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Large scale non-parametric applied shape optimization for computational fluid dynamics is considered. Treating a shape optimization problem as a standard optimal control problem by means of a parameterization, the Lagrangian usually requires knowledge of the partial derivative of the shape parameterization and deformation chain with respect to input parameters. For a variety of reasons, this mesh sensitivity Jacobian is usually quite problematic. For a sufficiently smooth boundary, the Hadamard theorem provides a gradient expression that exists on the surface alone, completely bypassing the mesh sensitivity Jacobian. Building upon this, the gradient computation becomes independent of the number of design parameters and all surface mesh nodes are used as design unknown in this work, effectively allowing a free morphing of shapes during optimization. Contrary to a parameterized shape optimization problem, where a smooth surface is usually created independently of the input parameters by construction, regularity is not preserved automatically in the non-parametric case. As part of this work, the shape Hessian is used in an approximative Newton method, also known as Sobolev method or gradient smoothing, to ensure a certain regularity of the updates, and thus a smooth shape is preserved while at the same time the one-shot optimization method is also accelerated considerably. For PDE constrained shape optimization, the Hessian usually is a pseudo-differential operator. Fourier analysis is used to identify the operator symbol both analytically and discretely. Preconditioning the one-shot optimization by an appropriate Hessian symbol is shown to greatly accelerate the optimization. As the correct discretization of the Hadamard form usually requires evaluating certain surface quantities such as tangential divergence and curvature, special attention is also given to discrete differential geometry on triangulated surfaces for evaluating shape gradients and Hessians. The Hadamard formula and Hessian approximations are applied to a variety of flow situations. In addition to shape optimization of internal and external flows, major focus lies on aerodynamic design such as optimizing two dimensional airfoils and three dimensional wings. Shock waves form when the local speed of sound is reached, and the gradient must be evaluated correctly at discontinuous states. To ensure proper shock resolution, an adaptive multi-level optimization of the Onera M6 wing is conducted using more than 36, 000 shape unknowns on a standard office workstation, demonstrating the applicability of the shape-one-shot method to industry size problems.
This work is concerned with arbitrage bounds for prices of contingent claims under transaction costs, but regardless of other conceivable market frictions. Assumptions on the underlying market are held as weak as convenient for the deduction of meaningful results that make good economic sense. In discrete time we also allow for underlying price processes with uncountable state space. In continuous time the underlying price process is modeled by a semimartingale. For the most part we could avoid any stronger assumptions. The main problems with which we deal in this work are the modelling of (proportional) transaction costs, Fundamental Theorems of Asset Pricing under transaction costs, dual characterizations of arbitrage bounds under transaction costs, Quantile-Hedging under transaction costs, alternatives to the Black-Scholes model in continuous time (under transaction costs). The results apply to stock and currency markets.
Recently, optimization has become an integral part of the aerodynamic design process chain. However, because of uncertainties with respect to the flight conditions and geometrical uncertainties, a design optimized by a traditional design optimization method seeking only optimality may not achieve its expected performance. Robust optimization deals with optimal designs, which are robust with respect to small (or even large) perturbations of the optimization setpoint conditions. The resulting optimization tasks become much more complex than the usual single setpoint case, so that efficient and fast algorithms need to be developed in order to identify, quantize and include the uncertainties in the overall optimization procedure. In this thesis, a novel approach towards stochastic distributed aleatory uncertainties for the specific application of optimal aerodynamic design under uncertainties is presented. In order to include the uncertainties in the optimization, robust formulations of the general aerodynamic design optimization problem based on probabilistic models of the uncertainties are discussed. Three classes of formulations, the worst-case, the chance-constrained and the semi-infinite formulation, of the aerodynamic shape optimization problem are identified. Since the worst-case formulation may lead to overly conservative designs, the focus of this thesis is on the chance-constrained and semi-infinite formulation. A key issue is then to propagate the input uncertainties through the systems to obtain statistics of quantities of interest, which are used as a measure of robustness in both robust counterparts of the deterministic optimization problem. Due to the highly nonlinear underlying design problem, uncertainty quantification methods are used in order to approximate and consequently simplify the problem to a solvable optimization task. Computationally demanding evaluations of high dimensional integrals resulting from the direct approximation of statistics as well as from uncertainty quantification approximations arise. To overcome the curse of dimensionality, sparse grid methods in combination with adaptive refinement strategies are applied. The reduction of the number of discretization points is an important issue in the context of robust design, since the computational effort of the numerical quadrature comes up in every iteration of the optimization algorithm. In order to efficiently solve the resulting optimization problems, algorithmic approaches based on multiple-setpoint ideas in combination with one-shot methods are presented. A parallelization approach is provided to overcome the amount of additional computational effort involved by multiple-setpoint optimization problems. Finally, the developed methods are applied to 2D and 3D Euler and Navier-Stokes test cases verifying their industrial usability and reliability. Numerical results of robust aerodynamic shape optimization under uncertain flight conditions as well as geometrical uncertainties are presented. Further, uncertainty quantification methods are used to investigate the influence of geometrical uncertainties on quantities of interest in a 3D test case. The results demonstrate the significant effect of uncertainties in the context of aerodynamic design and thus the need for robust design to ensure a good performance in real life conditions. The thesis proposes a general framework for robust aerodynamic design attacking the additional computational complexity of the treatment of uncertainties, thus making robust design in this sense possible.
Industrial companies mainly aim for increasing their profit. That is why they intend to reduce production costs without sacrificing the quality. Furthermore, in the context of the 2020 energy targets, energy efficiency plays a crucial role. Mathematical modeling, simulation and optimization tools can contribute to the achievement of these industrial and environmental goals. For the process of white wine fermentation, there exists a huge potential for saving energy. In this thesis mathematical modeling, simulation and optimization tools are customized to the needs of this biochemical process and applied to it. Two different models are derived that represent the process as it can be observed in real experiments. One model takes the growth, division and death behavior of the single yeast cell into account. This is modeled by a partial integro-differential equation and additional multiple ordinary integro-differential equations showing the development of the other substrates involved. The other model, described by ordinary differential equations, represents the growth and death behavior of the yeast concentration and development of the other substrates involved. The more detailed model is investigated analytically and numerically. Thereby existence and uniqueness of solutions are studied and the process is simulated. These investigations initiate a discussion regarding the value of the additional benefit of this model compared to the simpler one. For optimization, the process is described by the less detailed model. The process is identified by a parameter and state estimation problem. The energy and quality targets are formulated in the objective function of an optimal control or model predictive control problem controlling the fermentation temperature. This means that cooling during the process of wine fermentation is controlled. Parameter and state estimation with nonlinear economic model predictive control is applied in two experiments. For the first experiment, the optimization problems are solved by multiple shooting with a backward differentiation formula method for the discretization of the problem and a sequential quadratic programming method with a line search strategy and a Broyden-Fletcher-Goldfarb-Shanno update for the solution of the constrained nonlinear optimization problems. Different rounding strategies are applied to the resulting post-fermentation control profile. Furthermore, a quality assurance test is performed. The outcomes of this experiment are remarkable energy savings and tasty wine. For the next experiment, some modifications are made, and the optimization problems are solved by using direct transcription via orthogonal collocation on finite elements for the discretization and an interior-point filter line-search method for the solution of the constrained nonlinear optimization problems. The second experiment verifies the results of the first experiment. This means that by the use of this novel control strategy energy conservation is ensured and production costs are reduced. From now on tasty white wine can be produced at a lower price and with a clearer conscience at the same time.
Das Konzept der proximalen Mehrschritt-Regularisierung (MSR) auf Folgen von Gittern bei der Lösung inkorrekter Variationsungleichungen wurde von Kaplan und Tichatschke im Jahre 1997 in ihrer Arbeit "Prox-regularization and solution of illposed elliptic variational inequalities" vorgeschlagen und theoretisch motiviert. In demselben Artikel betrachtet man ein allgemeines Problem der partiellen Regularisierung auf einem abgeschlossenen Unterraum. Als Gegenstand der Anwendung solcher Regularisierung können die schlecht gestellten Optimalsteuerprobleme heraustreten, wobei der Unterraum in dem ganzen Prozessraum durch Steuervariablen gebildet wird. Im ersten Kapitel der vorliegenden Dissertation betrachten wir ein abstraktes linear-quadratisches Kontrollproblem in allgemeinen Hilberträumen. Wir diskutieren Voraussetzungen und Bedingungen, unter denen das Kontrollproblem inkorrekt wird. Danach werden zwei allgemeine numerische Verfahren der partiellen Mehrschritt-Regularisierung formuliert. Im ersten Fall untersucht man das MSR-Verfahren, in dem die Zustandsgleichung in einen quadratischen Strafterm eingebettet wird, gemäß der entsprechenden Publikationen von Kaplan und Tichatschke. Im zweiten Fall werden die Ersatzprobleme des MSR-Verfahrens mit exakt erfüllter Zustandsgleichung entwickelt. Im Mittelpunkt sämtlicher Forschungen steht die Konvergenz der approximativen Lösungen von Ersatzproblemen des MSR-Verfahrens gegen ein Element aus der Optimalmenge des Ausgangsproblems. Es stellt sich die Frage: in welchem der genannten Fälle können schwächeren Konvergenzbedingungen für die inneren Approximationen angegeben werden? Um diese Frage aufzuklären, untersuchen wir zwei inkorrekten Kontrollproblme mit elliptischen Zustandsgleichungen und verteilter Steuerung. Das erste Problem kann auf das bekannte Fuller-Problem zurückgeführt werden, für welches eine analytische Lösung mit sogenanntem "chattering regime" existiert und welches ein Basisbeispiel für unsere Aufgaben liefert. Zur Lösung des Fuller-Problems formulieren wir einen MSR-Algorithmus, in dem man mit Fehlern des Strafverfahrens und der FEM-Approximationen rechnen muß. Als Hauptergebnis erhalten wir ein Konvergenzkriterium, das das asymptotische Verhalten von Regularisierungs-, Diskretisierungs- und Strafparametern des MSR-Algorithmus bestimmt. Im letzten Kapitel formulieren wir ein anderes schlecht gestelltes Optimalsteuerproblem mit verteilter Steuerung über dem Polygongebiet. Die Zustandsgleichung wird nun durch ein Poisson-Problem mit gemischten Randbedingungen erzeugt. Solche Aufgabenstellung liefert eine natürliche Erweiterung des auf einer gewöhnlichen Differentialgeichung beruhenden Fuller-Problems auf die Kontrollprobleme mit partiellen Differentialgleichungen. Wir formulieren neuerlich das MSR-Verfahren, in dem man neben dem Diskretisierungsfehler auch einen Berechnungsfehler berücksichtigt. Diesmal verzichten wir aber auf die Straftechniken und stellen die Ersatzprobleme mit exakt erfüllter Zustandsgleichung zusammen. Mit diesem alternativen Zugang und anhand der Falkschen Beweistechniken erhalten wir ein schwächeres und somit auch besseres Konvergenzkriterium für das MSR-Verfahren. Zum Abschluß präsentieren wir Ergebnisse der numerischen Tests, durchgeführt mit dem MSR-Verfahren für ein konkretes Optimalsteuerproblem, dessen Lösung ein zweidimensionales chattering regime aufweist.
In this thesis, global surrogate models for responses of expensive simulations are investigated. Computational fluid dynamics (CFD) have become an indispensable tool in the aircraft industry. But simulations of realistic aircraft configurations remain challenging and computationally expensive despite the sustained advances in computing power. With the demand for numerous simulations to describe the behavior of an output quantity over a design space, the need for surrogate models arises. They are easy to evaluate and approximate quantities of interest of a computer code. Only a few number of evaluations of the simulation are stored for determining the behavior of the response over a whole range of the input parameter domain. The Kriging method is capable of interpolating highly nonlinear, deterministic functions based on scattered datasets. Using correlation functions, distinct sensitivities of the response with respect to the input parameters can be considered automatically. Kriging can be extended to incorporate not only evaluations of the simulation, but also gradient information, which is called gradient-enhanced Kriging. Adaptive sampling strategies can generate more efficient surrogate models. Contrary to traditional one-stage approaches, the surrogate model is built step-by-step. In every stage of an adaptive process, the current surrogate is assessed in order to determine new sample locations, where the response is evaluated and the new samples are added to the existing set of samples. In this way, the sampling strategy learns about the behavior of the response and a problem-specific design is generated. Critical regions of the input parameter space are identified automatically and sampled more densely for reproducing the response's behavior correctly. The number of required expensive simulations is decreased considerably. All these approaches treat the response itself more or less as an unknown output of a black-box. A new approach is motivated by the assumption that for a predefined problem class, the behavior of the response is not arbitrary, but rather related to other instances of the mutual problem class. In CFD, for example, responses of aerodynamic coefficients share structural similarities for different airfoil geometries. The goal is to identify the similarities in a database of responses via principal component analysis and to use them for a generic surrogate model. Characteristic structures of the problem class can be used for increasing the approximation quality in new test cases. Traditional approaches still require a large number of response evaluations, in order to achieve a globally high approximation quality. Validating the generic surrogate model for industrial relevant test cases shows that they generate efficient surrogates, which are more accurate than common interpolations. Thus practical, i.e. affordable surrogates are possible already for moderate sample sizes. So far, interpolation problems were regarded as separate problems. The new approach uses the structural similarities of a mutual problem class innovatively for surrogate modeling. Concepts from response surface methods, variable-fidelity modeling, design of experiments, image registration and statistical shape analysis are connected in an interdisciplinary way. Generic surrogate modeling is not restricted to aerodynamic simulation. It can be applied, whenever expensive simulations can be assigned to a larger problem class, in which structural similarities are expected.
In a paper of 1996 the british mathematician Graham R. Allan posed the question, whether the product of two stable elements is again stable. Here stability describes the solvability of a certain infinite system of equations. Using a method from the theory of homological algebra, it is proved that in the case of topological algebras with multiplicative webs, and thus in all common locally convex topological algebras that occur in standard analysis, the answer of Allan's question is affirmative.
Es wird die Existenz einer Potenzreihe vom Konvergenzradius 1 bewiesen, so dass die mit einer zweifach unendlichen Matrix A (deren komplexe Einträge drei Bedingungen erfüllen müssen) gebildeten A -Transformierten außerhalb des (einfach zusammenhängenden) Holomorphiegebietes der Potenzreihe überkonvergieren. Das Hauptergebnis der Arbeit ist ein Satz über die Existenz einer universellen Potenzreihe vom Konvergenzradius 1, so dass deren A "Transformierte stetige Funktionen auf kompakten, holomorphe Funktionen auf offenen Mengen (in beiden Fällen liegen die Mengen im Komplement des einfach zusammenhängenden Holomorphiegebietes der Potenzreihe) approximieren und sich zusätzlich zur fast-überall-Approximation messbarer Funktionen auf messbaren Mengen (im Komplement des Holomorphiegebietes der Potenzreihe gelegen) eignen. Als wichtige Konsequenz dieses Hauptergebnisses ergibt sich für den Fall, dass das Holomorphiegebietes der Potenzreihe der Einheitskreis ist, die Existenz einer universellen trigonometrischen Reihe, so dass deren A "Transformierte auf dem Rand des Einheitskreises stetige Funktionen approximieren und zusätzlich messbare Funktionen fast-überall auf [0,2π] approximieren
Considering the numerical simulation of mathematical models it is necessary to have efficient methods for computing special functions. We will focus our considerations in particular on the classes of Mittag-Leffler and confluent hypergeometric functions. The PhD Thesis can be structured in three parts. In the first part, entire functions are considered. If we look at the partial sums of the Taylor series with respect to the origin we find that they typically only provide a reasonable approximation of the function in a small neighborhood of the origin. The main disadvantages of these partial sums are the cancellation errors which occur when computing in fixed precision arithmetic outside this neighborhood. Therefore, our aim is to quantify and then to reduce this cancellation effect. In the next part we consider the Mittag-Leffler and the confluent hypergeometric functions in detail. Using the method we developed in the first part, we can reduce the cancellation problems by "modifying" the functions for several parts of the complex plane. Finally, in in the last part two other approaches to compute Mittag-Leffler type and confluent hypergeometric functions are discussed. If we want to evaluate such functions on unbounded intervals or sectors in the complex plane, we have to consider methods like asymptotic expansions or continued fractions for large arguments z in modulus.
