttoaineresurssien käyttö ja ydinjätettä syntyy vähemmän. Lisäksi ne ovat taloudellisesti kilpailukykyisempiä ja niissä on erinomainen proliferaation vastustuskyky. Kuulakekoreaktorikonsepti on toinen korkealämpötilaisten kaasujäähdytteisten reaktoreiden (HTGR, High Temperature Reactor) päätyypeistä ja jäähdytteen lämpötilan noustessa reaktorissa riittävän korkealle, sitä voidaan pitää myös erittäin korkean lämpötilan reaktorina (VHTR, Very High Temperature Reactor), joka on neljännen sukupolven reaktorikonsepti. Tässä kandidaatintyössä käsitellään 90-luvulla Sveitsissä sijainnutta kuulakekoreaktori-tyyppistä koereaktoria HTR-PROTEUS (tai LEU-HTR-PROTEUS), jolla tutkittiin ennen kaikkea matalaväkevöidyn (LEU, Low Enriched Uranium) uraanipolttoaineen käyttöä kuulakekoreaktorissa. Lisäksi erityisenä mielenkiinnon kohteena oli veden joutuminen reaktoriin onnettomuustilanteessa. Työn tarkoituksena on mallintaa reaktorisysteemi ja laskea kasvutekijät viidelle eri reaktorikonfiguraatiolle. Reaktorin mallinnus ja laskenta suoritetaan Monte Carlo -menetelmää käyttävällä Serpent-laskentakoodilla. Saatuja tuloksia verrataan muissa lähteissä eri laskentakoodeilla esitettyihin tuloksiin.

RBMK-koereaktorin mallintaminen

Tämän kandidaatintyön tarkoituksena on tutkia jäähdytteen poistamisen vaikutusta RBMK-koereaktorin kasvukertoimeen ja erityisesti sitä, kuinka hyvin Monte Carlo -menetelmää käyttävä Serpent-laskentakoodi pystyy mallintamaan jäähdytteen poistamisen vaikutuksen. Aluksi tarkastellaan taustatietoina käytettyä raporttia käsiteltävän koereaktorin kriittisyysajoista ja aiemmista simulaatioista, sekä RBMK-reaktorin ominaispiirteitä ja Monte Carlo -simulaation teoriaa. Seuraavaksi esitellään koereaktorista luotu malli, selitetään mallinnettaessa tehdyt yksinkertaistukset ja kuvataan simulaation alkutilanne. Lopuksi käsitellään simulaation tuloksia ja Serpentillä luodun mallin soveltuvuutta verrattuna aiemmin suoritettuihin simulaatioihin.

Mobile robot localization using sonar ranging and wlan intensity maps

 Main goal of this thesis was to implement a localization system which uses sonars and WLAN intensity maps to localize an indoor mobile robot. A probabilistic localization method, Monte Carlo Localization is used in localization. Also the theory behind probabilistic localization is explained. Two main problems in mobile robotics, path tracking and global localization, are solved in this thesis. Implemented system can achieve acceptable performance in path tracking. Global localization using WLAN received signal strength information is shown to provide good results, which can be used to localize the robot accurately, but also some bad results, which are no use when trying to localize the robot to the correct place. Main goal of solving ambiguity in office like environment is achieved in many test cases.

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.

Vaikutusalakirjastojen muodostaminen Serpent-ARES-laskentaketjussa

Tässä diplomityössä on esitetty työn yhteydessä toteutetun Serpent-ARES-laskentaketjun muodostamiseksi tarvittavat toimenpiteet. ARES-reaktorisydän-simulaattorissa tarvittavien homogenisoitujen ryhmävakiokirjastojen muodostaminen Serpentiä käyttäen tekee laskentaketjusta muiden käytössä olevien reaktorisydämen laskentaketjujen mahdollisista virhelähteistä riippumattoman. Monte Carlo-laskentamenetelmään perustuvaa reaktorifysiikan laskentaohjelmaa käyttämällä ryhmävakiokirjastot muodostetaan uudella menetelmällä ja näin saadaan viranomaiskäyttöön voimayhtiöiden käyttämistä menetelmistä riippumaton laskentaketju reaktorien turvallisuusmarginaalien laskentaan. Työn yhteydessä muodostetun laskentaketjun ja tehtyjen vaikutusalakirjastojen muodostamisrutiinien sekä parametrisovitteiden toimivuus on todettu laskemalla Olkiluoto 3 - reaktorin alkulatauksen säätösauvojen tehokkuuksia ja sammutusmarginaaleja eri olosuhteissa. Menetelmä on todettu toimivaksi parametrien pätevyysalueella ja saadut laskentatulokset ovat oikeaa suuruusluokkaa. Parametrimallin tarkkuutta ja pätevyysaluetta on syytä vielä kehittää, ennen kuin laskentaketjua voidaan käyttää varmentamaan muilla menetelmillä laskettujen tulosten oikeellisuutta.

Long-term Prospects For The Environmental Profile Of Advanced Sugar Cane Ethanol.

This work assessed the environmental impacts of the production and use of 1 MJ of hydrous ethanol (E100) in Brazil in prospective scenarios (2020-2030), considering the deployment of technologies currently under development and better agricultural practices. The life cycle assessment technique was employed using the CML method for the life cycle impact assessment and the Monte Carlo method for the uncertainty analysis. Abiotic depletion, global warming, human toxicity, ecotoxicity, photochemical oxidation, acidification, and eutrophication were the environmental impacts categories analyzed. Results indicate that the proposed improvements (especially no-til farming-scenarios s2 and s4) would lead to environmental benefits in prospective scenarios compared to the current ethanol production (scenario s0). Combined first and second generation ethanol production (scenarios s3 and s4) would require less agricultural land but would not perform better than the projected first generation ethanol, although the uncertainties are relatively high. The best use of 1 ha of sugar cane was also assessed, considering the displacement of the conventional products by ethanol and electricity. No-til practices combined with the production of first generation ethanol and electricity (scenario s2) would lead to the largest mitigation effects for global warming and abiotic depletion. For the remaining categories, emissions would not be mitigated with the utilization of the sugar cane products. However, this conclusion is sensitive to the displaced electricity sources.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

Resolvendo a equação de Schrödinger utilizando procedimentos numéricos fundamentais

A combination of the variational principle, expectation value and Quantum Monte Carlo method is used to solve the Schrödinger equation for some simple systems. The results are accurate and the simplicity of this version of the Variational Quantum Monte Carlo method provides a powerful tool to teach alternative procedures and fundamental concepts in quantum chemistry courses. Some numerical procedures are described in order to control accuracy and computational efficiency. The method was applied to the ground state energies and a first attempt to obtain excited states is described.

Modelos de volatilidade estocástica em séries financeiras: uma aplicação para o IBOVESPA

Neste artigo apresentamos uma análise Bayesiana para o modelo de volatilidade estocástica (SV) e uma forma generalizada deste, cujo objetivo é estimar a volatilidade de séries temporais financeiras. Considerando alguns casos especiais dos modelos SV usamos algoritmos de Monte Carlo em Cadeias de Markov e o software WinBugs para obter sumários a posteriori para as diferentes formas de modelos SV. Introduzimos algumas técnicas Bayesianas de discriminação para a escolha do melhor modelo a ser usado para estimar as volatilidades e fazer previsões de séries financeiras. Um exemplo empírico de aplicação da metodologia é introduzido com a série financeira do IBOVESPA.

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.

Polyelectrolyte-protein complexation driven by charge regulation

The interplay between the biocolloidal characteristics (especially size and charge), pH, salt concentration and the thermal energy results in a unique collection of mesoscopic forces of importance to the molecular organization and function in biological systems. By means of Monte Carlo simulations and semi-quantitative analysis in terms of perturbation theory, we describe a general electrostatic mechanism that gives attraction at low electrolyte concentrations. This charge regulation mechanism due to titrating amino acid residues is discussed in a purely electrostatic framework. The complexation data reported here for interaction between a polyelectrolyte chain and the proteins albumin, goat and bovine alpha-lactalbumin, beta-lactoglobulin, insulin, k-casein, lysozyme and pectin methylesterase illustrate the importance of the charge regulation mechanism. Special attention is given to pH congruent to pI where ion-dipole and charge regulation interactions could overcome the repulsive ion-ion interaction. By means of protein mutations, we confirm the importance of the charge regulation mechanism, and quantify when the complexation is dominated either by charge regulation or by the ion-dipole term.

Allosteric Control of Gating Mechanisms Revisited: The Large Conductance Ca(2+)-Activated K(+) Channel

Large-conductance Ca(2+)-activated K(+) channels (BK) play a fundamental role in modulating membrane potential in many cell types. The gating of BK channels and its modulation by Ca(2+) and voltage has been the subject of intensive research over almost three decades, yielding several of the most complicated kinetic mechanisms ever proposed. A large number of open and closed states disposed, respectively, in two planes, named tiers, characterize these mechanisms. Transitions between states in the same plane are cooperative and modulated by Ca(2+). Transitions across planes are highly concerted and voltage-dependent. Here we reexamine the validity of the two-tiered hypothesis by restricting attention to the modulation by Ca(2+). Large single channel data sets at five Ca(2+) concentrations were simultaneously analyzed from a Bayesian perspective by using hidden Markov models and Markov-chain Monte Carlo stochastic integration techniques. Our results support a dramatic reduction in model complexity, favoring a simple mechanism derived from the Monod-Wyman-Changeux allosteric model for homotetramers, able to explain the Ca(2+) modulation of the gating process. This model differs from the standard Monod-Wyman-Changeux scheme in that one distinguishes when two Ca(2+) ions are bound to adjacent or diagonal subunits of the tetramer.

Distribution of hepatitis c virus (hcv) genotypes in patients with chronic infection from Rondonia, Brazil

Background: Hepatitis C virus (HCV) is an important human pathogen affecting around 3% of the human population. In Brazil, it is estimated that there are approximately 2 to 3 million HCV chronic carriers. There are few reports of HCV prevalence in Rondonia State (RO), but it was estimated in 9.7% from 1999 to 2005. The aim of this study was to characterize HCV genotypes in 58 chronic HCV infected patients from Porto Velho, Rondonia (RO), Brazil. Methods: A fragment of 380 bp of NS5B region was amplified by nested PCR for genotyping analysis. Viral sequences were characterized by phylogenetic analysis using reference sequences obtained from the GenBank (n = 173). Sequences were aligned using Muscle software and edited in the SE-AL software. Phylogenetic analyses were conducted using Bayesian Markov chain Monte Carlo simulation (MCMC) to obtain the MCC tree using BEAST v. 1.5.3. Results: From 58 anti-HCV positive samples, 22 were positive to the NS5B fragment and successfully sequenced. Genotype 1b was the most prevalent in this population (50%), followed by 1a (27.2%), 2b (13.6%) and 3a (9.0%). Conclusions: This study is the first report of HCV genotypes from Rondonia State and subtype 1b was found to be the most prevalent. This subtype is mostly found among people who have a previous history of blood transfusion but more detailed studies with a larger number of patients are necessary to understand the HCV dynamics in the population of Rondonia State, Brazil.