Syttyminen ja palamisen eteneminen partikkelikerroksessa

Syttymistä ja palamisen etenemistä partikkelikerroksessa tutkitaan paloturvallisuuden parantamista sekä kiinteitä polttoaineita käyttävien polttolaitteiden toiminnan tuntemista ja kehittämistä varten. Tässä tutkimuksessa on tavoitteena kerätä yhteen syttymiseen ja liekkirintaman etenemiseen liittyviä kokeellisia ja teoreettisia tutkimustuloksia, jotka auttavat kiinteäkerrospoltto- ja -kaasutus-laitteiden kehittämisessä ja suunnittelussa. Työ on esitutkimus sitä seuraavalle kokeelliselle ja teoreettiselle osalle. Käsittelyssä keskitytään erityisesti puuperäisiin polttoaineisiin. Hiilidioksidipäästöjen vähentämistavoitteet sekä kiinteiden jätteiden energiakäytön lisääminen ja kaatopaikalle viennin vähentäminen aiheuttavat lähitulevaisuudessa kerrospolton lisääntymistä. Kuljetusmatkojen optimoinnin takia joudutaan rakentamaan melko pieniä polttolaitoksia, joissa kerrospolttotekniikka on edullisin vaihtoehto. Syttymispisteellä tarkoitetaan Semenovin määritelmän mukaan tilaa ja ajankohtaa, jolloin polttoaineen ja hapen reaktioissa muodostuva nettoenergia aikayksikössä on yhtäsuuri kuin ympäristöön siirtyvä nettoenergiavirta. Itsesyttyminen tarkoittaa syttymistä ympäristön lämpötilan tai paineen suurenemisen seurauksena. Pakotettu syttyminen tapahtuu, kun syttymispisteen läheisyydessä on esimerkiksi liekki tai hehkuva kiinteä kappale, joka aiheuttaa paikallisen syttymisen ja syttymisrintaman leviämisen muualle polttoaineeseen. Kokeellinen tutkimus on osoittanut tärkeimmiksi syttymiseen ja syttymisrintaman etenemiseen vaikuttaviksi tekijöiksi polttoaineen kosteuden, haihtuvien aineiden pitoisuuden ja lämpöarvon, partikkelikerroksen huokoisuuden, partikkelien koon ja muodon, polttoaineen pinnalle tulevan säteilylämpövirran tiheyden, kaasun virtausnopeuden kerroksessa, hapen osuuden ympäristössä sekä palamisilman esilämmityksen. Kosteuden lisääntyminen suurentaa syttymisenergiaa ja -lämpötilaa sekä pidentää syttymisaikaa. Mitä enemmän polttoaine sisältää haihtuvia aineita sitä pienemmässä lämpötilassa se syttyy. Syttyminen ja syttymisrintaman eteneminen ovat sitä nopeampia mitä suurempi on polttoaineen lämpöarvo. Kerroksen huokoisuuden kasvun on havaittu suurentavan palamisen etenemisnopeutta. Pienet partikkelit syttyvät yleensä nopeammin ja pienemmässä lämpötilassa kuin suuret. Syttymisrintaman eteneminen nopeutuu partikkelien pinta-ala - tilavuussuhteen kasvaessa. Säteilylämpövirran tiheys on useissa polttosovellutuksissa merkittävin lämmönsiirtotekijä, jonka kasvu luonnollisesti nopeuttaa syttymistä. Ilman ja palamiskaasujen virtausnopeus kerroksessa vaikuttaa konvektiiviseen lämmönsiirtoon ja hapen pitoisuuteen syttymisvyöhykkeellä. Ilmavirtaus voi jäähdyttää ja kuumankaasun virtaus lämmittää kerrosta. Hapen osuuden kasvaminen nopeuttaa syttymistä ja liekkirintaman etenemistä kunnes saavutetaan tila, jota suuremmilla virtauksilla ilma jäähdyttää ja laimentaa reaktiovyöhykettä. Palamisilman esilämmitys nopeuttaa syttymisrintaman etenemistä. Syttymistä ja liekkirintaman etenemistä kuvataan yleensä empiirisillä tai säilyvyysyhtälöihin perustuvilla malleilla. Empiiriset mallit perustuvat mittaustuloksista tehtyihin korrelaatioihin sekä joihinkin tunnettuihin fysikaalisiin lainalaisuuksiin. Säilyvyysyhtälöihin perustuvissa malleissa systeemille määritetään massan, energian, liikemäärän ja alkuaineiden säilymisyhtälöt, joiden nopeutta kuvaavien siirtoyhtälöiden muodostamiseen käytetään teoreettisella ja kokeellisella tutkimuksella saatuja yhtälöitä. Nämä mallinnusluokat ovat osittain päällekkäisiä. Pintojen syttymistä kuvataan usein säilyvyysyhtälöihin perustuvilla malleilla. Partikkelikerrosten mallinnuksessa tukeudutaan enimmäkseen empiirisiin yhtälöihin. Partikkelikerroksia kuvaavista malleista Xien ja Liangin hiilipartikkelikerroksen syttymiseen liittyvä tutkimus ja Gortin puun ja jätteen polttoon liittyvä reaktiorintaman etenemistutkimus ovat lähimpänä säilyvyysyhtälöihin perustuvaa mallintamista. Kaikissa malleissa joudutaan kuitenkin yksinkertaistamaan todellista tapausta esimerkiksi vähentämällä dimensioita, reaktioita ja yhdisteitä sekä eliminoimalla vähemmän merkittävät siirtomekanismit. Suoraan kerrospolttoa ja -kaasutusta palvelevia syttymisen ja palamisen etenemisen tutkimuksia on vähän. Muita tarkoituksia varten tehtyjen tutkimusten polttoaineet, kerrokset ja ympäristöolosuhteet poikkeavat yleensä selvästi polttolaitteiden vastaavista olosuhteista. Erikokoisten polttoainepartikkelien ja ominaisuuksiltaan erilaisten polttoaineiden seospolttoa ei ole tutkittu juuri ollenkaan. Polttoainepartikkelien muodon vaikutuksesta on vain vähän tutkimusta.Ilman kanavoitumisen vaikutuksista ei löytynyt tutkimuksia.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Transient current technique simulation of semiconductor detectors

Nowadays advanced simulation technologies of semiconductor devices occupies an important place in microelectronics production process. Simulation helps to understand devices internal processes physics, detect new effects and find directions for optimization. Computer calculation reduces manufacturing costs and time. Modern simulation suits such as Silcaco TCAD allow simulating not only individual semiconductor structures, but also these structures in the circuit. For that purpose TCAD include MixedMode tool. That tool can simulate circuits using compact circuit models including semiconductor structures with their physical models. In this work, MixedMode is used for simulating transient current technique setup, which include detector and supporting electrical circuit. This technique was developed by RD39 collaboration project for investigation radiation detectors radiation hard properties.

The Use of Radiostereometric Analysis in Fractures of the Distal Radius: From Phantom Models to Clinical Application

Radiostereometric analysis (RSA) is a highly accurate method for the measurement of in vivo micromotion of orthopaedic implants. Validation of the RSA method is a prerequisite for performing clinical RSA studies. Only a limited number of studies have utilised the RSA method in the evaluation of migration and inducible micromotion during fracture healing. Volar plate fixation of distal radial fractures has increased in popularity. There is still very little prospective randomised evidence supporting the use of these implants over other treatments. The aim of this study was to investigate the precision, accuracy, and feasibility of using RSA in the evaluation of healing in distal radius fractures treated with a volar fixed-angle plate. A physical phantom model was used to validate the RSA method for simple distal radius fractures. A computer simulation model was then used to validate the RSA method for more complex interfragmentary motion in intra-articular fractures. A separate pre-clinical investigation was performed in order to evaluate the possibility of using novel resorbable markers for RSA. Based on the validation studies, a prospective RSA cohort study of fifteen patients with plated AO type-C distal radius fractures with a 1-year follow-up was performed. RSA was shown to be highly accurate and precise in the measurement of fracture micromotion using both physical and computer simulated models of distal radius fractures. Resorbable RSA markers demonstrated potential for use in RSA. The RSA method was found to have a high clinical precision. The fractures underwent significant translational and rotational migration during the first two weeks after surgery, but not thereafter. Maximal grip caused significant translational and rotational interfragmentary micromotion. This inducible micromotion was detectable up to eighteen weeks, even after the achievement of radiographic union. The application of RSA in the measurement of fracture fragment migration and inducible interfragmentary micromotion in AO type-C distal radius fractures is feasible but technically demanding. RSA may be a unique tool in defining the progress of fracture union.

TURKU TAKING STEPS TOWARDS FUTURE - technology supports physical activity

Physical activity (PA) is an important field of healthcare research internationally and within Finland. As technology devices and services penetrate deeper levels within society, the need for studying the usefulness for PA turns vital. We started this research work by reviewing literature consisting of two hundred research journals, all of which have found technology to significantly improve an individual’s ability to get motivation and achieve officially recommended levels of physical activity, like the 10000 steps a day, being tracked with the help of pedometers. Physical activity recommendations require sustained encouragement, consistent performance in order to achieve the long term benefits. We surveyed within the city of Turku, how the motivation levels and thirty three other criterions encompassing technology awareness, adoption and usage attitudes are impacted. Our aim was to know the factors responsible for achieving consistent growth in activity levels within the individuals and focus groups, as well as to determine the causes of failures and for collecting user experience feedback. The survey results were quite interesting and contain impeccable information for this field. While the focus groups confirmed the theory established by past studies within our literature review, it also establishes our research propositions that ict tools and services have provided and can further add higher benefits and value to individuals in tracking and maintain their activity levels consistently for longer time durations. This thesis includes two new models which dictate technology and physical activity adoption patterns based on four easy to evaluate criterions, thereby helping the healthcare providers to recommend improvements and address issues with an easy rule based approach. This research work provides vital clues on technology based healthcare objectives and achievement of standard PA recommendations by people within Turku and nearby regions.

Flexible multibody approach in bone strain estimation during physical activity:

Bone strain plays a major role as the activation signal for the bone (re)modeling process, which is vital for keeping bones healthy. Maintaining high bone mineral density reduces the chances of fracture in the event of an accident. Numerous studies have shown that bones can be strengthened with physical exercise. Several hypotheses have asserted that a stronger osteogenic (bone producing) effect results from dynamic exercise than from static exercise. These previous studies are based on short-term empirical research, which provide the motivation for justifying the experimental results with a solid mathematical background. The computer simulation techniques utilized in this work allow for non-invasive bone strain estimation during physical activity at any bone site within the human skeleton. All models presented in the study are threedimensional and actuated by muscle models to replicate the real conditions accurately. The objective of this work is to determine and present loading-induced bone strain values resulting from physical activity. It includes a comparison of strain resulting from four different gym exercises (knee flexion, knee extension, leg press, and squat) and walking, with the results reported for walking and jogging obtained from in-vivo measurements described in the literature. The objective is realized primarily by carrying out flexible multibody dynamics computer simulations. The dissertation combines the knowledge of finite element analysis and multibody simulations with experimental data and information available from medical field literature. Measured subject-specific motion data was coupled with forward dynamics simulation to provide natural skeletal movement. Bone geometries were defined using a reverse engineering approach based on medical imaging techniques. Both computed tomography and magnetic resonance imaging were utilized to explore modeling differences. The predicted tibia bone strains during walking show good agreement with invivo studies found in the literature. Strain measurements were not available for gym exercises; therefore, the strain results could not be validated. However, the values seem reasonable when compared to available walking and running invivo strain measurements. The results can be used for exercise equipment design aimed at strengthening the bones as well as the muscles during workout. Clinical applications in post fracture recovery exercising programs could also be the target. In addition, the methodology introduced in this study, can be applied to investigate the effect of weightlessness on astronauts, who often suffer bone loss after long time spent in the outer space.

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.