Welding time models for cost calculations in the early stages of the design process

It is generally accepted that between 70 and 80% of manufacturing costs can be attributed to design. Nevertheless, it is difficult for the designer to estimate manufacturing costs accurately, especially when alternative constructions are compared at the conceptual design phase, because of the lack of cost information and appropriate tools. In general, previous reports concerning optimisation of a welded structure have used the mass of the product as the basis for the cost comparison. However, it can easily be shown using a simple example that the use of product mass as the sole manufacturing cost estimator is unsatisfactory. This study describes a method of formulating welding time models for cost calculation, and presents the results of the models for particular sections, based on typical costs in Finland. This was achieved by collecting information concerning welded products from different companies. The data included 71 different welded assemblies taken from the mechanical engineering and construction industries. The welded assemblies contained in total 1 589 welded parts, 4 257 separate welds, and a total welded length of 3 188 metres. The data were modelled for statistical calculations, and models of welding time were derived by using linear regression analysis. Themodels were tested by using appropriate statistical methods, and were found to be accurate. General welding time models have been developed, valid for welding in Finland, as well as specific, more accurate models for particular companies. The models are presented in such a form that they can be used easily by a designer, enabling the cost calculation to be automated.

Wavelet Analysis in Turbulence

The objective of this thesis is to study wavelets and their role in turbulence applications. Under scrutiny in the thesis is the intermittency in turbulence models. Wavelets are used as a mathematical tool to study the intermittent activities that turbulence models produce. The first section generally introduces wavelets and wavelet transforms as a mathematical tool. Moreover, the basic properties of turbulence are discussed and classical methods for modeling turbulent flows are explained. Wavelets are implemented to model the turbulence as well as to analyze turbulent signals. The model studied here is the GOY (Gledzer 1973, Ohkitani & Yamada 1989) shell model of turbulence, which is a popular model for explaining intermittency based on the cascade of kinetic energy. The goal is to introduce better quantification method for intermittency obtained in a shell model. Wavelets are localized in both space (time) and scale, therefore, they are suitable candidates for the study of singular bursts, that interrupt the calm periods of an energy flow through various scales. The study concerns two questions, namely the frequency of the occurrence as well as the intensity of the singular bursts at various Reynolds numbers. The results gave an insight that singularities become more local as Reynolds number increases. The singularities become more local also when the shell number is increased at certain Reynolds number. The study revealed that the singular bursts are more frequent at Re ~ 107 than other cases with lower Re. The intermittency of bursts for the cases with Re ~ 106 and Re ~ 105 was similar, but for the case with Re ~ 104 bursts occured after long waiting time in a different fashion so that it could not be scaled with higher Re.

Lärande som text : en dramapedagogiskt förankrad läsning av det kroppsliga, rumsliga och retoriska i kunskapsbildande processer

My presupposition, that learning at some level deals with life praxis, is expressed in four metaphors: space, time, fable and figure. Relations between learning,knowledge building and meaning making are linked to the concept of personal knowledge. I present a two part study of learning as text in a drama pedagogical rooted reading where learning is framed as the ongoing event, and knowledge, as the product of previous processes, is framed as culturally formed utterances. A frame analysis model is constructed as a topological guide for relations between the two concepts learning and knowledge. It visualises an aesthetic understanding, rooted in drama pedagogical comprehension. Insight and perception are linked in an inner relationship that is neither external nor identical. This understanding expresses the movement "in between" connecting asymmetrical and nonlinear features of human endeavour and societal issues. The performability of bodily and oral participation in the learning event in a socio-cultural setting is analysed as a dialogised text. In an ethnographical case study I have gathered material with an interest for the particular. The empirical material is based on three problem based learning situations in a Polytechnic setting. The act of transformation in the polyphony of the event is considered as a turning point in the narrative employment. Negotiation and figuration in the situation form patterns of the space for improvisation (flow) and tensions at the boundaries (thresholds) which imply the logical structure of transformation. Learning as a dialogised text of "yes" and "no", of structure and play for the improvised, interrelate in that movement. It is related to both the syntagmic and the paradigmatic forms of thinking. In the philosophical study, forms of understanding are linked to the logical structure of transformation as a cultural issue. The classical rhetorical concepts of Logos, Pathos, Ethos and Mythos are connected to the multidimensional rationality of the human being. In the Aristotelian form of knowledge, phronesis,a logic structure of inquiry is recognised. The shifting of perspectives between approaches, the construction of knowledge as context and the human project of meaning making as a subtext, illuminates multiple layers of the learning text. In an argumentation that post-modern apprehension of knowledge, emphasising contextual and situational values, has an empowering impact on learning, I find pedagogical benefits. The dialogical perspective has opened lenses that manage to hold in aesthetic doubling the individual action of inquiry and the stage with its cultural tools in a three dimensional reading.

Induction practices : experiences of newly qualified teachers

The thesis consists of four studies (articles I–IV) and a comprehensive summary. The aim is to deepen understanding and knowledge of newly qualified teachers’ experiences of their induction practices. The research interest thus reflects the ambition to strengthen the research-based platform for support measures. The aim can be specified in the following four sub-areas: to scrutinise NQTs’ experiences of the profession in the transition from education to work (study I), to describe and analyse NQTs’ experiences of their first encounters with school and classroom (study II), to explore NQTs’ experiences of their relationships within the school community (study III), to view NQTs’ experiences of support through peer-group mentoring as part of the wider aim of collaboration and assessment (study IV). The overall theoretical perspective constitutes teachers’ professional development. Induction forms an essential part of this continuum and can primarily be seen as a socialisation process into the profession and the social working environment of schools, as a unique phase of teachers’ development contributing to certain experiences, and as a formal programme designed to support new teachers. These lines of research are initiated in the separate studies (I–IV) and deepened in the theoretical part of the comprehensive summary. In order to appropriately understand induction as a specific practice the lines of research are in the end united and discussed with help of practice theory. More precisely the theory of practice architectures, including semantic space, physical space-time and social space, are used. The methodological approach to integrating the four studies is above all represented by abduction and meta-synthesis. Data has been collected through a questionnaire survey, with mainly open-ended questions, and altogether ten focus group meetings with newly qualified primary school teachers in 2007–2008. The teachers (n=88 in questionnaire, n=17 in focus groups), had between one and three years of teaching experience. Qualitative content analysis and narrative analysis were used when analysing the data. What is then the collected picture of induction or the first years in the profession if scrutinising the results presented in the articles? Four dimensions seem especially to permeate the studies and emerge when they are put together. The first dimension, the relational ˗ emotional, captures the social nature of induction and teacher’s work and the emotional character intimately intertwined. The second dimension, the tensional ˗ mutable, illustrates the intense pace of induction, together with the diffuse and unclear character of a teacher’s job. The third dimension, the instructive ˗ developmental, depicts induction as a unique and intensive phase of learning, maturity and professional development. Finally, the fourth dimension, the reciprocal ˗ professional, stresses the importance of reciprocity and collaboration in induction, both formally and informally. The outlined four dimensions, or integration of results, describing induction from the experiences of new teachers, constitute part of a new synthesis, induction practice. This synthesis was generated from viewing the integrated results through the theoretical lens of practice architecture and the three spaces, semantic space, physical space-time and social space. In this way, a more comprehensive, refined and partially new architecture of teachers’ induction practices are presented and discussed.

Simulation of structural stress history based on dynamic analysis

Modern machine structures are often fabricated by welding. From a fatigue point of view, the structural details and especially, the welded details are the most prone to fatigue damage and failure. Design against fatigue requires information on the fatigue resistance of a structure’s critical details and the stress loads that act on each detail. Even though, dynamic simulation of flexible bodies is already current method for analyzing structures, obtaining the stress history of a structural detail during dynamic simulation is a challenging task; especially when the detail has a complex geometry. In particular, analyzing the stress history of every structural detail within a single finite element model can be overwhelming since the amount of nodal degrees of freedom needed in the model may require an impractical amount of computational effort. The purpose of computer simulation is to reduce amount of prototypes and speed up the product development process. Also, to take operator influence into account, real time models, i.e. simplified and computationally efficient models are required. This in turn, requires stress computation to be efficient if it will be performed during dynamic simulation. The research looks back at the theoretical background of multibody dynamic simulation and finite element method to find suitable parts to form a new approach for efficient stress calculation. This study proposes that, the problem of stress calculation during dynamic simulation can be greatly simplified by using a combination of floating frame of reference formulation with modal superposition and a sub-modeling approach. In practice, the proposed approach can be used to efficiently generate the relevant fatigue assessment stress history for a structural detail during or after dynamic simulation. In this work numerical examples are presented to demonstrate the proposed approach in practice. The results show that approach is applicable and can be used as proposed.

Dynamic ecosystems under anthropogenic stress : how does macrotidal sandy fauna respond to green tides?

Highly dynamic systems, often considered as resilient systems, are characterised by abiotic and biotic processes under continuous and strong changes in space and time. Because of this variability, the detection of overlapping anthropogenic stress is challenging. Coastal areas harbour dynamic ecosystems in the form of open sandy beaches, which cover the vast majority of the world’s ice-free coastline. These ecosystems are currently threatened by increasing human-induced pressure, among which mass-development of opportunistic macroalgae (mainly composed of Chlorophyta, so called green tides), resulting from the eutrophication of coastal waters. The ecological impact of opportunistic macroalgal blooms (green tides, and blooms formed by other opportunistic taxa), has long been evaluated within sheltered and non-tidal ecosystems. Little is known, however, on how more dynamic ecosystems, such as open macrotidal sandy beaches, respond to such stress. This thesis assesses the effects of anthropogenic stress on the structure and the functioning of highly dynamic ecosystems using sandy beaches impacted by green tides as a study case. The thesis is based on four field studies, which analyse natural sandy sediment benthic community dynamics over several temporal (from month to multi-year) and spatial (from local to regional) scales. In this thesis, I report long-lasting responses of sandy beach benthic invertebrate communities to green tides, across thousands of kilometres and over seven years; and highlight more pronounced responses of zoobenthos living in exposed sandy beaches compared to semi-exposed sands. Within exposed sandy sediments, and across a vertical scale (from inshore to nearshore sandy habitats), I also demonstrate that the effects of the presence of algal mats on intertidal benthic invertebrate communities is more pronounced than that on subtidal benthic invertebrate assemblages, but also than on flatfish communities. Focussing on small-scale variations in the most affected faunal group (i.e. benthic invertebrates living at low shore), this thesis reveals a decrease in overall beta-diversity along a eutrophication-gradient manifested in the form of green tides, as well as the increasing importance of biological variables in explaining ecological variability of sandy beach macrobenthic assemblages along the same gradient. To illustrate the processes associated with the structural shifts observed where green tides occurred, I investigated the effects of high biomasses of opportunistic macroalgae (Ulva spp.) on the trophic structure and functioning of sandy beaches. This work reveals a progressive simplification of sandy beach food web structure and a modification of energy pathways over time, through direct and indirect effects of Ulva mats on several trophic levels. Through this thesis I demonstrate that highly dynamic systems respond differently (e.g. shift in δ13C, not in δ15N) and more subtly (e.g. no mass-mortality in benthos was found) to anthropogenic stress compared to what has been previously shown within more sheltered and non-tidal systems. Obtaining these results would not have been possible without the approach used through this work; I thus present a framework coupling field investigations with analytical approaches to describe shifts in highly variable ecosystems under human-induced stress.

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.

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.

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.

Using time and stochastic models for software reliability forecasting and analysis

Tämä työ luo katsauksen ajallisiin ja stokastisiin ohjelmien luotettavuus malleihin sekä tutkii muutamia malleja käytännössä. Työn teoriaosuus sisältää ohjelmien luotettavuuden kuvauksessa ja arvioinnissa käytetyt keskeiset määritelmät ja metriikan sekä varsinaiset mallien kuvaukset. Työssä esitellään kaksi ohjelmien luotettavuusryhmää. Ensimmäinen ryhmä ovat riskiin perustuvat mallit. Toinen ryhmä käsittää virheiden ”kylvöön” ja merkitsevyyteen perustuvat mallit. Työn empiirinen osa sisältää kokeiden kuvaukset ja tulokset. Kokeet suoritettiin käyttämällä kolmea ensimmäiseen ryhmään kuuluvaa mallia: Jelinski-Moranda mallia, ensimmäistä geometrista mallia sekä yksinkertaista eksponenttimallia. Kokeiden tarkoituksena oli tutkia, kuinka syötetyn datan distribuutio vaikuttaa mallien toimivuuteen sekä kuinka herkkiä mallit ovat syötetyn datan määrän muutoksille. Jelinski-Moranda malli osoittautui herkimmäksi distribuutiolle konvergaatio-ongelmien vuoksi, ensimmäinen geometrinen malli herkimmäksi datan määrän muutoksille.

Mechanistic population Models in Biology: Model Derivation and Application

In general, models of ecological systems can be broadly categorized as ’top-down’ or ’bottom-up’ models, based on the hierarchical level that the model processes are formulated on. The structure of a top-down, also known as phenomenological, population model can be interpreted in terms of population characteristics, but it typically lacks an interpretation on a more basic level. In contrast, bottom-up, also known as mechanistic, population models are derived from assumptions and processes on a more basic level, which allows interpretation of the model parameters in terms of individual behavior. Both approaches, phenomenological and mechanistic modelling, can have their advantages and disadvantages in different situations. However, mechanistically derived models might be better at capturing the properties of the system at hand, and thus give more accurate predictions. In particular, when models are used for evolutionary studies, mechanistic models are more appropriate, since natural selection takes place on the individual level, and in mechanistic models the direct connection between model parameters and individual properties has already been established. The purpose of this thesis is twofold. Firstly, a systematical way to derive mechanistic discrete-time population models is presented. The derivation is based on combining explicitly modelled, continuous processes on the individual level within a reproductive period with a discrete-time maturation process between reproductive periods. Secondly, as an example of how evolutionary studies can be carried out in mechanistic models, the evolution of the timing of reproduction is investigated. Thus, these two lines of research, derivation of mechanistic population models and evolutionary studies, are complementary to each other.

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.