The aim of this thesis is to optimize hydro power plants with data generated from observations and field tests at the plants. The output is optimal production tables and curves in order to operate and plan hydro power plants in an optimized way concerning power output, efficiency and distribution of water. The thesis is performed in collaboration with Vattenfall AB, which currently use an internal optimization program called SEVAP. Two alternative methods have been selected, employed and compared with the current optimization program, these are Interior-Point Method and Sequential Quadratic Programming. Three start-point strategies are created to increase the probability of finding a global optima. A heuristic rule is used for selection of strategy in order to prevent rapid changes in load distribution for small variations in dispatched water. The optimization is performed at three plants in Sweden with different size and setup. The results of this evaluation showed marginally better results for the employed methods in comparison to the currently used optimization. Further, the developed program is more flexible and compatible to integrate with future digitalization projects. / Syftet med detta examensarbete är att optimera vattenkraftverk med data som genererats från indextester vid kraftverken. Resultatet är optimala produktionstabeller och kurvor för drift och planering av vattenkraftverk. Dessa är baserade på att optimalt fördela vattnet mellan aggregaten för att maximera uteffekt och verkningsgrad. Detta arbete har utförts i samarbete med Vattenfall AB, som för närvarande använder ett internt optimeringsprogram som heter SEVAP. Två optimeringsmetoder har valts, implementerats och jämförts med det nuvarande optimeringsprogrammet. Dessa metoder är inrepunktsmetoden (IPM) och sekventiell kvadratiskt programmering (SQP). Tre startpunktsstrategier har används för att öka sannolikheten att hitta ett globalt optima. För att förhindra hastiga förändringar i lastfördelning för små variationer av avsänt vatten har en heuristisk regel används. Optimeringen har utförts på tre stationer med olika uppsättning och storlek. Resultatet av detta examensarbete visar marginellt bättre resultat för de använda metoderna i jämförelse med den nuvarande optimeringen. Det utvecklade programmet är flexibelt och kompatibelt att integrera med framtida digitaliseringsprojekt.

[pt] Nesta dissertação apresenta-se uma formulação para a análise numérica de cascas espessas axissimétricas, sob os carregamentos de pressão e força distribuida ao longo de um paralelo, utilizando-se a técnica de elementos finitos enriquecidos. As discretizações dos campos de deslocamento axial e radial são consideradas no domínio do elemento verificando-se as seguintes restrições: tensões nulas nas faces interna e externa da casca e uma combinação das soluções analíticas para cascas espessas cilíndricas e esféricas. A formulação resulta em um modelo com seis graus-de-liberdade generalizados por ponto nodal para elementos unidimensionais, considerando- se como referencia a superfície média da casca. Na imposição de condições de continuidade e de fixação associados aos graus-de-liberdade empregou-se o método de penalidades. A formulação foi implementada e alguns testes numéricos são apresentados para demonstrar sua aplicabilidade em comparações com outras soluções analíticas ou numéricas. / [en] This work presents an element formulation for the analysis of axisymmetric thick shells under pressure and line loads using Enriched Finite Element technique. Axial and radial displacement fields are considered in the formulation under the conditions of zero stresses at internal and external surfaces of the shell and, a combination of analitical solutions for radial displacements of cilindrical and spherical thick wall shells. The formulation results in a six generalized degree-of-freedom uni-dimensional model refered to the element nodal points at the shell mid-surface. Continuity between adjoining elements and clamped boundary conditions associated to the element degrees-of-freedom are imposed by the use of a penalty method. The formulation has been implemented and some numerical analysis results are shown to demonstrate its aplicability, as compared to other analytical or numerical solutions.

Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both a better accuracy and a better stability (regularity) under noise in the data. We aim to smooth the nonparametric part of GPLM by using a modified form of Multiple Adaptive Regression Spline (MARS) which is very useful for high-dimensional problems and does not impose any specific relationship between the predictor and dependent variables. Instead, it can estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine the dependent variable. The MARS algorithm has two steps: the forward and backward stepwise algorithms. In the rst one, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm starts with removing the least significant basis functions from the model. In this study, we propose to use a penalized residual sum of squares (PRSS) instead of the backward stepwise algorithm and construct PRSS for MARS as a Tikhonov regularization problem. Besides, we provide numeric example with two data sets / one has interaction and the other one does not have. As well as studying the regularization of the nonparametric part, we also mention theoretically the regularization of the parametric part. Furthermore, we make a comparison between Infinite Kernel Learning (IKL) and Tikhonov regularization by using two data sets, with the difference consisting in the (non-)homogeneity of the data set. The thesis concludes with an outlook on future research.

Neste trabalho, o escoamento incompressível ao redor de um cilindro fixo é estudado numericamente através do método de elementos finitos. Foram realizadas simulações bidimensionais no domínio do tempo, com números de Reynolds variando entre 100 e 600, utilizando-se, para tanto, malhas não-estruturadas com elementos triangulares. Pretende-se aqui analisar a eficácia da solução das equações de Navier-Stokes utilizando o método das penalidades, meio pelo qual o acoplamento pressão-velocidade foi tratado. Avalia-se a convergência da solução para diferentes valores do fator de penalidade e sugere-se um método para estimá-lo. Analisa-se, ainda, a sensibilidade da resposta à utilização da matriz de inércia nos formatos consistente e concentrada. Por fim, é realizada a comparação dos coeficientes de arrasto médio, flutuação do coeficiente de sustentação e número de Strouhal obtidos neste trabalho com resultados de outras publicações. / In this work, the incompressible flow around a stationary cylinder is investigated by using the Finite Element Method. Two-dimensional simulations in time domain have been carried out, with Reynolds number varying from 100 to 600, using non-structured meshes with triangular elements. The aim of this work is to analyze the efficiency of Penalty Methods, which is the way that the velocity-pressure coupling problem is treated here, in Navier-Stokes equations solution. The solution convergence from different values of penalty parameter is evaluated and it is suggested a method to estimate it. In addition, it is studied the sensibilty of response when using the mass matrix in consistent or lumped format. At last, a comparison between average drag coefficient, fluctuating lift and Strouhal number obtained here and those found in other publications is shown.

Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest. In other words, a special advantage of MARS lies in its ability to estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine the response variable. The function fitted by MARS is continuous, whereas the one fitted by classical classification methods (CART) is not. Herewith, MARS becomes an alternative to CART. The MARS algorithm for estimating the model function consists of two complementary algorithms: the forward and backward stepwise algorithms. In the first step, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm is began by removing the least significant basis functions from the model. In this study, we propose not to use the backward stepwise algorithm. Instead, we construct a penalized residual sum of squares (PRSS) for MARS as a Tikhonov regularization problem, which is also known as ridge regression. We treat this problem using continuous optimization techniques which we consider to become an important complementary technology and alternative to the concept of the backward stepwise algorithm. In particular, we apply the elegant framework of conic quadratic programming which is an area of convex optimization that is very well-structured, herewith, resembling linear programming and, hence, permitting the use of interior point methods. The boundaries of this optimization problem are determined by the multiobjective optimization approach which provides us many alternative solutions. Based on these theoretical and algorithmical studies, this MSc thesis work also contains applications on the data investigated in a T&Uuml / BiTAK project on quality control. By these applications, MARS and our new method are compared.

Coupled systems are ubiquitous in modern engineering and science. Such systems can encompass fluid dynamics, structural mechanics, chemical species transport and electrostatic effects among other components, all of which can be coupled in many different ways. In addition, such models are usually multiscale, making their numerical simulation challenging, and necessitating the use of adaptive modeling techniques. The multiscale, multiphysics models of electrosomotic flow (EOF) constitute a particularly challenging coupled flow system. A special feature of such models is that the coupling between the electric physics and hydrodynamics is via the boundary. Numerical simulations of coupled systems are typically targeted towards specific Quantities of Interest (QoIs). Adjoint-based approaches offer the possibility of QoI targeted adaptive mesh refinement and efficient parameter sensitivity analysis. The formulation of appropriate adjoint problems for EOF models is particularly challenging, due to the coupling of physics via the boundary as opposed to the interior of the domain. The well-posedness of the adjoint problem for such models is also non-trivial. One contribution of this dissertation is the derivation of an appropriate adjoint problem for slip EOF models, and the development of penalty-based, adjoint-consistent variational formulations of these models. We demonstrate the use of these formulations in the simulation of EOF flows in straight and T-shaped microchannels, in conjunction with goal-oriented mesh refinement and adjoint sensitivity analysis. Complex computational models may exhibit uncertain behavior due to various reasons, ranging from uncertainty in experimentally measured model parameters to imperfections in device geometry. The last decade has seen a growing interest in the field of Uncertainty Quantification (UQ), which seeks to determine the effect of input uncertainties on the system QoIs. Monte Carlo methods remain a popular computational approach for UQ due to their ease of use and "embarassingly parallel" nature. However, a major drawback of such methods is their slow convergence rate. The second contribution of this work is the introduction of a new Monte Carlo method which utilizes local sensitivity information to build accurate surrogate models. This new method, called the Local Sensitivity Derivative Enhanced Monte Carlo (LSDEMC) method can converge at a faster rate than plain Monte Carlo, especially for problems with a low to moderate number of uncertain parameters. Adjoint-based sensitivity analysis methods enable the computation of sensitivity derivatives at virtually no extra cost after the forward solve. Thus, the LSDEMC method, in conjuction with adjoint sensitivity derivative techniques can offer a robust and efficient alternative for UQ of complex systems. The efficiency of Monte Carlo methods can be further enhanced by using stratified sampling schemes such as Latin Hypercube Sampling (LHS). However, the non-incremental nature of LHS has been identified as one of the main obstacles in its application to certain classes of complex physical systems. Current incremental LHS strategies restrict the user to at least doubling the size of an existing LHS set to retain the convergence properties of LHS. The third contribution of this research is the development of a new Hierachical LHS algorithm, that creates designs which can be used to perform LHS studies in a more flexibly incremental setting, taking a step towards adaptive LHS methods. / text

La simulation d'écoulements dans des géométries complexes nécessite la création de maillages parfois difficile à réaliser. La méthode de pénalisation proposée dans ce travail permet de simplifier cette étape. En effet, la résolution des équations qui gouvernent l'écoulement se fait sur un maillage plus simple mais non-adapté à la géométrie du problème. Les conditions aux limites sur les parties du domaine physique immergées dans le maillage sont prises en compte à travers l'ajout d'un terme de pénalisation dans les équations. Nous nous sommes intéressés à l'approximation du terme de pénalisation pour une discrétisation par volumes finis sur maillages décalés et colocatifs. Les cas tests de vérification réalisés attestent d'un ordre de convergence spatial égal à 2 pour la méthode de pénalisation appliquée à la résolution d'une équation de type Poisson ou des équations de Navier-Stokes. Enfin, on présente les résultats obtenus pour la simulation d'écoulements turbulents autour d'un cylindre à Re=3900 et à l'intérieur d'une partie d'un assemblage combustible à Re=9500. / The simulations of fluid flows in complex geometries require the generation of body-fitted meshes which are difficult to create.The penalty method developed in this work is useful to simplify the mesh generation task.The governing equations of fluid flow are discretized using a finite volume method on an unfitted mesh.The immersed boundary conditions are taken into account through a penalty term added to the governing equations.We are interested in the approximation of the penalty term using a finite volume discretization with collocated and staggered grid.The penalty method is second-order spatial accurate for Poisson and Navier-Stokes equations.Finally, simulations of turbulent flows around a cylinder at Re=3900 and turbulent motions in a rod bundle at Re=9500 are performed.

Neste trabalho, o escoamento incompressível ao redor de um cilindro fixo é estudado numericamente através do método de elementos finitos. Foram realizadas simulações bidimensionais no domínio do tempo, com números de Reynolds variando entre 100 e 600, utilizando-se, para tanto, malhas não-estruturadas com elementos triangulares. Pretende-se aqui analisar a eficácia da solução das equações de Navier-Stokes utilizando o método das penalidades, meio pelo qual o acoplamento pressão-velocidade foi tratado. Avalia-se a convergência da solução para diferentes valores do fator de penalidade e sugere-se um método para estimá-lo. Analisa-se, ainda, a sensibilidade da resposta à utilização da matriz de inércia nos formatos consistente e concentrada. Por fim, é realizada a comparação dos coeficientes de arrasto médio, flutuação do coeficiente de sustentação e número de Strouhal obtidos neste trabalho com resultados de outras publicações. / In this work, the incompressible flow around a stationary cylinder is investigated by using the Finite Element Method. Two-dimensional simulations in time domain have been carried out, with Reynolds number varying from 100 to 600, using non-structured meshes with triangular elements. The aim of this work is to analyze the efficiency of Penalty Methods, which is the way that the velocity-pressure coupling problem is treated here, in Navier-Stokes equations solution. The solution convergence from different values of penalty parameter is evaluated and it is suggested a method to estimate it. In addition, it is studied the sensibilty of response when using the mass matrix in consistent or lumped format. At last, a comparison between average drag coefficient, fluctuating lift and Strouhal number obtained here and those found in other publications is shown.

Doctor Scientiae - DSc / Robust Spectral Methods for Solving Option Pricing Problems by Edson Pindza PhD thesis, Department of Mathematics and Applied Mathematics, Faculty of Natural Sciences, University of the Western Cape Ever since the invention of the classical Black-Scholes formula to price the financial derivatives, a number of mathematical models have been proposed by numerous researchers in this direction. Many of these models are in general very complex, thus closed form analytical solutions are rarely obtainable. In view of this, we present a class of efficient spectral methods to numerically solve several mathematical models of pricing options. We begin with solving European options. Then we move to solve their American counterparts which involve a free boundary and therefore normally difficult to price by other conventional numerical methods. We obtain very promising results for the above two types of options and therefore we extend this approach to solve some more difficult problems for pricing options, viz., jump-diffusion models and local volatility models. The numerical methods involve solving partial differential equations, partial integro-differential equations and associated complementary problems which are used to model the financial derivatives. In order to retain their exponential accuracy, we discuss the necessary modification of the spectral methods. Finally, we present several comparative numerical results showing the superiority of our spectral methods.

The purpose of this thesis is to develop an optimization model that chooses the optimal and price efficient combination of underlying assets for a equally weighted basket option. To obtain a price efficient combination of underlying assets a function that calculates the basket option price is needed, for further use in an optimization model. The closed-form basket option pricing is a great challenge, due to the lack of a distribution describing the augmented stochastic price process. Many types of approaches to price an basket option has been made. In this thesis, an analytical approximation of the basket option price has been used, where the analytical approximation aims to develop a method to describe the augmented price process. The approximation is done by moment matching, i.e. matching the first two moments of the real distribution of the basket option with an lognormal distribution. The obtained price function is adjusted and used as the objective function in the optimization model. Furthermore, since the goal is to obtain en equally weighted basket option, the appropriate class of optimization models to use are binary optimization problems. This kind of optimization model is in general hard to solve - especially for increasing dimensions. Three different continuous relaxations of the binary problem has been applied in order to obtain continuous problems, that are easier to solve. The results shows that the purpose of this thesis is fulfilled when formulating and solving the optimization problem - both as an binary and continuous nonlinear optimization model. Moreover, the results from a Monte Carlo simulation for correlated stochastic processes shows that the moment matching technique with a lognormal distribution is a good approximation for pricing a basket option. / Syftet med detta examensarbete är att utveckla ett optimeringsverktyg som väljer den optimala och priseffektiva kombinationen av underliggande tillgångar för en likaviktad aktiekorg. För att kunna hitta en priseffektiv kombination av underliggande tillgångar behöver man finna en passande funktion som bestämmer priset på en likaviktad aktiekorg. Prissättningen av dessa typer av optioner är en stor utmaning. Detta är på grund av bristen av en sannolikhetsfördelning som kan beskriva den utökade och korrelerade stokastiska prisprocess som uppstår för en aktiekorg. Många typer av prissättningar har undersökts och tillämpats. I detta arbete har en analytisk approximation använts för att kunna beskriva den underliggande pris processen approximativt. Uppskattningen görs genom att matcha de tvåförsta momenten av den verkliga fördelningen med motsvarande moment för en lognormal fördelning. Den erhållna prisfunktionen justeras och används som målfunktionen i optimeringsmodellen. Binära ickelinjära optimeringsproblem är i allmänhet svåra att lösa - särskilt för ökande dimensioner av variabler. Tre olika kontinuerliga omformuleringar av det binära optimeringsproblemet har gjorts för att erhålla kontinuerliga problem som är lättare att lösa. Resultaten visar att en optimal och priseffektiv kombination av underliggande aktier är möjlig att hitta genom att formulera ett optimeringsproblem - både som en binär och kontinuerlig ickelinjär optimeringsmodell. Dessutom visar resultaten från en Monte Carlo-simulering, i detta fall för korrelerade stokastiska processer, att moment matching metoden utförd med en lognormal fördelning är en god approximation för prissättningen av aktiekorgar.